Impact of salinity of fracturing fluid on the migration of coal fines in propped fractures and cleats

During the hydraulic fracturing of coalbed methane (CBM) well, the deposition of coal fines in propped fractures and the migration of coal fines in cleats will damage the permeabilities of propped fractures and cleats, consequently affecting the hydraulic fracturing and the subsequent drainage of CBM well. For the purpose of dredging propped fractures and avoiding the clogging of cleats effectively, a novel method for the control of coal fines in propped fractures and cleats during hydraulic fracturing was proposed by optimizing the salinity of fracturing fluid. With the salinity decreasing stepwise, the experiments on the migration of coal fines in propped fractures and cleats were conducted on quartz sand-packed columns and anthracite coal plugs, respectively, to investigate the response characteristics of the migration of coal fines to the change of salinity. Additionally, the migration of coal fines was simulated by using the extended DLVO method, to elucidate the influence mechanisms of salinity on the migration of coal fines. On this basis, the optimal salinity range that takes into account the control of coal fines in propped fractures and cleats was explored. The results indicated that there existed a critical salt concentration (CSC) for the migration of coal fines in both propped fractures and cleats. When the salinity was lower than the CSC, the permeability of propped fractures abruptly increased while that of cleats decreased sharply, accompanied by a large amount of coal fines produced. The value of the CSC for the migration of coal fines in propped fractures was higher than that in cleats, which can be attributed to the fact that the surface electronegativity of proppants was stronger than that of cleats, while the hydrophobicity was weaker than that of cleats. With the gradual decrease of salinity, the electric double layer (EDL) repulsive force between coal fines and channel increased continuously. When the salinity decreased to the CSC, the EDL repulsion started to be greater than the sum of Lifshitz-van der Waals attraction and Lewis acid-base attraction, resulting in the migration of coal fines. Both the values of the predicted CSCs for the migration of coal fines in propped fractures and cleats were consistent with experimental data, indicating the effectiveness of the model. During hydraulic fracturing, the salinity of fracturing fluid can be designed between the CSCs for the migration of coal fines in propped fractures and cleats. In that case, the production of coal fines in propped fractures is promoted while the migration of coal fines is inhibited in cleats, so as to achieve the dual purposes of coal fines control in propped fractures and cleats

Wafer-scale arrayed p-n junctions based on few-layer epitaxial GaTe

Two-dimensional (2D) materials have attracted substantial attention in electronic and optoelectronic applications with superior advantages of being flexible, transparent and highly tunable. Gapless graphene exhibits ultra-broadband and fast photoresponse while the 2D semiconducting MoS2 and GaTe unveil high sensitivity and tunable responsivity to visible light. However, the device yield and the repeatability call for a further improvement of the 2D materials to render large-scale uniformity. Here we report a layer-by-layer growth of wafer-scale GaTe with a hole mobility of 28.4 cm2/Vs by molecular beam epitaxy. The arrayed p-n junctions were developed by growing few-layer GaTe directly on three-inch Si wafers. The resultant diodes reveal good rectifying characteristics, photoresponse with a maximum photoresponsivity of 2.74 A/W and a high photovoltaic external quantum efficiency up to 62%. The photocurrent reaches saturation fast enough to capture a time constant of 22 {\mu}s and shows no sign of device degradation after 1.37 million cycles of operation. Most strikingly, such high performance has been achieved across the entire wafer, making the volume production of devices accessible. Finally, several photo-images were acquired by the GaTe/Si photodiodes with a reasonable contrast and spatial resolution, demonstrating for the first time the potential of integrating the 2D materials with the silicon technology for novel optoelectronic devices

Crustal Azimuthal Anisotropy Beneath the Central North China Craton Revealed by Receiver Functions

To characterize crustal anisotropy beneath the central North China Craton (CNCC), we apply a recently developed deconvolution approach to effectively remove near-surface reverberations in the receiver functions recorded at 200 broadband seismic stations and subsequently determine the fast orientation and the magnitude of crustal azimuthal anisotropy by fitting the sinusoidal moveout of the P to S converted phases from the Moho and intracrustal discontinuities. The magnitude of crustal anisotropy is found to range from 0.06 s to 0.54Â s, with an average of 0.25 ± 0.08Â s. Fault-parallel anisotropy in the seismically active Zhangjiakou-Penglai Fault Zone is significant and could be related to fluid-filled fractures. Historical strong earthquakes mainly occurred in the fault zone segments with significant crustal anisotropy, suggesting that the measured crustal anisotropy is closely related to the degree of crustal deformation. The observed spatial distribution of crustal anisotropy suggests that the northwestern terminus of the fault zone probably ends at about 114°E. Also observed is a sharp contrast in the fast orientations between the western and eastern Yanshan Uplifts separated by the North-South Gravity Lineament. The NW-SE trending anisotropy in the western Yanshan Uplift is attributable to fossil crustal anisotropy due to lithospheric extension of the CNCC, while extensional fluid-saturated microcracks induced by regional compressive stress are responsible for the observed ENE-WSW trending anisotropy in the eastern Yanshan Uplift. Comparison of crustal anisotropy measurements and previously determined upper mantle anisotropy implies that the degree of crust-mantle coupling in the CNCC varies spatially

