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A representative molecular model of kerogen is the prerequisite to the study on molecular interaction of fluid molecules with organic nanopores of shale. The molecular kerogen model in Silurian deep gas shale from southern China remains to be developed. In this work, the chemical structure of the targeted deep shale kerogen is characterized by organic geochemical measurements. The derived structural parameters are utilized to build the molecular structure of the kerogen unit. The kerogen unit shows a good representation of realistic kerogen structure in shale reservoir in terms of carbon skeleton, heteroatomic compositions and groups. The kerogen matrix and a extended kerogen slit model are constructed based on the kerogen unit, which match well with the experimental results in matrix density and kerogen porosity, respectively. The kerogen matrix is overall disordered with gentle stacking of polyaromatic fragments, due to the bendable aliphatic linkages and large-size aromatic units. The kerogen nanopores are not saturated with methane at deep shale reservoir conditions. At high pressure, the absolute adsorption tends to reach a maximum, while the excess adsorption shows a decreasing trend. The effect of pressure on the dissolved gas density is not pronounced. The generated kerogen model can serve as a starting point for further theoretical investigations on fluid storage and transport in organic nanopores of deep shale reservoir at microscopic scale.

A quantized fixed-time fault-tolerant attitude control problem for hypersonic reentry vehicles is considered from the perspective of practical engineering. Taking into account the limited time available for recovery from a fault scenario, fixed-time extended state observers are used to simultaneously compensate for the negative effect of unknown time-varying actuator faults, uncertainties, and external disturbances. In contrast to the existing control schemes, which neglect the signal quantization transmission process and limited communication capacity, the hysteresis quantization mechanism is considered. Based on this, a fixed-time fault-tolerant control scheme is developed to ensure the fixed time convergence of all the tracking errors even in cases involving actuator faults. The stability and convergence are proven by performing a theoretical analysis. The simulation results validate the superiority and robustness of the proposed control scheme.

Cisplatin resistance is a vital obstacle for the prognosis of ovarian cancer. However, the mechanism of cisplatin resistance is still unknown. This research was performed to explore the role of Nrf2 (nuclear factor, erythroid 2 like 2) and CD99 (CD99 molecule) in cisplatin resistance in ovarian cancer. QRT-PCR and Western blot were used to detect the expression of CD99 in ovarian cancer cells and tissues with different cisplatin sensitivities. Cell viability was analyzed by the Cell Counting Kit-8 (CCK8). The relationship of Nrf2 and CD99 was assessed by dual-luciferase reporter gene assay and chromatin immunoprecipitation (ChIP). Bioinformatics analysis was performed to search for the downstream gene of CD99. In this study, it was revealed that CD99 was highly expressed in cisplatin-resistant ovarian cancer cells and tissues, while lower CD99 expression was found in cisplatin-sensitive ovarian cancer cells and tissues. In addition, the overexpression of CD99 resulted in cisplatin resistance; on the other hand, knockdown of CD99 sensitized ovarian cancer to cisplatin. Furthermore, survival analysis indicated that overall survival (OS) and progression-free survival (PFS) of patients with higher CD99 expression were shorter than those with lower CD99 expression. It was also found that when Nrf2 was upregulated in cisplatin-sensitive ovarian cells, CD99 expression and cell viability increased after cisplatin treatment. Knockdown of CD99 could reverse cisplatin resistance induced by Nrf2. Conversely, when Nrf2 was knocked down in cisplatin-resistant ovarian cancer cells, CD99 expression and cell viability with cisplatin treatment decreased, while simultaneously upregulating CD99 reactivated cisplatin resistance in ovarian cancer cells. The dual-luciferase reporter gene assay and ChIP analysis suggested CD99 was a downstream gene of Nrf2, and Nrf2 positively regulated the expression of CD99 at the transcriptional level. In conclusion, Nrf2 induced cisplatin resistance in ovarian cancer cells by promoting CD99 expression. Targeted CD99 might be an effective way to reverse cisplatin resistance in ovarian cancer.

Shale gas is becoming an increasingly supplementary energy source because of its clean-burning and abundance. Economic gas production in shale requires the techniques of horizontal drilling and multistage hydraulic fracturing to create complex fracture network (CFN). How to accurately describe the characteristics of geometry and flow mechanisms of the CFN and select the most efficient approach for modeling are challenging. In this paper, a production forecasting model for multistage fractured horizontal well (MFHW) with CFN in shale is proposed based on the multiple interacting continua (MINC) theory (organic/inorganic matrix, natural fractures system) and lower-dimensional discrete fracture network (DFN) model (hydraulic fractures system). The model is designed to describe the unconventional flow mechanisms in shale system, such as fractal porous media and non-Darcy multiscale flow in ultra-tight matrix, ad-desorption on organic materials’ surface, rock un-consolidation within natural fractures, high-velocity turbulent flow near well range, and multiphase behaviors. We also propose a novel hybrid control volume finite element (CVFE) and finite-difference (FD) simulation method to obtain the numerical results of the model based on the unstructured 3D tri-prisms. The accuracy of the simulator is successfully validated and sensitivity analysis of some key factors (e.g.: fractal model permeability, Langmuir volume, heterogeneities of reservoir and fractures, well platform) are conducted to evaluate the impacts on production performance. Combing with the micro-seismic monitoring (MSM) data and engineering analyses, the DFN model is applied in Longmaxi shale formation to obtain the history matching with the field data and predict the production.

