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The Chang 7 Member of the Ordos Basin has favorable shale oil resource potentiality. The exploration and development of sandstone interlayer shale oil in the Chang 7 Member has made substantial breakthroughs. The exploration and development potential of shale oil is huge, and will be a significant oil and gas alternative resource in the Changqing Oilfield in the future, but its related geological fundamental research is relatively weak. Based on the observation, description and systematic sampling of the centimeter-level core of the full core section of Chang 7 Member in Well Ning228, a series of experimental test analyses and identification outcomes indicated that Chang 7 Member primarily developed four rock types: Black shale, dark mudstone, siltstone and tuff. The overall conditions of source rocks in the Chang 7 member are superior, the organic carbon content of shale is high, and the hydrocarbon generation potential is large. The organic matter types are mainly type Ⅰ and type Ⅱ, which are in the low mature-mature stage, and the black shale has higher organic matter abundance. The main reservoir spaces of the Chang 7 Member are primary pores and secondary pores. Mud shale primarily develops organic pores and inorganic pores. Fine sandstone mainly develops residual intergranular pores, intragranular pores and intergranular pores. Tuff mainly develops intergranular pores and mineral intragranular pores. The overall oil-bearing property of the Chang 7 Member is good, but the mobility difference is large. The fluid mobility in sandstone and tuff is good, and the mobility inshale is poor. In short, fine anatomical analysis of typical interlayer shale oil provides favorable guidance for promoting the large-scale development of shale oil in the Ordos Basin.

With the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies. Multi-agent deep reinforcement learning shines brightly in this type of team electronic game, achieving results that surpass professional human players. Reinforcement learning algorithms based on Q-value estimation often suffer from Q-value overestimation, which may seriously affect the performance of AI in multi-agent scenarios. We propose a multi-agent mutual evaluation method and a multi-agent softmax method to reduce the estimation bias of Q values in multi-agent scenarios, and have tested them in both the particle multi-agent environment and the multi-agent tank environment we constructed. The multi-agent tank environment we have built has achieved a good balance between experimental verification efficiency and multi-agent game task simulation. It can be easily extended for different multi-agent cooperation or competition tasks. We hope that it can be promoted in the research of multi-agent deep reinforcement learning.

Global pandemics such as COVID-19 have resulted in significant global social and economic disruption. Although polymerase chain reaction (PCR) is recommended as the standard test for identifying the SARS-CoV-2, conventional assays are time-consuming. In parallel, although artificial intelligence (AI) has been employed to contain the disease, the implementation of AI in PCR analytics, which may enhance the cognition of diagnostics, is quite rare. The information that the amplification curve reveals can reflect the dynamics of reactions. Here, we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices (µPADs) to detect synthetic RNA templates of the SARS-CoV-2 ORF1ab gene. The µPADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments. During analysis, real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks, including RNN, LSTM, and GRU. Of these, the GRU is found to be most effective and accurate. Based on the experimentally obtained datasets, qualitative forecasting can be made as early as 13 cycles, which significantly enhances the efficiency of the PCR tests by 67.5% (∼40 min). Also, an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles. To further improve PCR testing efficiency, we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers, which enables real-time quantitative analysis of PCR tests. The presented approach is the first to integrate AI for on-chip PCR data analysis. It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation. It is also capable of fully exploring the dynamics and intrinsic features of each reaction. This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field. It is universally applicable and can be extended to multiple areas of fundamental research.

This paper presents a compact stacked RF energy harvester operating in the WiFi band with multi-condition adaptive energy management circuits (MCA-EMCs). The harvester is divided into antennas, impedance matching networks, rectifiers, and MCA-EMCs. The antenna is based on a polytetrafluoroethylene (PTFE) substrate using the microstrip antenna structure and a ring slot in the ground plane to reduce the antenna area by 13.7%. The rectifier, impedance matching network, and MCA-EMC are made on a single FR4 substrate. The rectifier has a maximum conversion efficiency of 33.8% at 5 dBm input. The MCA-EMC has two operating modes to adapt to multiple operating conditions, in which Mode 1 outputs 1.5 V and has a higher energy conversion efficiency of up to 93.56%, and Mode 2 supports a minimum starting input voltage of 0.33 V and multiple output voltages of 2.85–2.45 V and 1.5 V. The proposed RF energy harvester is integrated by multiple-layer stacking with a total size of 53 mm × 43.5 mm × 5.9 mm. The test results show that the proposed RF energy harvester can drive a wall clock (30 cm in diameter) at 10 cm distance and a hygrometer at 122 cm distance with a home router as the transmitting source.

This paper presents a compact RF energy harvesting wireless sensor node with the antenna, rectifier, energy management circuits, and load integrated on a single printed circuit board and a total size of 53 mm × 59.77 mm × 4.5 mm. By etching rectangular slots in the radiation patch, the antenna area is reduced by 13.9%. The antenna is tested to have an S11 of −24.9 dB at 2.437 GHz and a maximum gain of 4.8 dBi. The rectifier has a maximum RF-to-DC conversion efficiency of 52.53% at 7 dBm input energy. The proposed WSN can achieve self-powered operation at a distance of 13.4 m from the transmitter source. To enhance the conversion efficiency under different input energy densities, this paper establishes an energy model for two operating modes and proposes an energy-intensity adaptive management algorithm. The experiments demonstrated that the proposed WSN can effectively distinguish between the two operating modes based on input energy intensity and realize efficient energy management.

