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Rock pyrolysis, organic petrology, vitrinite reflectance, gas chromatography-mass spectrometry (GC-MS) analysis, and biomarker compound analysis were performed to comprehensively analyze the organic geochemical characteristics of the Jurassic Da’anzhai Member (J1da) shale strata in Yuanba and Puguang areas in the northeastern Sichuan Basin. Then the organic matter provenance and sedimentary environment were further analyzed. Finally, the significance of oil and gas exploration in J1da shale strata was discussed. Results show that the second section of the Da’anzhai Member (J1da2) has relatively high organic matter abundance (1.24%TOC), type Ⅱ-dominated organic matter type, which is the most favorable section of wells Y1 and T1 in the study area. The organic matter maturity and the hydrocarbon phases are quite different, which is 1.01%Ro dominated by oil generation in Puguang area, while it is 1.67%Ro dominated by gas generation in Yuanba area. Content and chromatograms of biomarkers including n-alkanes, tricyclic terpanes, C24 tetracyclic terpanes, and C27-C28-C29 regular steranes show that the organic matters of J1da shale strata derive from both terrestrial higher plants and lower aquatic organisms, with slightly differentiated mixed ratio of each sublayer. Characteristics of Pr/Ph, γ-cerane and hopanes compounds indicate that the overall depositional environment of J1da is a freshwater lacustrine environment, with saline lake deposits in local areas and intervals in the study area. The rapid changes of sedimentary environment have resulted in obvious stratification of water body, frequent interbeds, and strong heterogeneity of J1da shale strata. Comprehensive analysis shows the shale/mud microfacies in the semi-deep lake subfacies and shale/mud interbedded with siltstone and shell bank microfacies in the shallow lake subfacies are the most favorable sedimentary facies for J1da hydrocarbon enrichment. Deeper burial depth and higher maturity make for oil and gas enrichment with higher gas/oil ratio (GOR); moreover, the thicker intervals with organic-rich shale are favorable targets for geochemical evaluation.

Autonomous driving vehicle could increase driving efficiency, reduce traffic congestion and improve driving safety, it is considered as the solution of current traffic problems. Decision making systems for autonomous driving vehicles have significant effects on driving performance. The performance of decision making system is affected by its framework and decision making model. In real traffic scenarios, the driving condition of autonomous driving vehicle faced is random and time-varying, the performance of current decision making system is unable to meet the full scene autonomous driving requirements. For autonomous driving vehicle, the division between different driving behaviors needs clear boundary conditions. Typically, in lane change scenario, multiple reasonable driving behavior choices cause conflict of driving state. The fundamental cause of conflict lies in overlapping boundary conditions. To design a decision making system for autonomous driving vehicles, firstly, based on the decomposition of human driver operation process, five basic driving behavior modes are constructed, a driving behavior decision making framework for autonomous driving vehicle based on finite state machine is proposed. Then, to achieve lane change decision making for autonomous driving vehicle, lane change behavior characteristics of human driver lane change maneuver are analyzed and extracted. Based on the analysis, multiple attributes such as driving efficiency and safety are considered, all attributes benefits are quantified and the driving behavior benefit evaluation model is established. By evaluating the benefits of all alternative driving behaviors, the optimal driving behavior for current driving scenario is output. Finally, to verify the performances of the proposed decision making model, a series of real vehicle tests are implemented in different scenarios, the real time performance, effectiveness, and feasibility performance of the proposed method is accessed. The results show that the proposed driving behavior decision making model has good feasibility, real-time performance and multi-choice filtering performance in dynamic traffic scenarios.

Cloud Computing is becoming more and more popular for solving problems that need high concurrency and a lot of resources. Many traditional areas of research choose to solve their problems through the cloud, and workflow scheduling is one of them. Cloud computing brings many benefits, meanwhile, due to the almost “infinite” amount of resources for users, it also brings new challenges for scheduling and optimization, in which cost and makespan are the most concerned issues for workflow scheduling. Users want to obtain a low cost and fast makespan solution. This paper focuses on how to find an optimized solution to achieve better cost-makespan at the same time under the constraint of deadline. In order to solve this problem, an immune particle swarm optimization algorithm (IMPSO) is proposed, which effectively improves the quality and speed of the optimization. The proposed IMPSO overcomes the problem of slow convergence of PSO, which is easy to fall into local optimization. Experiments show the efficiency and effectiveness of the proposed approach.

