Search

This article offers a comprehensive review focused on the analysis and characterization of shale reservoirs, unconventional hydrocarbon resources that uniquely serve as both the source reservoir for gas and oil, and the rock. To analyze and characterize shale reservoirs, pore structure, rock components and rock mechanical properties are three main factors to analyze. These three main factors are necessary for successful field operations in shale reservoirs. Until now, there are various techniques utilized to characterize these three properties. Therefore, this study delves into the intricacies of shale reservoir's pore structure, rock components and mechanical properties under varying geological conditions, summarizing various techniques utilized to characterize these properties in previous studies. The study also discusses the role of analytical techniques in understanding the complex interactions between kerogen and the surrounding mineral matrix. By providing a summary of various techniques operated on the mentioned three main factors, this paper supplies the effective and optimal technique on analyzing different properties of shale reservoirs. Furthermore, the paper aims to contribute to more effective resource assessment and production optimization in shale reservoirs, offering insights that have significant implications for the future of unconventional hydrocarbon extraction.

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat lodging images were collected using the unmanned aerial vehicle (UAV) remote sensing platform. Based on these images, winter wheat lodging datasets were created using different time sequences and different UAV flight heights. These datasets aid in segmenting and classifying winter wheat lodging degrees and areas. The results show that MLP_U-Net has demonstrated superior detection performance in a small sample dataset. The accuracies of winter wheat lodging degree and lodging area grading were 96.1% and 92.2%, respectively, when the UAV flight height was 30 m. For a UAV flight height of 50 m, the accuracies of winter wheat lodging degree and lodging area grading were 84.1% and 84.7%, respectively. These findings indicate that MLP_U-Net is highly robust and efficient in accurately completing the winter wheat lodging-grading task. This valuable insight provides technical references for UAV remote sensing of winter wheat disaster severity and the assessment of losses.

Abstract Seizures due to cortical dysplasia are notorious for their poor prognosis even with medications and surgery, likely due to the widespread seizure network. Previous studies have primarily focused on the disruption of dysplastic lesions, rather than remote regions such as the hippocampus. Here, we first quantified the epileptogenicity of the hippocampus in patients with late-stage cortical dysplasia. We further investigated the cellular substrates leading to the epileptic hippocampus, using multiscale tools including calcium imaging, optogenetics, immunohistochemistry and electrophysiology. For the first time, we revealed the role of hippocampal somatostatin-positive interneurons in cortical dysplasia-related seizures. Somatostatin-positive were recruited during cortical dysplasia-related seizures. Interestingly, optogenetic studies suggested that somatostatin-positive interneurons paradoxically facilitated seizure generalization. By contrast, parvalbumin-positive interneurons retained an inhibitory role as in controls. Electrophysiological recordings and immunohistochemical studies revealed glutamate-mediated excitatory transmission from somatostatin-positive interneurons in the dentate gyrus. Taken together, our study reveals a novel role of excitatory somatostatin-positive neurons in the seizure network and brings new insights into the cellular basis of cortical dysplasia.

Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (Hdsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R2, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the Hdsm, are 0.58, 4528.23 kg/hm2, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm2) is slightly smaller than the measured mean AGB (16,960.23 kg/hm2). (2) The R2, RMSE, and RPD of the improved AGB estimation model, based on AGB/Hdsm, are 0.88, 2291.90 kg/hm2, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm2) is more similar to the measured mean AGB (17,222.59 kg/hm2). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the Hdsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

Abstract Shale oil resources are usually developed by tightly spaced horizontal wells that are hydraulically fractured. Infill wells are often used to develop the zones not thoroughly produced by previously fractured parent wells, and determining the parent well spacing has become a key issue in the field as it affects the overall fracturing and production efficiency. This study employs a coupled flow and geomechanics model to investigate the poroelastic changes induced by parent well depletion in the infill zone. Based on poromechanics, the temporal and spatial changes in pore pressure, minimum horizontal principal stress, breakdown pressure, and maximum horizontal principal stress orientation are quantified. Pore pressure is used to estimate the remaining hydrocarbon exploitation potential in the infill zone, while minimum horizontal principal stress, breakdown pressure, and maximum horizontal principal stress orientation are used to estimate the difficulties of establishing hydraulic fracture networks in the infill zone. Results show that increasing parent well spacing helps to maintain the pore pressure in the infill zone, while the difficulties of initiating hydraulic fracture networks in the infill zone are slightly increased. Tight well spacing intensifies the infill zone depletion and lowers the infill well development potential, while the breakdown pressure is decreased which can facilitate future infill well fracturing. The poroelastic effect of parent well spacings on the infill zone is not monotonic.