Galaxy morphology is one of the most fundamental ways to describe galaxy properties, but the morphology we observe may be affected by wavelength and spatial resolution, which may introduce systematic bias when comparing galaxies at different redshift. Taking advantage of the broad wavelength coverage from optical to near-IR and the high-resolution NIRCam instrument of the JWST, we measure the nonparametric morphological parameters of a total of 1376 galaxies at z ≃ 0.8–3.0 in the CEERS field through an optimized code called statmorph _ csst . We divide our sample into three redshift intervals and investigate the wavelength and redshift dependence of the morphological parameters. We also explore how the widely used galaxy type classification methods based on the morphological parameters depend on wavelength and spatial resolution. We find that there are variations in all morphological parameters with rest-frame wavelength ( λ _rf ), especially at the short-wavelength end, and that λ _rf mainly affects the classification between late- and early-type galaxies. As λ _rf increases, the galaxies on the G – M _20 diagram move to the upper left with a slope of −0.23 ± 0.03 on average. We find that spatial resolution mainly affects the merger identification. The merger fraction in F200W resolution can be ≳2 times larger than that in F444W resolution. Furthermore, we compare the morphological parameter evolution of galaxies with different stellar masses. We find that there are differences in the morphological evolution of high- and low-mass (log M _* ≥ 10 and 9 < log M _* < 10) galaxies in the studied redshift range, which may be caused by their different evolutionary paths.

We select a disk-like galaxy sample with observations of the H I, H _2 , and dust from the Herschel Reference Survey, and derive inner H I masses within the optical radius. We find that the inner gas-to-dust ratio is almost independent of gas-phase metallicity, and confirm that the inner gas mass (H I+H _2 ) shows a tighter relation with dust mass and monochromatic 500 μ m luminosity than the integral gas mass. It supports that dust is more closely associated with cospatial cold gas than the overall cold gas. Based on the newly calibrated relationship between inner gas mass and dust mass, we predict dust masses for disk-dominated galaxies from the xCOLD GASS sample. The predicted dust masses show scaling relations consistent with fiducial ones in the literature, supporting their robustness. Additionally, we find that at a given dust mass and star formation rate (SFR), the galactic Wide-field Infrared Explorer W3 luminosities show a significant dependence on the [N ii ] luminosity and the stellar mass surface density. Such dependence highlights the caveat of using the W3 luminosity as an integral SFR indicator, and is consistent with findings of studies that target star-forming regions in more nearby galaxies and accurately derive dust masses based on mapping-mode spectroscopy.

AbstractShale oil is an important strategic resource in the future. The development and utilization of shale oil is a pivotal revolution in the field of fossil energy. The successful development in North America brings new thinking to the world. In recent years, there have been many definitions of shale oil, but no agreement has been reached in the petroleum industry. Marine shale oil is dominant in the United States (the U.S.), while continental shale oil is the counterpart in China. This study takes Uinta Basin, Ordos Basin, and Junggar Basin, for example, to make a comparison of the concept and geological characteristics of shale oil in continental basins between the U.S. and China. Different kinds of shale oil are generated due to the distinctions of sedimentary environments, hydrocarbon supply conditions, and sandy sediment development in different regions. High-quality source rocks have been developed in freshwater and salty water environments of continental lacustrine basins. The lithologies of the Uteland Butte Member, Chang 7 Member, and Lucaogou Formation are lacustrine carbonate rocks, clastic rocks, and mixed sedimentary rocks, respectively. Results indicate that the lower pressure can be identified in continental basins in China. Furthermore, the comparison shows that the Chang 7 Member characterizes lighter crude oil and larger scale of “sweet spot” and the Lucaogou Formation developed a thicker target layer, while the Chang 7 Member is at a disadvantage of insufficient stratum energy and the Lucaogou Formation is difficult in heavier crude oil and higher oil viscosity.

Discrete-time adaptive control for ionic polymer-metal composite (IPMC) actuator is studied in this paper. First, a new mathematical model in discrete-time domain is proposed for IPMC actuator. Then, based on the obtained model, a discrete adaptive control law is synthesized for IPMC actuators. The proposed discrete adaptive controller can guarantee the global stability of the closed-loop system, and the position tracking error of the IPMC actuator can be controlled by the design parameters. Finally, the proposed model and control law are verified by IPMC actuator experiments.

In this paper, a neural decentralized adaptive quantized dynamic surface control scheme is proposed for a class of large-scale multi-machine power systems with static var compensator (SVC) and unknown line-transmission time delays. The main contributions of the proposed method are summarized as follows: (1) a decentralized dynamic surface quantized control scheme with simple structure is proposed for the large-scale multi-machine systems with SVC, where the “explosion of complexity”problem in backstepping method and the complexities introduced by SVC are overcome; (2) the unknown line-transmission time delays between different generators are considered and dealt with by introducing the finite-cover lemma with radial basis function neural networks (RBFNNs) approximator, which leads to the arbitrarily small £∞a tracking performance; (3) the strong nonlinearities, uncertain parameters and external disturbances of the system are considered and the number of the estimated parameters is greatly reduced by estimating the weight vector norm of neural networks instead of estimating the weighted vector itself. It is proved that all the signals in the control system are ultimately uniformly boundedb and can be made arbitrarily small. Simulation results show the validity of the proposed method.

Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.

The output tracking problem for a class of uncertain strict-feedback nonlinear systems with unknown Duhem hysteresis input is investigated. In order to handle the undesirable effects caused by unknown hysteresis, the properties in respect to Duhem model are used to decompose it as a nonlinear smooth term and a nonlinear bounded “disturbance-like” term, which makes it possible to deal with the unknown hysteresis without constructing inverse in the controller design. By combining robust control and dynamic surface control technique, an adaptive controller is proposed in this paper to avoid “the explosion complexity” in the standard backstepping design procedure. The negative effects caused by the unknown hysteresis can be mitigated effectively, and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system is obtained. The effectiveness of the proposed scheme is validated through a simulation example.