The effectiveness of transaction fraud detection methods directly affects the loss of users in online transactions. However, for low-frequency users with small transaction volume, the existing methods cannot accurately describe their transaction behaviors for each user, or lead to a high misjudgment rate. So we propose a new method for individual behavior construction, which can make the behavior of low-frequency users more accurate by migrating the current transaction group behavior and transaction status. Firstly, we consider the user's only historical transactions, combined with the optimal risk threshold determination algorithm, to form the user's own transaction behavior benchmark. Secondly, through the DBSCAN clustering algorithm, the behavior characteristics of all current normal samples and fraud samples are extracted to form the common behavior of the current transaction group. Finally, based on historical transaction records, the current transaction status is extracted using a sliding window mechanism. The combination of the three constitutes a new transaction behavior of the user. On this basis, a multi-behavior detection model based on new transaction behavior is proposed. According to the result of each behavior, Naive Bayes model is used to calculate the probability that current transaction belongs to fraud, and finally determine whether current transaction is fraud. Experiments prove that the method proposed in this paper can have a good effect on low-frequency users, which can accurately identify fraud transactions and has a low misjudgment rate for normal transactions.

Background: Combination therapy has become an attractive option in pulmonary arterial hypertension (PAH) treatment. The aim of this study was to investigate whether additional use of prostacyclin analogs could exert any additional benefits over background targeted therapies in PAH patients.Methods: Searches were performed on PubMed, Embase, and ClinicalTrials.gov from inception to 1 October 2021. Randomized controlled trials were included if patients had been treated with prostacyclin analog-containing combination therapy and compared with the use of other PAH-specific background therapies. The bias risk and statistical analysis of the enrolled studies were performed with RevMan 5.1. Sensitivity analysis and funnel plot were used to evaluate the stability and publication bias, respectively. PROSPERO registered number CRD42021284196.Results: Ten trials involving 1828 patients were included. Prostacyclin analog treatment was associated with greater improvement in clinical worsening (risk ratio [RR], 0.70; 95% confidence interval [CI], 0.57–0.86), 6-min walk distance (mean difference [MD], 37.17 m; 95% CI, 3.01–71.33 m), NYHA/WHO functional class (RR, 1.58; 95% CI, 1.21–2.05), mean pulmonary artery pressure (MD, −9.23 mmHg; 95% CI, −17.44 to −1.03 mmHg), and cardiac index (MD, 0.41 L/min/m2; 95% CI, 0.26–0.55 L/min/m2) than the control group. No significant differences in pulmonary vascular resistance (MD, −137.22 dyn·s/cm5; 95% CI, −272.61 to −1.84 dyn·s/cm5) and all-cause mortality (RR, 0.96; 95% CI, 0.57–1.61) were found between the prostacyclin analog group and control group. Of note, more adverse events (RR, 1.07; 95% CI, 1.02–1.13) occurred in the prostacyclin analog group but no significant increase in serious adverse events (RR, 1.25; 95% CI, 0.75–2.11).Conclusion: Additional prostacyclin analog treatment exerted benefits on clinical worsening, exercise capacity, functional class, mean pulmonary artery pressure, and cardiac index in PAH patients, but it was associated with overall risk of adverse events.Clinical Trial Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021284196, identifier CRD42021284196.

Accurate recognition of pedestrian crossing intentions is essential for the safe operation of autonomous vehicles on urban roads. However, the current pedestrian crossing intention recognition model has the problems of relatively low recognition accuracy and short recognition advance time. Based on the above problems, this paper carried out a study on the recognition model of pedestrian crossing intention. Firstly, the pedestrian and vehicle crossing data were collected through laser radar and a high-definition monitor, and 1980 groups of valid samples were selected. Secondly, the pedestrian crossing intention characterization parameter set was determined through statistical analysis. Finally, this paper proposes a pedestrian crossing intention recognition model based on stacking ensemble learning. The ensemble learning framework integrates random forest (RF), support vector machine (SVM), long short-term memory network (LSTM), an attention mechanism, and bidirectional LSTM (AT-Bi-LSTM). Compared with traditional machine learning methods, the proposed method shows greater advantages in recognition accuracy. The model recognition accuracy reaches 95.36% when the model is recognized at 0.5 s before crossing the zebra crossing, and the model recognition accuracy is 89.27% when the model is recognized at 1s before crossing the zebra crossing. The research in this paper is of great significance for building a more intelligent pedestrian-vehicle collaboration and promoting the industrial application of the autonomous vehicle.