Shale gas is a potential substitute for the gradually depleted conventional oil and gas resources. Since studies on microscopic behavior of shale gas in nano−slits and the exact mechanical mechanisms behind it are still in urgent demand, this paper uses the model of graphite layers to describe microscopic details of shale gas occurrence behavior in organic−rich nano−slits from the viewpoint of molecular interactions by molecular simulation. “Dual adsorption mechanisms” and the consistency theory of gas distributions affected by wall effects under the condition of no overlapped wall force field are proposed to clarify the formation mechanisms of shale gas in the nano−slits. Results also show the critical channel width for all gases to be affected by the nano−scale effects is between 1 and 2 nm, and the regions (counted from the gas zone) for overlapped wall force field in 0.5 and 1 nm slits are about 0.32 and 0.42 nm, respectively. Furthermore, the effects of overlapped wall forces, channel size and pressure variations on gas aggregation and its movability etc., interaction mechanisms inside the nano−slits and their causalities, the development enlightenment for Knudsen layer (KL) and the whole gas have been explicitly depicted and clarified, which is expected to be a useful reference not only for shale gas evaluation and exploitation, but also for widespread research of gas occurrence phenomena in carbon−based materials in the field of industry.

Autonomous aerial refueling (AAR) exact docking control has always been an intractable problem due to the strong nonlinearity, the tight coupling of the 6 DOF aircraft model and the complex disturbances of the multiple environment flows. In this paper, the strongly coupled nonlinear 6 DOF model of the receiver aircraft which considers the multiple flow disturbances is established in the affine nonlinear form to facilitate the nonlinear controller design. The items reflecting the influence of the unknown flow disturbances in the receiver dynamics are taken as the components of the “lumped disturbances” together with the items which have no linear correlation with the virtual control variables. These unmeasurable lumped disturbances are estimated and compensated by a specially designed high order sliding mode observer (HOSMO) with excellent estimation property. With the compensation of the estimated lumped disturbances, a back-stepping high order sliding mode based exact docking flight controller is proposed for AAR in the presence of multiple flow disturbances. Extensive simulation results demonstrate the feasibility and superiority of the proposed docking controller.

This paper proposes a performance-focused and deep learning-based hierarchical fault-tolerant control framework for over-actuated hypersonic reentry vehicles. The hierarchical fault tolerance mechanism refers to the fault tolerance that can be realized both in the control allocation and control layers. First, a long short-term memory neural network-based fault diagnosis unit is proposed to solve the difficult problem of diagnosing actuator fault information under multisource disturbances. Based on the diagnosed fault information, the robust least-squares control allocation method is deployed to distribute the desired control moment to the actuator and achieve fault tolerance in the control allocation layer. Then, combined with a fixed time extended state observer, an improved multiple-degree-of-freedom prescribed performance control method is used to compensate the lumped disturbances, including control allocation error, ensure the prescribed tracking performance, and realize fault tolerance in the control layer. Finally, the theoretical analysis proves the stability of the proposed hierarchical fault-tolerant control scheme, and the effectiveness is verified by numerical simulation.

For shale gas resevoirs development, many researches focus on how to improve the estimated ultimate recovery (EUR), and ignore the relation of economic benefits and EUR. Therefore, based on the research of EUR evaluation procedure as the basis of economic estimation, the relation of economic benefits and EUR is revealed in detail, and the economy-geology-engineering (EGE) model is creatively built. The results show that six researched wells all have economic benefits: the average EUR is 1.60 × 108 m3, and the after-tax averages of net present value, financial internal rate of return and investment payback period (Pt) are 434.32 × 104 USD, 10.39% and 4.45 years respectively. Combining with the seepage theory and on-site experience, the negative relation of Pt and development degree of free gas is determined. The economic benefits for the unit volume of shale gas are close about 0.03 USD/m3 under the current development technologies. The influence degrees of economic parameters from large to small are gas price, drilling cost, fracturing cost, business cost and subsidy respectively. The proposed EGE model can effectively predict the economic benefits with the complex geology-engineering factors in the Changning block, and the corresponding method can be generalized to more fields.