In view of the fact that the current peanut test was still artificial, it was difficult to meet the actual demand of peanut test, this paper established a peanut high-throughput automatic test system (automatic measurement), which improves the work efficiency of peanut test. The system mainly included peanut automatic seed testing device, scanning gun, industrial computer, multi-serial controller and peanut automatic seed testing system, which could obtain the automation of the whole process of peanut pod testing and kernel fruit testing in real time. The results showed that the system can measure the peanut pods and nuts and other parameters in real time, and the average measuring accuracy of the length, width, diameter and quantity of the peanut pod and kernel was more than 98%. Compared with the manual test, the automatic measurement can significantly improved the test efficiency. The system was also suitable for the acquisition of other crop test parameters and provides a reference for high-throughput automatic test.

In wheat breeding, spike number is a key indicator for evaluating wheat yield, and the timely and accurate acquisition of wheat spike number is of great practical significance for yield prediction. In actual production; the method of using an artificial field survey to count wheat spikes is time-consuming and labor-intensive. Therefore, this paper proposes a method based on YOLOv5s with an improved attention mechanism, which can accurately detect the number of small-scale wheat spikes and better solve the problems of occlusion and cross-overlapping of the wheat spikes. This method introduces an efficient channel attention module (ECA) in the C3 module of the backbone structure of the YOLOv5s network model; at the same time, the global attention mechanism module (GAM) is inserted between the neck structure and the head structure; the attention mechanism can be more Effectively extract feature information and suppress useless information. The result shows that the accuracy of the improved YOLOv5s model reached 71.61% in the task of wheat spike number, which was 4.95% higher than that of the standard YOLOv5s model and had higher counting accuracy. The improved YOLOv5s and YOLOv5m have similar parameters, while RMSE and MEA are reduced by 7.62 and 6.47, respectively, and the performance is better than YOLOv5l. Therefore, the improved YOLOv5s method improves its applicability in complex field environments and provides a technical reference for the automatic identification of wheat spike numbers and yield estimation. Labeled images, source code, and trained models are available at: https://github.com/228384274/improved-yolov5.

The Fengcheng Formation of the Mahu Sag is an unconventional reservoir that is of paramount importance for exploration and development of hydrocarbon resource. However, current research on natural fractures in the Fengcheng Formation remains limited, posing challenges for exploration of hydrocarbon resource in the region. This study is based on core observations, thin section identification, geochemical testing and the evolution of regional tectonic movements to investigate the characteristics and periods of formation of natural fractures to address this gap. According to the characteristics of natural fractures in the drilling core samples and microsections, the natural fractures in the Fengcheng Formation can be grouped into structural fractures and atectonic fractures. Structural fractures can be further divided into three subtypes: high-angle interlayer shear fractures, along-layer shear fractures, and tensile fractures. Additionally, non-tectonic fractures in this studied area are primarily bedding fractures, hydraulic fractures, and hydrocarbon-generating overpressure fractures. Vertically, fracture development is more prominent at the bottom of Feng #2 Formation and at the top of Feng #3 Formation. Results also indicate that natural fractures primarily formed during three distinct tectonic movement periods. The initial stage of fracture evolution pertains to the Late Permian period (243–266 Ma), filled with fibrous calcite, and exhibiting a uniform temperature of 70–100 °C. The second stage of fracture evolution occurred during the Late Indosinian to Early Yanshanian period (181–208 Ma), mostly filled or semi-filled with calcite, with a uniform temperature of 110–130 °C. The third stage of fracture development happened during the late Yanshanian to early Himalaya period (50–87 Ma), predominantly filled with calcite, and presenting a uniform temperature of 130–150 °C. Among the various types of structural fractures, the density of high-angle interlayer shear fractures demonstrates a positive correlation with daily gas production, indicating their vital role in promoting hydrocarbon resource production and transportation. Furthermore, microfractures generated by hydrocarbon-generating overpressure fractures exhibit small pore sizes and strong connectivity. These microfractures can create an effective permeability system by connecting previously isolated micropores in shale reservoirs, thus establishing interconnected pore spaces in the shale formation.

Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method’s effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments.

As a non-destructive, efficient and non-invasive detection method, nuclear magnetic resonance (NMR) technology has always played an important role in the analysis of oil and gas cores, but the traditional nuclear magnetic resonance detection results (T2 spectrum) only reflect the size distribution characteristics of rock pores, while the connectivity of pores cannot be directly characterized. Through the improvement of experimental procedures and data processing methods of the standard nuclear magnetic T2 spectrum, the capillary pressure dimension is added in to obtain the T2-Pc two-dimensional nuclear magnetic experiment spectrum, which solves the problem that the nuclear magnetic experiment cannot reflect the pore connectivity. Practical application shows that the T2-Pc two-dimensional nuclear magnetic experiment can not only intuitively evaluate reservoir connectivity, but also obtain bound water saturation under different production pressure differentials, thus providing more reservoir information for oil and gas exploration and development.

Abstract Background Senile osteoporosis with age-related bone loss is diagnosed depending on radiographic changes of bone and bone mineral density (BMD) measurement. However, radiographic alterations are usually signs of medium-late stage osteoporosis. Therefore, biomarkers have been proposed as indicators of bone loss. In the current study, Galectin-1 (Gal-1) showed age-related decline in mice serum. The role of Gal-1 in osteoporosis has not been investigated so far. Hence, the current study illustrated the relationship of serum Gal-1 level with bone loss. Methods We employed 6- and 18-month-old mice to establish an animal model of age-related trabecular bone loss, whose bone density and microstructure were investigated by micro-CT. ELISA was used to measure the levels of Gal-1 in serum. The correlation analysis was performed to illustrate the relationship between serum Gal-1 levels and trabecular bone loss. In addition, immunohistochemistry was used to investigate the abundance of Gal-1 in bone marrow of mice. ELISA and western blot were performed to measure the secretion ability and protein expression of Gal-1 in bone marrow stromal cells (BMSC), hematopoietic stem cells (HSC) and myeloid progenitor (MP) respectively. Flow cytometry was used to measure BMSC number in bone marrow. Finally, male volunteers with age-related BMD decrease were recruited and the relationship between serum Gal-1 and BMD was analyzed. Results Gal-1 showed age-related decline in mice serum. Serum Gal-1 was positively associated with BV/TV of femur, tibia and L1 vertebrae in mice. BMSC secreted more Gal-1 compared with HSC and MP. BMSC number in bone marrow was significantly lower in aged mice compared with young mice. Significant attenuation of Gal-1 protein expression was observed in BMSC and HSC from aged mice compared with young mice. Further, we found a decline in serum Gal-1 levels in men with age-related BMD decrease. There was positive correlation between BMD and serum Gal-1 levels in these men. Conclusions Age-related trabecular bone loss is associated with a decline in serum Gal-1 level in mice and men. Our study suggested Gal-1 had great potential to be a biomarker for discovering BMSC senescence, diagnosing early osteoporosis and monitoring trabecular bone loss.

The fractal theory has been widely applied to the analysis of gas adsorption isotherms, which are used for the pore structure characterization in unconventional reservoirs. Fractal dimension is a key parameter that can indicate the complexity of the pore structures. So far, most fractal models for gas adsorption are for N2 adsorption, while fractal models for CO2 adsorption are rarely reported. In this paper, we built a fractal model for CO2 adsorption by combining a thermodynamic model and the Dubinin–Astakhov model. We then applied the new model to three CO2 adsorption isotherms measured on shale samples. The results show that the fractal dimension from the new model lies between 2 and 3, which agrees with the fractal geometry. The new model presented in this paper can be used for the CO2 adsorption analysis, which allows characterizing micropore structures in shales.

Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (GΓD) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.

