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This paper aims to solve the technical problems such as poor drillability， large formation inclination， difficult well deflection control， and easy wall instability in the middle and deep Carboniferous and Permian hard formation in the Hashan area of Shengli western exploration area and realize the efficient exploration and benefit development of oil and gas reserves. Based on the current conventional drilling technology， the applicability and process optimization of segmented combined drilling technology were studied， and supporting technologies for speed acceleration and safety construction related to gas drilling were proposed for the Carboniferous and Permian hard formation of dolomitic rock and igneous rock， forming the regional construction standards. After the transformation from gas drilling to drilling fluid construction， the combined drilling technology of “gas drilling + ‘impregnated bit + turbine/high-speed screw’” was preferred. According to the formation characteristics， efficient impregnated bit series of different sizes were developed to achieve well deflection control and improve the rate of penetration. The combined drilling technology was successfully applied in Hashan3 and Hashan2 wells and achieved good results. During gas drilling， the rate of penetration in Well Hashan3 reached 5.2 m/h， more than three times faster than the conventional drilling method. During drilling fluid construction， the technology of “impregnated bit + turbine/high-speed screw” was adopted， and the formation inclination was 30°. The well deflection was well controlled， and the rate of penetration was close to 1 m/h， which showed a good regional application effect compared with conventional drilling technology. The field application shows that the combined drilling technology of “gas drilling + ‘impregnated bit + turbine/high-speed screw’” is an effective means to increase the speed and efficiency of middle and deep drilling in the Hashan area of Shengli west exploration area. However， the technology also has bottlenecks. Combined with the applicability research of particle impact drilling （PID） technology， it is suggested that the PID technology should be introduced and improved in the next step， so as to achieve speed and efficiency breakthrough in regional drilling.

In order to promote the strategic planning for the oil import security of China, relevant influencing factors were studied by integrating document coding with the Decision Making Trial and Evaluation Laboratory-Interpretative Structural Modeling(DEMATELISM). The current situation and challenges of oil import of China were introduced based on the 4A definition of energy security in terms of supply, transportation and price. Meanwhile, the analytical framework of oil import security was established and coupled with document coding method to define the factors that influence the oil import security of China, thus forming the category of code statistics at different levels. Besides, the interrelation and hierarchical structure of factors were analyzed by DEMATEL-ISM, and suggestions were put forward on the policies. The results show that oil import security is influenced directly by superficial factors, including the production and transportation capacity of oil, and fundamentally by underlying factors, such as the stage of economic development and the structure of energy consumption. In addition, the shallow and deep factors, including the oil policy agreement, the import channel pattern, the import source structure and the construction of emergency system, are the critical points of oil import security. Therefore, special attention should be paid to the above-mentioned factors. Generally, the research results could provide theoretical and methodological support for the oil import of petroleum companies and governments.

Abstract Background Since early 2022, patients with 2019 novel coronavirus (COVID-19) infection have increased rapidly in Shanghai, China. Nevertheless, there is no widely used unified rehabilitation treatment available for discharged patients with post-infection sequelae such as dyspnea, depression, and fatigue. To promote the rehabilitation of discharged patients, our team formulated Kangyi Qiangshen Gong exercise prescription on the basis of traditional Chinese medicine rehabilitation exercises (TCMRE). We designed a randomized controlled trial to evaluate the efficacy of rehabilitation and advantages of KQG for discharged patients with post-COVID-19 syndrome. Methods/design This is a parallel-design, two-arm, analyst assessor-blinded, randomized controlled trial. In total, 60 discharged patients with COVID-19 sequelae, aged from 20 to 80 years will be recruited and randomly assigned to the World Health Organization instructed breathing techniques (BT) group and the Kangyi Qiangshen Gong exercise prescription (KQG) group at a ratio of 1:1. The patients in the BT group will perform breathing techniques exercise, and the patients in the KQG group will perform KQG exercise. Both groups will perform exercises twice a day for 3 months. The primary outcome will be measured with the Modified Medical Research Council Dyspnea Scale, and the secondary outcomes will include the Modified Borg Scale, Fatigue Scale-14, Patient Health Questionnaire-9 Scale, Pittsburgh Sleep Quality Index, and the Respiratory Symptoms Scale. Clinical scales will be assessed at three points (pre-exercise, 3 months post-exercise, and 3 months follow-up). Adverse events will be recorded for safety assessment. Discussion This trial will serve high-quality evidence of the value of KQG for treating discharged patients with COVID-19 in rehabilitation period. Trial registration Chinese Clinical Trial Registry ChiCTR2200059504. Registered on 03 May 2022. Dissemination The results will be published in peer-reviewed journals and disseminated through the study’s website, and conferences.

