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The Heshui area within the Ordos Basin holds significant strategic importance for the extraction and development of tight oil resources in the Changqing Oilfield. This study extensively explored the geochemical features and distribution tendencies of source rocks in the Chang 7 member, utilizing core samples and logging data for a comprehensive analysis. A more advanced model was utilized to predict the dispersion of Total Organic Carbon (TOC) in the Chang 7 member source rock. The properties and hydrocarbon generation potential of source rocks were thoroughly assessed through a comprehensive analysis that involved evaluating their total organic carbon content, pyrolysis parameters, and reflectance (Ro) values. The research concluded that the source rocks boast substantial organic matter, predominantly categorized as type II-I organic material. The thermal maturation levels span from low maturity to maturity, signifying significant potential for oil generation. Generally, the source rock quality falls within the range of good to excellent. Sedimentary patterns notably influence the distribution of hydrocarbon-source rocks. The northeastern sector of the study area is situated in an area characterized by deep to semi-deep lake sedimentation, making it the primary location for the presence of Chang 7 member hydrocarbon source rocks. With a thickness ranging from 40 to 70 m, this zone becomes a pivotal focus for the potential exploration of tight oil resources in the future. The results of this study offer crucial insights for understanding the geochemical characteristics of hydrocarbon source rocks, evaluating their potential for hydrocarbon generation, and forecasting favorable zones for oil and gas exploration in similar regions. [ABSTRACT FROM AUTHOR]

Partially Premixed Compression Ignition (PPCI) and Multiple Premixed Compression Ignition (MPCI) are two high efficiency and clean combustion strategies. This paper presents an experimental investigation on the efficiency of PPCI and MPCI combustion using five gasoline-like fuels: gasoline, gasoline/ethylhexyl-nitrate (EHN) blends, gasoline/diesel blends, gasoline/polyoxymethylene dimethyl ethers (PODEn) blends, gasoline/diesel/PODEn blends, denoted as G, GE, GD, GP, GDP, respectively. The study was conducted using a single cylinder engine that was retrofitted from a production Euro IV diesel engine. The results showed that in PPCI mode, the order of thermal efficiency from the highest to the lowest was GP, G, GDP, GE, and GD, while the order in MPCI mode was GP, GDP, GE, G, and GD. The thermal efficiency of MPCI mode was higher than that of PPCI mode, except for GP, whose thermal efficiency was the highest of about 48.8% for both modes due to the early CA50 and short combustion duration. In PPCI mode, NOx and soot emissions were below 0.4 g/kWh and 0.01 g/kWh, respectively, satisfying the Euro VI emission standard. NOx emission in MPCI mode can meet the requirement of Euro VI. However, the soot emission exceeded the limit because the second stage combustion was partially diffusion combustion, implying an extra Gasoline Particle Filter (GPF) was needed to meet the Euro VI standard. This study indicates that PPCI injection strategy alone and MPCI injection strategy with GPF are two potential ways of retrofitting a Euro IV engine to meet the Euro VI emission standard. GPF increases manufacturing cost, but higher thermal efficiency in MPCI mode could reduce the total cost of ownership due to the lower fuel consumption. [ABSTRACT FROM AUTHOR]

Urban–rural integration is a complex process in the giant urban–rural system of the Beijing suburbs with various evolutions in modern suburban functions. Therefore, the functions and processes of the whole Beijing suburb system cannot be treated in isolation. Based on the theory of generalised space proposed by Chen (2008), the urban–rural integration process in the Beijing suburbs was analysed in real space (R-space), phase space (P-space) and order space (O-space). The results show that the macro urban–rural gap is the dominant factor in the O-space. A multifunctional policy at the meso-level will promote the scientific evolution of the P-space. These factors in turn influence the evolution of the urban–rural integration in Beijing suburbs at various levels in the R-space. Finally, new features of spatial organisation, for example, fractals, are generalised from the interactions of those three spaces. Therefore, the perspective of the R-P-O space fusion can provide theoretical guidance to investigators for the urban–rural integration of Beijing suburbs. [ABSTRACT FROM AUTHOR]

With the current spatial planning reform in China, public participation is becoming increasingly important in the success of rural spatial planning. However, engaging various stakeholders in spatial planning projects is difficult, mainly due to the lack of planning knowledge and computer skills. Therefore, this paper discusses the development of a virtual globe-based 3D participatory geographic information system (PGIS) aiming to support public participation in the spatial planning process. The 3D PGIS-based rural planning approach was applied in the village of XiaFan, Ningbo, China. The results demonstrate that locals' participation capacity was highly promoted, with their interest in 3D PGIS visualization being highly activated. The interactive landscape design tools allow stakeholders to present their own suggestions and designs, just like playing a computer game, thus improving their interactive planning abilities on-site. The scientific analysis tools allow planners to analyze and evaluate planning scenarios in different disciplines in real-time to quickly respond to suggestions from participants on-site. Functions and tools such as data management, marking, and highlighting were found to be useful for smoothing the interactions among planners and participants. In conclusion, virtual globe-based 3D PGIS highly supports the participatory rural landscape planning process and is potentially applicable to other regions. [ABSTRACT FROM AUTHOR]