Various types of marine-continental transitional facies are present in the gas-bearing shales of the southeastern Sichuan Basin. A review of the different lithological assemblages in these rocks is important for assessing the likely shale gas content and the development of the storage space. This study of the lithological assemblages of the Permian Longtan Formation in the southeastern Sichuan Basin at Well X1 used core observations, optical thin-section observations, Ar-ion polishing, scanning electron microscopy, and nitrogen adsorption tests to compare and analyze storage space types and pore structures in the shale to determine the sedimentary paleoenvironment, petromineralogy, and organic content. The marine-continental transitional facies in the study area were deposited in a warm climate that favored enrichment by organic matter. The kerogen is type II2-III (average vitrinite reflectance 2.66%), which is within the favorable thermal maturity range for the presence of shale gas. The lithology mainly consists of shale, siltstone, and limestone (with bioclasts), as well as a coal seam. The lithological development divides the Longtan Formation into lower (swamp), middle (tidal flat/lagoon), and upper (delta) sub-members. From lower to upper divisions, the lithofacies evolved from silty shale to clay shale and then to shale intercalated with siltstone or calcareous layers. The proportions of intergranular and dissolution pores in the clay minerals decrease gradually from lower to upper sub-members, and pore size sizes also tend to decrease. Relatively large-diameter pores and microfractures occur in the inorganic matter in the lowest sub-member. Quartz and clay are the main constituents of the shale, respectively contributing to the specific surface area and specific pore volume of the reservoir space.

The Silurian Longmaxi (S1l) marine shale and Jurassic Dongyuemiao (J1d) lacustrine shale in the Sichuan Basin, West China have attracted considerable attention from the oil-and-gas industry in China. Currently, the differences in pore types and pore structures between them are poorly understood, which has limited shale-resource exploration in the Sichuan Basin. This paper systemically compares the pore characteristics of Longmaxi shale and Dongyuemiao shale and investigates their impact on shale-gas enrichment by integrating field-emission scanning electron microscopy (FE–SEM), X-ray diffraction (XRD), low-pressure gas (CO2 and N2) adsorption and mercury-intrusion porosimetry, high-pressure sorption isotherms, gas-saturation measurement, molecular-dynamics simulation, etc. The results show that the S1l organic-rich marine shale and the J1d lacustrine shale have different pore types and pore structures. The S1l shale is dominated by organic pores, mainly micropores and mesopores with ink-bottle-like pore shapes, while the J1d shale is primarily composed of clay-mineral pores, mainly mesopores and macropores with slit- or plate-like pore shapes. Organic pores can provide considerable storage space for shale-gas enrichment in S1l marine shale, which also determines the adsorption capacity of shale reservoirs. Although organic pores are not the most prevalent in the Dongyuemiao lacustrine shale, they also play an important role in enhancing reservoir quality and absorbed-gas enrichment. Clay-mineral pores contribute weakly to the storage space of J1d-lacustrine-shale reservoirs. Mesopores are the most important form of storage space in both S1l shale and J1d shale, contributing significantly to shale-gas enrichment. Micropores are secondary in importance in S1l marine shale, while macropores are secondary contributors to pore volume in J1d lacustrine shale.

To solve the problem of the real-time path-planning of autonomous vehicles for obstacle avoidance on structured roads, a path-planning approach based on the B-spline algorithm is proposed in this paper. Firstly, the mechanism of driver path planning is analyzed, and a dynamic risk-identification model based on the support vector machine is proposed. It combines the driver’s risk perception characteristics and a risk model. Then, the B-spline algorithm model is improved based on the risk-identification model. Furthermore, road features, road constraints and dynamic constraints are considered to further optimize the planning algorithm. To verify the path-planning approach proposed in this paper, a co-simulation experiment based on CarSim/Simulink is conducted. Results show that the improved algorithm is effective in static and dynamic obstacles avoidance. The algorithm can generate collision-free obstacle avoidance paths, and the paths meet the real-time requirements and dynamic constraints of obstacle avoidance scenarios. In addition, the proposed algorithm optimizes the path according to the driver’s operating characteristics, which can further improve the safety and comfort of autonomous vehicles.