Chronic obstructive pulmonary disease (COPD) has been increasingly accounted for global morbidity and mortality worldwide. Although it is partially reversible, the obstructive ventilatory schema of COPD often causes chronic inflammation that primarily affects peripheral airways, pulmonary parenchyma, and the development of lung lymphoid follicles. Among various T-helper (Th) cell types associated with COPD, Th1, Th2 and Th17 cell numbers are increased in COPD patients, whereas Treg cell number is reduced. Here, we reviewed recent advance in understanding the roles of Th1/Th2 and Th17/Treg in the pathogenesis of COPD and discussed the potential underlying mechanism.

This paper reports the organization and implementation of courses with ideological-political elements for postgraduates by means of questionnaires and interviews, and puts forward suggestions on improving the quality and learning outcomes of ideological-political courses, enhancing teachers awareness of courses with ideological-political elements and capacity building, establishing a curriculum reform system for courses with ideological-political elements, and paying attention to the synergistic effect between ideological-political elements and the relevant courses, so as to provide reference for the revision of syllabus,curriculum standard and remodeling of ideological-political courses.

Objective: Optimized extraction technology of Carum carvi L. essential oil and analyzed the volatile components. Methods: Supercritical carbon dioxide was used to extract essential oil from Carum carvi L. seeds and the volatile components of essential oil were analyzed by gas chromatography-mass spectrometry （GC-MS）. Results: The suitable extraction conditions of supercritical carbon dioxide were: temperature of extraction kettle 50 ℃, temperature of separation kettle 40 ℃, pressure of extraction kettle 30 MPa, pressure of separation kettle 0.4 MPa, flow rate of carbon dioxide 20 g/min, extraction time 90 min, extraction yield of essential oil was 4.79%. Compared with simultaneous distillation extraction （SDE）, supercritical carbon dioxide required less time and had higher extraction yield by diffusing into the sample particles quickly and dissolving the essential oil components fully without solvent residue. D-limonene （50.96%） and carvone （46.65%） were the main components of the essential oil extracted by supercritical carbon dioxide, and the type and relative content of volatile compounds were both higher than the essential oil obtained by SDE. Conclusion: Compared with SDE, the supercritical carbon dioxide was more suitable for the extraction of Carum carvi L. essential oil.

The literature on corporate political activity (CPA) generally views nonmarket actions aimed at influencing political actors (e.g., lobbying or campaign contributions) as related but separate activities from market actions. This study demonstrates how firms' core market actions (e.g., market entry or geographic expansion) can function as CPA. We theorize two mechanisms through which firms leverage market actions as CPA: "pork" (i.e., ones that primarily benefit a politician's constituents) and "perk" (i.e., ones that directly benefit the politician). We document these mechanisms through an empirical analysis of data from the U.S. airline industry in 1990–2019. Specifically, we find that airlines increase the supply of flights from the airports in the home district of the chair of the Transportation Committee in the U.S. House of Representatives (pork). We also find that the airlines increase the supply of nonstop flights to Washington, DC. from the Chair's district (perk). We use counterfactual estimation methods and exogenous turnovers in committee leadership to provide causal evidence. Moreover, the observed increase in flight supplies is negatively associated with formal policy changes in Congress, and with text mining techniques, we find that this effect is stronger for bills related to aviation safety and security. We contribute to the literature on CPA by demonstrating a blurred boundary between market and nonmarket actions, which helps explain firms' competitive actions that cannot be explained by market considerations alone. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.17026. [ABSTRACT FROM AUTHOR]

Groundwater contamination induced by anthropogenic activities has long been a global issue. Characterizing and modeling contaminant transport processes is crucial to groundwater protection and management. However, challenges still exist in process complexity, data constraint, and computational cost. In the era of big data, the growth of machine learning has led to new opportunities in studying contaminant transport in groundwater systems. In this work, we introduce a new attention‐based graph neural network (aGNN) for modeling contaminant transport with limited monitoring data and quantifying causal connections between contaminant sources (drivers) and their spreading (outcomes). In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). It also demonstrates a high level of applicability in inferring observations for unmonitored sites (i.e., inductive learning). Furthermore, an explanatory analysis based on aGNN quantifies the influence of each contaminant source, which has been validated by a physics‐based model with consistent outcomes with an R2 value exceeding 92%. The major advantage of aGNN is that it not only has a high level of predictive power in multiple scenario evaluations but also substantially reduces computational cost. Overall, this study shows that aGNN is efficient and robust for highly nonlinear spatiotemporal learning in subsurface contaminant transport, and provides a promising tool for groundwater management involving contaminant source attribution. Plain Language Summary: Groundwater contamination caused by human activities is a longstanding global challenge. Accurately characterizing and modeling the movement of contaminants is crucial for the protection and management of groundwater resources. However, the complexity of the processes, limitations in data availability, and high computational demands pose significant challenges. In the age of big data, machine learning offers new avenues for exploring contaminant transport in groundwater. In this study, we introduce a novel machine learning model called an attention‐based graph neural network (aGNN) designed to model contaminant transport with sparse monitoring data and to analyze the causal relationships between contaminant sources and observed concentrations at specific locations. We conducted five synthetic case studies across diverse aquifer systems with varying monitoring setups, where aGNN demonstrated superior performance over models based on other approaches. It also proved highly capable of making inferences about pollution levels at unmonitored sites. Moreover, an explanatory analysis using aGNN effectively quantified the impact of each contaminant source, with results validated by a physics‐based model. Overall, this study establishes aGNN as an efficient and robust method for complex spatiotemporal learning in subsurface contaminant transport, making it a valuable tool for groundwater management and contaminant source identification. Key Points: A novel graph‐based deep learning method is proposed for modeling contaminant transport constrained by monitoring dataThe proposed model quantifies the contribution of each potential contaminant source to the observed concentration at an arbitrary locationThe deep learning method substantially reduces the computational cost compared with a physics‐based contaminant transport model [ABSTRACT FROM AUTHOR]