Analyzing the petrophysical properties of gas hydrate reservoirs under different conditions by experimental methods is time-consuming, costly, and very difficult. Therefore, it is of great practical significance to accurately reconstruct gas hydrate digital rocks using numerical simulation methods. In this study, based on in situ observations of gas hydrate pore habits and variation laws in the laboratory, a hybrid method was adopted to simulate pore-scale spatial distribution characteristics of gas hydrate using a digital rock model. The hybrid method combined classical nucleation theory, the diffusion-limited aggregation model, and the cluster-cluster aggregation model. Then, the pore size distribution, local porosity distribution, and average seepage probability distribution of gas hydrate digital rock and gas hydrate–bearing sediments were calculated and compared. In addition, the finite-element method was used to simulate and compare the reconstructed digital rock's resistivity index and elastic modulus. The results showed that the hybrid method can effectively simulate the spatial distribution characteristics and variation laws of gas hydrate on the pore scale. In addition, the resistivity index and elastic modulus of the reconstructed gas hydrate digital rock were relatively close to those of gas hydrate–bearing sediments. However, compared with gas hydrate–bearing sediments, the average pore size of the reconstructed gas hydrate digital rock was smaller, and the pore connectivity showed a slight amount of difference. In summary, this investigation was of great significance in systematically studying the petrophysical properties of gas hydrate reservoirs. [ABSTRACT FROM AUTHOR]

In recent years, wild morel mushroom species have begun to be widely cultivated in China due to their high edible and medicinal values. To parse the medicinal ingredients, we employed the technique of liquid-submerged fermentation to investigate the secondary metabolites of Morehella importuna. Two new natural isobenzofuranone derivatives (1–2) and one new orsellinaldehyde derivative (3), together with seven known compounds, including one o-orsellinaldehyde (4), phenylacetic acid (5), benzoic acid (6), 4-hydroxy-phenylacetic acid (7), 3,5-dihydroxybenzoic acid (8), N,N′-pentane-1,5-diyldiacetamide (9), and 1H-pyrrole-2-carboxylic acid (10), were obtained from the fermented broth of M. importuna. Their structures were determined according to the data of NMR, HR Q-TOF MS, IR, UV, optical activity, and single-crystal X-ray crystallography. TLC-bioautography displayed that these compounds possess significant antioxidant activity with the half DPPH free radical scavenging concentration of 1.79 (1), 4.10 (2), 4.28 (4), 2.45 (5), 4.40 (7), 1.73 (8), and 6.00 (10) mM. The experimental results would shed light on the medicinal value of M. importuna for its abundant antioxidants. [ABSTRACT FROM AUTHOR]

Accurate monitoring of bare soil land (BSL) is an urgent need for environmental governance and optimal utilization of land resources. High-resolution imagery contains rich semantic information, which is beneficial for the recognition of objects on the ground. Simultaneously, it is susceptible to the impact of its background. We propose a semantic segmentation model, Deeplabv3+-M-CBAM, for extracting BSL. First, we replaced the Xception of Deeplabv3+ with MobileNetV2 as the backbone network to reduce the number of parameters. Second, to distinguish BSL from the background, we employed the convolutional block attention module (CBAM) via a combination of channel attention and spatial attention. For model training, we built a BSL dataset based on BJ-2 satellite images. The test result for the F1 of the model was 88.42%. Compared with Deeplabv3+, the classification accuracy improved by 8.52%, and the segmentation speed was 2.34 times faster. In addition, compared with the visual interpretation, the extraction speed improved by 11.5 times. In order to verify the transferable performance of the model, Jilin-1GXA images were used for the transfer test, and the extraction accuracies for F1, IoU, recall and precision were 86.07%, 87.88%, 87.00% and 95.80%, respectively. All of these experiments show that Deeplabv3+-M-CBAM achieved efficient and accurate extraction results and a well transferable performance for BSL. The methodology proposed in this study exhibits its application value for the refinement of environmental governance and the surveillance of land use. [ABSTRACT FROM AUTHOR]

Accurate mapping of dams can provide useful information about geographical locations and boundaries and can help improve public dam datasets. However, when applied to disaster emergency management, it is often difficult to completely determine the distribution of dams due to the incompleteness of the available data. Thus, we propose an automatic and intelligent extraction method that combines location with post-segmentation for dam detection. First, we constructed a dataset named RSDams and proposed an object detection model, YOLOv5s-ViT-BiFPN (You Only Look Once version 5s-Vision Transformer-Bi-Directional Feature Pyramid Network), with a training method using deep transfer learning to generate graphical locations for dams. After retraining the model on the RSDams dataset, its precision for dam detection reached 88.2% and showed a 3.4% improvement over learning from scratch. Second, based on the graphical locations, we utilized an improved Morphological Building Index (MBI) algorithm for dam segmentation to derive dam masks. The average overall accuracy and Kappa coefficient of the model applied to 100 images reached 97.4% and 0.7, respectively. Finally, we applied the dam extraction method to two study areas, namely, Yangbi County of Yunnan Province and Changping District of Beijing in China, and the recall rates reached 69.2% and 81.5%, respectively. The results show that our method has high accuracy and good potential to serve as an automatic and intelligent method for the establishment of a public dam dataset on a regional or national scale. [ABSTRACT FROM AUTHOR]