UAV imaging provides an efficient and non-destructive tool for characterizing farm information, but the quality of the UAV model is often affected by the image’s spatial resolution. In this paper, the predictability of models established using UAV multispectral images with different spatial resolutions for nitrogen content of winter wheat was evaluated during the critical growth stages of winter wheat over the period 2021–2022. Feature selection based on UAV image reflectance, vegetation indices, and texture was conducted using the competitive adaptive reweighted sampling, and the random forest machine learning method was used to construct the prediction model with the optimized features. Results showed that model performance increased with decreasing image spatial resolution with a R2, a RMSE, a MAE and a RPD of 0.84, 4.57 g m−2, 2.50 g m−2 and 2.34 (0.01 m spatial resolution image), 0.86, 4.15 g m−2, 2.82 g m−2 and 2.65 (0.02 m), and 0.92, 3.17 g m−2, 2.45 g m−2 and 2.86 (0.05 m), respectively. Further, the transferability of models differed when applied to images with coarser (upscaling) or finer (downscaling) resolutions. For upscaling, the model established with the 0.01 m images had a R2 of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively. For downscaling, the model established with the 0.05 m image features had a R2 of 0.86 and 0.83 when applied to images of 0.01 m and 0.02 m resolutions. Though the image spatial resolution affects image texture features more than the spectral features and the effects of image spatial resolution on model performance and transferability decrease with increasing plant wetness under irrigation treatment, it can be concluded that all the UAV images acquired in this study with different resolutions could achieve good predictions and transferability of the nitrogen content of winter wheat plants.

Abstract Background As a common complication of diabetes, the incidence of diabetic peripheral neuropathy (DPN) is 60–70% worldwide. DPN is a major risk factor for diabetic foot, which may lead to foot ulceration and even amputation. The treatment of DPN remains challenging. Our preliminary study demonstrated that the external application of Tangbi Waixi (TW) decoction to the lower extremities relieved clinical symptoms and improved nerve conduction velocity in DPN patients. The aim of this study was to verify the efficacy of TW among DPN patients and evaluate the herb mixture’s safety using rigorous methodological designs. Methods/design This study is a multicenter, double-blind, randomized controlled trial. A total of 640 DPN patients will be recruited and randomized to receive a foot bath with either the TW decoction or control drug. Participants will be assessed at baseline and 12 and 24 weeks after treatment. The primary outcome was the change of the Toronto Clinical Scoring System (TCSS). Secondary outcomes were nerve conduction velocity, blood glucose, blood lipids, serum inflammatory cytokines, and the European Quality of Life Five-Dimension Scale (EQ-5D) and TCM symptom scores. Discussion This multicenter, prospective, randomized clinical trial will provide valuable data regarding the efficacy and safety of foot bath treatment with TW decoction. Positive results would provide a novel treatment regimen for DPN patients. Trial registration Chinese Clinical Trial Registry, ChiCTR-IOR-16009331. Registered on 8 October 2016.

In the present study, we aim to elucidate the roles of caveolin-1(Cav-1), a 22 kDa protein in plasma membrane invaginations, in modulating neuronal differentiation of neural progenitor cells (NPCs). In the hippocampal dentate gyrus, we found that Cav-1 knockout mice revealed remarkably higher levels of vascular endothelial growth factor (VEGF) and the more abundant formation of newborn neurons than wild type mice. We then studied the potential mechanisms of Cav-1 in modulating VEGF signaling and neuronal differentiation in isolated cultured NPCs under normoxic and hypoxic conditions. Hypoxic embryonic rat NPCs were exposed to 1% O₂ for 24 h and then switched to 21% O₂ for 1, 3, 7 and 14 days whereas normoxic NPCs were continuously cultured with 21% O₂. Compared with normoxic NPCs, hypoxic NPCs had down-regulated expression of Cav-1 and up-regulated VEGF expression and p44/42MAPK phosphorylation, and enhanced neuronal differentiation. We further studied the roles of Cav-1 in inhibiting neuronal differentiation by using Cav-1 scaffolding domain peptide and Cav-1-specific small interfering RNA. In both normoxic and hypoxic NPCs, Cav-1 peptide markedly down-regulated the expressions of VEGF and flk1, decreased the phosphorylations of p44/42MAPK, Akt and Stat3, and inhibited neuronal differentiation, whereas the knockdown of Cav-1 promoted the expression of VEGF, phosphorylations of p44/42MAPK, Akt and Stat3, and stimulated neuronal differentiation. Moreover, the enhanced phosphorylations of p44/42MAPK, Akt and Stat3, and neuronal differentiation were abolished by co-treatment of VEGF inhibitor V1. These results provide strong evidence to prove that Cav-1 can inhibit neuronal differentiation via down-regulations of VEGF, p44/42MAPK, Akt and Stat3 signaling pathways, and that VEGF signaling is a crucial target of Cav-1. The hypoxia-induced down-regulation of Cav-1 contributes to enhanced neuronal differentiation in NPCs.