The lacustrine shale in the Dongyuemiao Member of the Fuling area, Sichuan Basin, is widely distributed and has huge shale oil resource potential. It is one of the important replacement areas for shale oil exploration in China. To investigate the key shale oil evaluation well, Well FY10, in the Fuling area, X-ray diffraction (XRD) mineral analysis, Rock-Eval, argon ion polishing-scanning electron microscope (SEM), Mercury injection capillary pressure (MICP), and low pressure nitrogen adsorption were launched to determine the heterogeneity of the pore system in the lacustrine shale of the Dongyuemiao Member. The mineral composition exhibits a high degree of heterogeneity, and the shale can be divided into two main lithofacies: argillaceous shale and mixed shale. The porosity ranges from 2.95 to 8.43%, and the permeability ranges from 0.05 to 1.07 × 10−3 μm2. The physical properties of mixed shale are obviously better than those of argillaceous shale. Inorganic mineral pores, such as linear pores between clay minerals and calcite dissolution pores, are mainly developed, while a small amount of organic pores can be observed. The average total pore volume (Vp) is 0.038 ml/g with an average specific surface area of 5.38 m2/g. Mesopores provide the main Vp (average 61.72%), and micropores provide mostly specific surface area. TOC imposes a strong controlling effect on the development of micropores. Clay minerals are the main contributors to mesopores and macropores. The organic-inorganic interaction during the process of diagenesis and hydrocarbon generation controls the formation of shale pore systems.

Arabidopsis EH/Pan1 proteins are part of the TPLATE complex (TPC) that is required for endocytosis in plants. Here, the authors show AtEH/Pan1 proteins also act in actin-mediated autophagy, by interacting with VAP27-1 at ER-PM contact sites and recruiting TPLATE and AP-2 complex subunits, clathrin and ARP2/3/ proteins to autophagosomes.

Vehicle handing stability under the combined acceleration steering and braking steering conditions has a great impact on the vehicle safety. Therefore, the coupled steering and driving/braking stability control is essential. Thus, a novel controller based on differential flatness and vehicle stability region is proposed. First, the reference vehicle state variables are calculated and the vehicle stability region is established based on vehicle dynamics and bifurcation analysis. Then, the flatness-based controller is designed which can ensure the vehicle handing stability by controlling the vehicle states approaching their references. Based on the driver preview model, the vehicle can keep good path tracking effect by tracking the lateral deviation. The simulation results of the double lane change tests show that the proposed control method can ensure both the vehicle handing stability and the path tracking effect. Compared to vehicle equipped with the integration of active front steering and direct yaw moment control, vehicle controlled by flatness-based controller performs better in both handing stability and path tracking.

Automatic steering system of self-driving vehicles has significant influence on vehicle safety. The performance of automatic steering system is affected by external disturbance, parameter perturbation, and real-time ability of control algorithm. First, to develop a high-performance automatic steering control strategy, a vehicle-road system model based on vehicle dynamics and kinematics is established. Then, the system state equation with direction-angle and lateral-position deviations is derived to describe the tracking accuracy. Finally, based on the sliding mode variable structure control method, an automatic steering control algorithm is proposed and verified. The results show that the dynamic quality of the system is improved with the optimization of the sliding mode. Furthermore, good robustness against vehicular velocity, real-time performance, and tracking accuracy is achieved.

With the development of cloud computing, the cloud market is becoming more and more complicated. In a cloud data center, there are many cloud instance types with different computing capacity and price, which brings users some confusions when they select cloud instance types. In order to solve this selection problem, a cloud brokering architecture is proposed. In this architecture, the selection problem is modeled as a multi-objective optimization problem, and through analysis, we get the relationship between complete Pareto set and solution space. Based on this, a two-stage Cloud Instance Type Selection Model (CITSM) is proposed to help users select the cloud instance types. The first stage is Complete Pareto Set Generation Algorithm (CPSGA) which can generate a complete Pareto set of the cloud instance type selection schemes. Then, the Optimal cloud instance type selection Scheme Screening Algorithm (OSSA) is used to select one scheme from the complete Pareto set. We perform some experiments to prove the proposed CITSM is efficient and effective. The proposed method can also solve the single objective optimization problem by modifying OSSA, which illustrates the scalability.