Association of maternal prenatal copper concentration with gestational duration and preterm birth: a multicountry meta-analysis

Background Copper (Cu), an essential trace mineral regulating multiple actions of inflammation and oxidative stress, has been implicated in risk for preterm birth (PTB). Objectives This study aimed to determine the association of maternal Cu concentration during pregnancy with PTB risk and gestational duration in a large multicohort study including diverse populations. Methods Maternal plasma or serum samples of 10,449 singleton live births were obtained from 18 geographically diverse study cohorts. Maternal Cu concentrations were determined using inductively coupled plasma mass spectrometry. The associations of maternal Cu with PTB and gestational duration were analyzed using logistic and linear regressions for each cohort. The estimates were then combined using meta-analysis. Associations between maternal Cu and acute-phase reactants (APRs) and infection status were analyzed in 1239 samples from the Malawi cohort. Results The maternal prenatal Cu concentration in our study samples followed normal distribution with mean of 1.92 μg/mL and standard deviation of 0.43 μg/mL, and Cu concentrations increased with gestational age up to 20 wk. The random-effect meta-analysis across 18 cohorts revealed that 1 μg/mL increase in maternal Cu concentration was associated with higher risk of PTB with odds ratio of 1.30 (95% confidence interval [CI]: 1.08, 1.57) and shorter gestational duration of 1.64 d (95% CI: 0.56, 2.73). In the Malawi cohort, higher maternal Cu concentration, concentrations of multiple APRs, and infections (malaria and HIV) were correlated and associated with greater risk of PTB and shorter gestational duration. Conclusions Our study supports robust negative association between maternal Cu and gestational duration and positive association with risk for PTB. Cu concentration was strongly correlated with APRs and infection status suggesting its potential role in inflammation, a pathway implicated in the mechanisms of PTB. Therefore, maternal Cu could be used as potential marker of integrated inflammatory pathways during pregnancy and risk for PTB

TISD: A Three Bands Thermal Infrared Dataset for All Day Ship Detection in Spaceborne Imagery

The development of infrared remote sensing technology improves the ability of night target observation, and thermal imaging systems (TIS) play a key role in the military field. Ship detection using thermal infrared (TI) remote sensing images (RSIs) has aroused great interest for fishery supervision, port management, and maritime safety. However, due to the high secrecy level of infrared data, thermal infrared ship datasets are lacking. In this paper, a new three-bands thermal infrared ship dataset (TISD) is proposed to evaluate all-day ship target detection algorithms. All images are from SDGSAT-1 satellite TIS three bands RSIs of the real world. Based on the TISD, we use the state-of-the-art algorithm as a baseline to do the following. (1) Common ship detection methods and existing ship datasets from synthetic aperture radar, visible, and infrared images are elementarily summarized. (2) The proposed standard deviation of single band, correlation coefficient of combined bands, and optimum index factor features of three-bands datasets are analyzed, respectively. Combined with the above theoretical analysis, the influence of the bands’ information input on the detection accuracy of a neural network model is explored. (3) We construct a lightweight network based on Yolov5 to reduce the number of floating-point operations, which is beneficial to reduce the inference time. (4) By utilizing up-sampling and registration pre-processing methods, TI images are fused with glimmer RSIs to verify the detection accuracy at night. In practice, the proposed datasets are expected to promote the research and application of all-day ship detection

TISD: A Three Bands Thermal Infrared Dataset for All Day Ship Detection in Spaceborne Imagery

The development of infrared remote sensing technology improves the ability of night target observation, and thermal imaging systems (TIS) play a key role in the military field. Ship detection using thermal infrared (TI) remote sensing images (RSIs) has aroused great interest for fishery supervision, port management, and maritime safety. However, due to the high secrecy level of infrared data, thermal infrared ship datasets are lacking. In this paper, a new three-bands thermal infrared ship dataset (TISD) is proposed to evaluate all-day ship target detection algorithms. All images are from SDGSAT-1 satellite TIS three bands RSIs of the real world. Based on the TISD, we use the state-of-the-art algorithm as a baseline to do the following. (1) Common ship detection methods and existing ship datasets from synthetic aperture radar, visible, and infrared images are elementarily summarized. (2) The proposed standard deviation of single band, correlation coefficient of combined bands, and optimum index factor features of three-bands datasets are analyzed, respectively. Combined with the above theoretical analysis, the influence of the bands’ information input on the detection accuracy of a neural network model is explored. (3) We construct a lightweight network based on Yolov5 to reduce the number of floating-point operations, which is beneficial to reduce the inference time. (4) By utilizing up-sampling and registration pre-processing methods, TI images are fused with glimmer RSIs to verify the detection accuracy at night. In practice, the proposed datasets are expected to promote the research and application of all-day ship detection