Aiming at the complexity and particularity of urban environment, a solar-powered UAV (SUAV) path planning framework is proposed in this paper. The framework can be decomposed into three aspects to resolve. First, to make SUAV avoid the building obstacles, a nature-inspired path planning method called Interfered Fluid Dynamical System (IFDS) is introduced. Aiming at the defect that the traditional IFDS is not suitable for SUAV energy optimization calculation, the dynamic constraints and model are introduced to IFDS. The modified IFDS, called Restrained IFDS (RIFDS), is proposed. Second, to resolve the path planning issue efficiently, a novel intelligent optimization algorithm called Whale Optimization Algorithm (WOA) is selected as the basic framework solver. To further overcome the drawback of local minima, adaptive chaos-Gaussian switching solving strategy and coordinated decision-making mechanism are introduced to the basic WOA. The modified algorithm, called Improved WOA (IWOA), is proposed. Third, to solve the accurate modeling problem of solar energy in urban environment, two measures are adopted: (1) A practical judgment method for sunlight occlusions is proposed; (2) Aiming at some unreasonable aspects in the solar energy production model, the received solar energy is modified and recalculated by ASHRAE Clear Sky Model and the solar irradiance calculation principle for slant surfaces in this paper. Finally, the effectiveness of the proposed framework is tested by the simulations.

In recent years, the greenhouse gas control has been a hot issue. A solution of injecting supercritical CO2 (scCO2) in Longmaxi shale gas formation (in Southwest China) is proposed in this study. To evaluate the feasibility of this method that storing CO2 and enhanced gas recovery (EGR) simultaneously, we first carried out isothermal adsorption experiments on samples from Longmaxi formation to describe the adsorption behavior of CH4 and CO2, and a generalized Ono-Kondo Lattice (OK) model was applied to predict adsorption amount of pure CH4/CO2 and their binary mixtures under supercritical, high-pressure conditions. In addition, discrete fracture network (DFN) model was adopted to characterize the complex hydraulic fracture networks constructed from micro-seismic monitoring (MSM) data and engineering analysis. The porous flow in shale matrix was modeled with the multiple interacting continua (MINC) and fractal theories. A multiscale compositional numerical model based on unstructured tri-prism grids was finally developed and solved by control volume finite element (CVFE) method. The simulation results of base and CO2 injection cases presented that scCO2 huff ‘n’ puff in Longmaxi formation might be an effective method for CO2 storage and EGR, and the orthogonal experiments were designed to optimize scenarios in the field application and obtain a maximum balanced result of EUR and CO2 storage capacity (CSC) for multistage fractured horizontal well (MFHW) with complex fracture networks in shale.

Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target tracking problem in urban environment is modeled by formulating the SUAVs kinematic and target models, the urban environment constraints, solar power harvesting and consumption models for SUAV, and sight occlusions by constructions. Especially, the sight occlusions in urban environment for SUAV are taken into consideration for the first time in this paper. A judgment method of sight occlusions for SUAV is proposed to make the calculation of the energy index more precise. Second, based on the precise modeling, the DMPC method is adopted as the framework for trajectory optimization in real time. Third, AGOA, a novel intelligent algorithm to mimic the behaviors of grasshoppers, is proposed to be the DMPC solver. The proposed AGOA has a better searching ability than the traditional GOA and some other intelligent algorithms by introducing some improvement measures e.g. the natural selection strategy, the democratic decision-making mechanism, and the dynamic feedback mechanism based on the 1/5 Principle. Finally, the effectiveness of the proposed method is demonstrated by the simulations.

This paper proposes an active disturbance rejection control (ADRC) scheme for automatic carrier landing system (ACLS) in final approach. To track desired touchdown point (DTP) altitude in the presence of carrier air wake turbulence and deck motion, based on the estimation and compensation of internal dynamics and external disturbances by extended state observer (ESO), outer-loop altitude controller and inner-loop attitude controllers are designed via ADRC. Moreover, tracking differentiator-based deck motion compensation (TD-DMC) is designed to eliminate the inherent phase lag in ACLS. In addition, controller parameters are optimized via a novel Levy flight based whale optimization algorithm (LWOA). Finally, comparative simulations demonstrate that the proposed ADRC-based ACLS has better tracking performance and robustness compared to conventional ACLS.