In this era of Internet and big data, there is billions of news generated every day, and the traditional manual methods are insufficient for public opinion orientation analysis. Especially for Chinese, which has more complicated syntax and semantic structure, and there is no space between words as separator. This greatly increases the difficulty of analyzing opinion orientation. In this paper, a novel approach is proposed aiming at solving the problem of public opinion orientation analysis based on Chinese news. The approach combines word2vec, sentiment dictionaries and syntax rules, where the word2vec can map words into different vectors with finite dimensions. Through it we can calculate the cosine similarity between the words and sentiment dictionaries to get the orientation value of target words, which is helpful for calculating the orientation value of key sentences and full text. Specifically, the process consists of three steps. First, word2vec is used to train word embedding, and every word in corpus is mapped into a given vector space. Then, key sentences are extracted from news content. Finally, pre-defined syntax rules with word vector similarity are used to analyze document orientation based on key sentences. Several experiments are conducted on both closed and open datasets, and the results validate the effectiveness of the proposed approach.

The optimal design of a super highspeed flywheel rotor could improve flywheel battery energy density. The improvement of flywheel battery energy density could enhance the performance of the flywheel lithium battery composite energy storage system. However, there are still many problems in the structure, material and flywheel winding of super highspeed flywheels. Therefore, in this paper, electric flywheel energy and power density parameters are designed based on CPE (Continuous Power Energy) function and vehicle dynamics. Then, according to the design index requirements, the structure, size and material of the electric flywheel rotor are designed. Furthermore, the numerical analysis model of stress and displacement of multi-ring interference fit flywheel rotor under plane stress state is established. On this basis, the influence laws of flywheel rotor wheel flange numbers and interlaminar interference on stress distribution of flywheel rotor are analyzed, and the assembly form of wheel flange is determined. Finally, the stress check of the flywheel rotor is completed. The results show that the super highspeed flywheel rotor designed in this paper meets vehicle dynamics requirements in terms of energy storage and power. In terms of strength, it meets the design requirements of static assembly stress and dynamic stress at maximum speed.

Environment perception is the foundation of the intelligent driving system and is a prerequisite for achieving path planning and vehicle control. Among them, obstacle detection is the key to environment perception. In order to solve the problems of difficult-to-distinguish adjacent obstacles and easy-to-split distant obstacles in the traditional obstacle detection algorithm, this study firstly designed a 3D point cloud data filtering algorithm, completed the point cloud data removal of vehicle body points and noise points, and designed the point cloud down-sampling method. Then a ground segmentation method based on the Ray Ground Filter algorithm was designed to solve the under-segmentation problem in ground segmentation, while ensuring real time. Furthermore, an improved DBSCAN (Density-Based Spatial Clustering of Application with Noise) clustering algorithm was proposed, and the L-shaped fitting method was used to complete the 3D bounding box fitting of the point cloud, thus solving the problems that it is difficult to distinguish adjacent obstacles at close distances caused by the fixed parameter thresholds and it is easy for obstacles at long distances to split into multiple obstacles; thus, the real-time performance of the algorithm was improved. Finally, a real vehicle test was conducted, and the test results show that the proposed obstacle detection algorithm in this paper has improved the accuracy by 6.1% and the real-time performance by 13.2% compared with the traditional algorithm.

A wide range of hemocyte types exist in insects but a full definition of the different subclasses is not yet established. The current knowledge of the classification of silkworm hemocytes mainly comes from morphology rather than specific markers, so our understanding of the detailed classification, hemocyte lineage and functions of silkworm hemocytes is very incomplete. Bombyx mori nucleopolyhedrovirus (BmNPV) is a representative member of the baculoviruses and a major pathogen that specifically infects silkworms (Bombyx mori) and causes serious losses in sericulture industry. Here, we performed single-cell RNA sequencing (scRNA-seq) of hemocytes in BmNPV and mock-infected larvae to comprehensively identify silkworm hemocyte subsets and determined specific molecular and cellular characteristics in each hemocyte subset before and after viral infectmadion. A total of 20 cell clusters and their potential marker genes were identified in silkworm hemocytes. All of the hemocyte clusters were infected by BmNPV at 3 days after inoculation. Interestingly, BmNPV infection can cause great changes in the distribution of hemocyte types. The cells appearing in the infection group mainly belong to prohemocytes (PR), while plasmatocytes (PL) and granulocytes (GR) are very much reduced. Furthermore, we found that BmNPV infection suppresses the RNA interference (RNAi) and immune response in the major hemocyte types. In summary, our results revealed the diversity of silkworm hemocytes and provided a rich resource of gene expression profiles for a systems-level understanding of their functions in the uninfected condition and as a response to BmNPV.