CFD Simulation Strategy for Hypersonic Aerodynamic Heating around a Blunt Biconic

The design of the thermal protection system requires high-precision and high-reliability CFD simulation for validation. To accurately predict the hypersonic aerodynamic heating, an overall simulation strategy based on mutual selection is proposed. Foremost, the grid criterion based on the wall cell Reynolds number is developed. Subsequently, the dependence of the turbulence model and the discretization scheme is considered. It is suggested that the appropriate value of wall cell Reynolds number is 1 through careful comparison between one another and with the available experimental data. The excessive number of cells is not recommended due to time-consuming computation. It can be seen from the results that the combination of the AUSM+ discretization scheme and the Spalart-Allmaras turbulence model has the highest accuracy. In this work, the heat flux error of the stagnation point is within 1%, and the overall average relative error is within 10%

A Multi-task Framework for Infrared Small Target Detection and Segmentation

Due to the complicated background and noise of infrared images, infrared small target detection is one of the most difficult problems in the field of computer vision. In most existing studies, semantic segmentation methods are typically used to achieve better results. The centroid of each target is calculated from the segmentation map as the detection result. In contrast, we propose a novel end-to-end framework for infrared small target detection and segmentation in this paper. First, with the use of UNet as the backbone to maintain resolution and semantic information, our model can achieve a higher detection accuracy than other state-of-the-art methods by attaching a simple anchor-free head. Then, a pyramid pool module is used to further extract features and improve the precision of target segmentation. Next, we use semantic segmentation tasks that pay more attention to pixel-level features to assist in the training process of object detection, which increases the average precision and allows the model to detect some targets that were previously not detectable. Furthermore, we develop a multi-task framework for infrared small target detection and segmentation. Our multi-task learning model reduces complexity by nearly half and speeds up inference by nearly twice compared to the composite single-task model, while maintaining accuracy. The code and models are publicly available at https://github.com/Chenastron/MTUNet

Hyperspectral Image Denoising via Adversarial Learning

Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks. Therefore, HSI denoising has become an essential part of HSI preprocessing. Traditional methods tend to tackle one specific type of noise and remove it iteratively, resulting in drawbacks including inefficiency when dealing with mixed noise. Most recently, deep neural network-based models, especially generative adversarial networks, have demonstrated promising performance in generic image denoising. However, in contrast to generic RGB images, HSIs often possess abundant spectral information; thus, it is non-trivial to design a denoising network to effectively explore both spatial and spectral characteristics simultaneously. To address the above issues, in this paper, we propose an end-to-end HSI denoising model via adversarial learning. More specifically, to capture the subtle noise distribution from both spatial and spectral dimensions, we designed a Residual Spatial-Spectral Module (RSSM) and embed it in an UNet-like structure as the generator to obtain clean images. To distinguish the real image from the generated one, we designed a discriminator based on the Multiscale Feature Fusion Module (MFFM) to further improve the quality of the denoising results. The generator was trained with joint loss functions, including reconstruction loss, structural loss and adversarial loss. Moreover, considering the lack of publicly available training data for the HSI denoising task, we collected an additional benchmark dataset denoted as the Shandong Feicheng Denoising (SFD) dataset. We evaluated five types of mixed noise across several datasets in comparative experiments, and comprehensive experimental results on both simulated and real data demonstrate that the proposed model achieves competitive results against state-of-the-art methods. For ablation studies, we investigated the structure of the generator as well as the training process with joint losses and different amounts of training data, further validating the rationality and effectiveness of the proposed method

Dimension Measurement and Key Point Detection of Boxes through Laser-Triangulation and Deep Learning-Based Techniques

Dimension measurement is of utmost importance in the logistics industry. This work studies a hand-held structured light vision system for boxes. This system measures dimension information through laser triangulation and deep learning using only two laser-box images from a camera and a cross-line laser projector. The structured edge maps of the boxes are detected by a novel end-to-end deep learning model based on a trimmed-holistically nested edge detection network. The precise geometry of the box is calculated by the 3D coordinates of the key points in the laser-box image through laser triangulation. An optimization method for effectively calibrating the system through the maximum likelihood estimation is then proposed. Results show that the proposed key point detection algorithm and the designed laser-vision-based visual system can locate and perform dimension measurement of measured boxes with high accuracy and reliability. The experimental outcomes show that the system is suitable for portable automatic box dimension online measurement