Efficiently and automatically acquiring information on earthquake damage through remote sensing has posed great challenges because the classical methods of detecting houses damaged by destructive earthquakes are often both time consuming and low in accuracy. A series of deep-learning-based techniques have been developed and recent studies have demonstrated their high intelligence for automatic target extraction for natural and remote sensing images. For the detection of small artificial targets, current studies show that You Only Look Once (YOLO) has a good performance in aerial and Unmanned Aerial Vehicle (UAV) images. However, less work has been conducted on the extraction of damaged houses. In this study, we propose a YOLOv5s-ViT-BiFPN-based neural network for the detection of rural houses. Specifically, to enhance the feature information of damaged houses from the global information of the feature map, we introduce the Vision Transformer into the feature extraction network. Furthermore, regarding the scale differences for damaged houses in UAV images due to the changes in flying height, we apply the Bi-Directional Feature Pyramid Network (BiFPN) for multi-scale feature fusion to aggregate features with different resolutions and test the model. We took the 2021 Yangbi earthquake with a surface wave magnitude (Ms) of 6.4 in Yunan, China, as an example; the results show that the proposed model presents a better performance, with the average precision (AP) being increased by 9.31% and 1.23% compared to YOLOv3 and YOLOv5s, respectively, and a detection speed of 80 FPS, which is 2.96 times faster than YOLOv3. In addition, the transferability test for five other areas showed that the average accuracy was 91.23% and the total processing time was 4 min, while 100 min were needed for professional visual interpreters. The experimental results demonstrate that the YOLOv5s-ViT-BiFPN model can automatically detect damaged rural houses due to destructive earthquakes in UAV images with a good performance in terms of accuracy and timeliness, as well as being robust and transferable. [ABSTRACT FROM AUTHOR]

Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores' contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T2) curves were synthesized artificially. This task was done by dividing the area under the T2 curve into eight porosity bins and estimating each bin's value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T2 curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs. [ABSTRACT FROM AUTHOR]

As a new satellite sensor of the GaoFen (GF) series, GF-6 Wide Field of View (WFV) with the resolution of 16 m has the characteristics of wide coverage, high-frequency imaging and has four new bands of two red-edge, yellow, and purple compared with GF-1 WFV. In order to test the validity of the supplementary bands of GF-6WFV data for change detection of land use/land cover (LULC), this study applied the Double-constrained Change Detection Method (DCDM) that uses the double constraints (change vector intensity and correlation coefficient) for change detection on object-level. According to two GF-6WFV imageries acquired in the Xiong'an New Area in June of 2018 and 2019, feature analysis was performed to determine whether the new bands are helpful to detect the change of LULC first. Then, by coupling these selected features, the intensity of change vector and correlation coefficient were used as the double constraints to perform the change detection. The study demonstrates that the relevant features of the two red-edge bands can achieve the overall accuracy of 89% for change detection of LULC and improved by 2% comparing with using the corresponding temporal GF-1WFV data, while the purple and yellow bands cannot provide enough effective information for this detection. This study can provide theoretical support for the in-depth applications of GF-6 WFV data products in the change detection fields and has explored its applicability and potential in resource and environment monitoring, it is helpful to the further applications. [ABSTRACT FROM AUTHOR]

Effective extraction of disaster information of buildings from remote sensing images is of great importance to supporting disaster relief and casualty reduction. In high-resolution remote sensing images, object-oriented methods present problems such as unsatisfactory image segmentation and difficult feature selection, which makes it difficult to quickly assess the damage sustained by groups of buildings. In this context, this paper proposed an improved Convolution Neural Network (CNN) Inception V3 architecture combining remote sensing images and block vector data to evaluate the damage degree of groups of buildings in post-earthquake remote sensing images. By using CNN, the best features can be automatically selected, solving the problem of difficult feature selection. Moreover, block boundaries can form a meaningful boundary for groups of buildings, which can effectively replace image segmentation and avoid its fragmentary and unsatisfactory results. By adding Separate and Combination layers, our method improves the Inception V3 network for easier processing of large remote sensing images. The method was tested by the classification of damaged groups of buildings in 0.5 m-resolution aerial imagery after the earthquake of Yushu. The test accuracy was 90.07% with a Kappa Coefficient of 0.81, and, compared with the traditional multi-feature machine learning classifier constructed by artificial feature extraction, this represented an improvement of 18% in accuracy. Our results showed that this improved method could effectively extract the damage degree of groups of buildings in each block in post-earthquake remote sensing images. [ABSTRACT FROM AUTHOR]