Aiming to problems in the pairwise registration of point clouds, such as keypoints are difficult to describe accurately, corresponding points are difficult to match accurately and convergence speed is slow due to uncertainty of initial transformation matrix, we propose a novel feature descriptor based on ratio of rotational volume to describe effectively keypoints, and on the basis of the feature descriptor, we proposed an improved coarse-to-fine registration pipeline of point clouds, in which we use coarse registration to obtain a good initial transformation matrix and then use fine registration based on a modified ICP algorithm to obtain an accurate transformation matrix. Experimental results show that our proposed feature descriptor has a good robustness to rotation, noise, scale and varying mesh resolution, less storage space and faster running speed than PFH, FPFH, SHOT and RoPS descriptors, and our improved pairwise registration pipeline is very effective to solve the problems in the pairwise registration of point clouds.

To study the characteristics of nitrogen (N) loss on slopes, different vegetation (bare soil, alfalfa), slopes (5°, 10°, 15°), and rainfall intensities (40, 60, 80 mm/h) were set as variable factors in simulated rainfall experiments. Surface runoff accounts for 60.38–96.16% of total runoff and most N loss (57.69–88.67% of NO3−-N). Alfalfa can reduce average concentrations of N loss in runoff and reduce N loss in surface runoff by more than 48.29%, as well as subsurface runoff by 3.8%. Average N loss in subsurface runoff exceeds that of surface runoff. Rainfall intensity most affects N loss from surface runoff in bare soil conditions, and slope most affects N loss in subsurface runoff. Rainfall intensity in alfalfa treatments most influences runoff volume and N loss. The comprehensive effects of rainfall intensity, slope, and vegetation cover on the total loss of various forms of nitrogen in surface runoff can be described using a linear correlation equation, with a correlation coefficient between 0.84 and 0.91. [ABSTRACT FROM AUTHOR]

Rainfall intensity and slope gradient are the main drivers of slope surface runoff and nitrogen loss. To explore the distribution of rainfall runoff and nitrogen loss on the Miyun Reservoir slopes, we used artificial indoor simulated rainfall experiments to determine the distribution characteristics and nitrogen migration paths of surface and subsurface runoff under different rainfall intensities and slope gradients. The initial runoff generation time of subsurface runoff lagged that of surface runoff, and the lag time under different rainfall intensity and slope conditions ranges from 3.97 to 12.62 min. Surface runoff rate increased with increasing rainfall intensity and slope gradient; compared with a rainfall intensity of 40 mm/h, at a slope of 15°, average surface runoff rate at 60 and 80 mm/h increased by 2.38 and 3.60 times, respectively. Meanwhile, the subsurface runoff rate trended upwards with increasing rainfall intensity, in the order 5 > 15 > 10°. It initially increased and then decreased with increasing slope gradient, in the order 5 > 10 > 15°. Total nitrogen (TN) loss concentration of surface runoff shows a decrease followed by a stabilization trend; the concentration of TN loss decreases with decreasing rainfall intensity, and the stabilization time becomes earlier and is most obvious in 5° slope conditions. TN loss concentration in subsurface runoff decreased with increasing rainfall intensity, i.e., 40 > 60 > 80 mm/h. The surface runoff rainfall coefficient was mainly affected by rainfall intensity, a correlation between αs and slope gradients S was not obvious, and the fitting effect was poor. The subsurface runoff rainfall coefficient was mainly affected by slope gradient, the R2 of all rainfall intensities was <0.60, and the fitting effect was poor. The main runoff loss pathway from the Miyun Reservoir slopes was surface runoff, which was more than 62.57%. At the same time, nitrogen loss was subsurface runoff, more than 51.14%. The proportion of surface runoff to total runoff increases with the increase of rainfall intensity and slope, with a minimum of 62.57%, and the proportion of nitrogen loss from subsurface runoff also decreases with increasing rainfall intensity but does not change with slope gradient. The order of different runoff modulus types was mixed runoff (surface and subsurface runoff occur simultaneously) > surface runoff > subsurface runoff. The surface and mixed runoff modulus increased significantly with increasing rain intensity under different rain intensities and slope gradients. Overall, rainfall intensity significantly affected slope surface runoff, and slope gradient significantly affected nitrogen loss. [ABSTRACT FROM AUTHOR]