Deoxynivalenol (DON) can activate related signaling pathways and induce gastrointestinal disorders. Based on the results of previous studies, this study tried to explore the relationship between DON-induced intestinal inflammation of weaned rabbits and the ERK-p38 signaling pathway. Forty-five weaned rabbits were divided into three treatments: control, LD and HD group. All rabbits were treated with diet containing a same nutrient content, but animals in the LD and HD groups were additionally administered DON via drinking water at 0.5 and 1.5 mg/kg b.w./d, respectively. The protocol consisted of a total feeding period of 31 days, including a pre-feeding period of 7 days. Western blotting, qRT-PCR, and immunohistochemistry were applied for analysis the expression of protein and mRNA of extracellular signal-regulated kinase (ERK), p38, double-stranded RNA-activated protein kinase (PKR), and hematopoietic cell kinase (Hck) in the duodenum, jejunum, and ileum of rabbits, as well as the distribution of positive reactants. The results proved that DON intake could enhance the levels of inflammatory factors in serum and damage the intestinal structure barrier of rabbits. Meanwhile, DON addition can stimulate the protein and mRNA expression for ERK, p38, PKR, and Hck in the intestine of rabbits, especially in the duodenum, as well as expand the distribution of positive reactants, in a dose-dependent manner.

In financial anti-fraud field, negative samples are small and sparse with serious sample imbalanced problem. Generating negative samples consistent with original data to naturally solve imbalanced problem is a serious problem. This article proposes a new method to solve this problem. We introduce a new generation model, combined Generative Adversarial Network with Long Short-Term Memory network for one-dimensional negative financial samples. The characteristic association between transaction sequences can be learned by long short-term memory layer, and the generator covers real data distribution by the adversarial discriminator with time-sequence. Mapping data distribution to feature space is a common evaluation method of synthetic data; however, relationships between data attributes have been ignored in online transactions. We define a comprehensive evaluation method to evaluate the validity of generated samples from data distribution and attribute characteristics. Experimental results on real bank B2B transaction data show that the proposed model has higher overall ratings, which is 10% higher than traditional generation models. Finally, well-trained model is used to generate negative samples and form new dataset. The classification results on new datasets show that precision and recall are all higher than baseline models. Our work has a certain practical value and provides a new idea to solve imbalanced problem in whatever fields.

We study the research status of thermal simulation experiment and put forward three issues worthy of attention and five important development directions. Classification according to thermal simulation system is the most widely used classification scheme. Different thermal simulation experimental systems have their own characteristics, and according to different experimental purpose, a suitable thermal simulation experiment system can be selected according to different experimental purposes. The closed experimental system is more suitable for the thermal simulation experiment of humic source rocks. The online analysis of open system has unique advantages in the study of volatile components. The semi-open system is the most closest to the thermal simulation system of the thermal evolution of the source rocks in the actual geological condition. Three key issues are presented concerning the thermal simulation experiment are presented. The first is the influence of water on the thermal simulation experiment. The second is whether convincing isotope reversal can be presented expect Fischer–Tropsch synthesis. The third is that, in the study of the thermal simulation experiment, the model of hydrocarbon generation must be built in combination with the actual geological background. Five key development directions concerning the thermal simulation experiment are proposed. The first is thermal simulation experimental study on a relatively low temperature and a long time with water participation. The second is experimental research on thermal simulation of unconventional petroleum. The third is a study on the development of pore microcracks in source rocks and the correlation of the interaction of fluid discharge in thermal simulation experiments. The fourth is an experimental study on thermal simulation of carbonate source rock. The fifth is an experimental study of thermal simulation related to abnormal pressure. Keywords: Thermal simulation experiment, Research status, Classification scheme, Development direction