The highly feasible trajectory planning of unmanned aerial vehicle (UAV) is very important in some tasks but has not yet attracted sufficient study attention. Most current studies use simplified UAV model with some state constraints to plan the trajectory, but the feasibility is reduced, because the simplified model is very different from the actual UAV system, so that the tracking characteristics of UAV cannot be fully considered. In this paper, a novel control-oriented UAV highly feasible trajectory planning method is proposed. First, a UAV closed-loop model prediction method, which is the combination of a low-level controller and a UAV 6 DOF nonlinear model, is adopted in the trajectory planning phase to predict the flight trajectory. This complicated model is very similar to the actual UAV system because it comprehensively considers the controller performance and the detailed UAV model, but it also has poor efficiency. Therefore, a trajectory-mapping network (TMN) is proposed using a deep learning approach to improve the planning efficiency. Furthermore, a novel time-series convolutional neural network (TSCNN) is proposed for the TMN to further improve its computation speed and prediction accuracy. Finally, the flight trajectory predicted by the TMN is used to evaluate the planning cost. In this way, the planned trajectory will be highly feasible. The effectiveness of the proposed method is demonstrated by simulations.

Background miR-194-5p has been associated with drug resistance in many cancers. However, the role of miR-194-5p in cisplatin resistance in ovarian cancer is still unclear. Materials and methods To study the role and mechanism of miR-194-5p in cisplatin resistance, qRT-PCR was performed to determine the expression of miR-194-5p and SLC40A1 in ovarian cancer. Cell Counting Kit-8 (CCK8) assay was used to analyse cell viability after cisplatin treatment. Dual-luciferase reporter gene assay was performed to examine the relationship between miR-194-5p and SLC40A1. The genes downstream of SLC40A1 were investigated through bioinformatics analysis. Results Compared to cisplatin-sensitive ovarian cancer cells, higher miR-194-5p expression and lower SLC40A1 expression were found in cisplatin-resistant ovarian cancer cells. Moreover, this study demonstrated that over-expression of miR-194-5p inhibited SLC40A1 expression, and knockdown of miR-194-5p promoted SLC40A1 expression. In addition, dual-luciferase reporter gene assay further confirmed the negative correlation between miR-194-5p and SLC40A1. Furthermore, we found that over-expression of miR-194-5p resulted in cisplatin resistance. When miR-194-5p and SLC40A1 were simultaneously up-regulated, cisplatin sensitivity increased, while down-regulation of miR-194-5p sensitised ovarian cancer cells to cisplatin. However, when miR-194-5p and SLC40A1 were simultaneously down-regulated, cisplatin sensitivity was decreased. These data suggested that miR-194-5p inhibited SLC40A1 expression to induce cisplatin resistance. In addition, bioinformatics analysis indicated a positive correlation of SLC40A1 with hephaestin (HEPH), and homeostatic iron regulator (HFE). However, we found that HEPH and HFE were associated with cisplatin resistance, suggesting that their role in drug resistance is induced by miR-194-5p/SLC40A1. Conclusion In conclusion, we found that miR-194-5p inhibited SLC40A1 expression to induce cisplatin resistance in ovarian cancer. This study suggests that miR-194-5p could be a potential therapeutic target and a prognostic biomarker for ovarian cancer, with important implications for future research.

We investigated the difference in production performances of three shale gas wells with different completions and fracturing designs. We used assisted history matching technique to perform production match automatically and obtain multiple history matching solutions. The history matching algorithm used in this study is neural networks Markov chain Monte Carlo. In addition, we utilized embedded discrete fracture model technique to model hydraulic fractures instead of local grid refinement method to speed up the simulation runs. The history matching parameters consist of fracture half-length, fracture height, fracture conductivity, fracture width and fracture water saturation for hydraulic fractures properties and include matrix porosity, matrix permeability and relative permeability curves for matrix properties. We found that fracture width, fracture half-length, fracture conductivity and matrix permeability were significantly different in three shale gas wells while fracture height, fracture water saturation, porosity and relative permeability curves were less different. The information from this study will be used further to improve completion and fracturing design.

We performed assisted history matching (AHM) in a real shale gas well using uncertain parameters such as fracture geometry, fracture conductivity, matrix permeability, matrix and fracture water saturation, and relative permeability curves. We also investigated the performance of two proxy models including K-nearest neighbors (KNN) and neural networks (NN) to be used in multiple-proxy-based Markov chain Monte Carlo (MCMC) algorithm. We emphasized the performance of both proxy models by comparing the number of history matching solution found and elapsed time. While, KNN required less elapsed time by half than NN, we found that NN performed better in terms of accuracy and predictability than KNN. In other words, NN required a smaller number of simulations by half than KNN in order to obtain the same number of history matching solutions. Therefore, it depends on what is more important to each problem either number of simulations or elapsed time. For history matching result, both proxy models in the multiple-proxy-based MCMC algorithm have similar results of posterior distribution of uncertain parameters. This confirms the robustness of the proposed history matching algorithm. The benefits of this study are that we can characterize fracture geometry and reservoir properties in a probabilistic manner. These multiple realizations can be further used for a probabilistic production forecast, future fracturing design, and well spacing optimization and planning. This AHM workflow can be applied to any hydraulic-fractured wells with historical production data.