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Sanitary sewer systems are major infrastructures in every modern city, which are essential in protecting water pollution and preventing urban waterlogging. Since the conditions of sewer systems continuously deteriorate over time due to various defects and extrinsic factors, early intervention in the defects is necessary to prolong the service life of the pipelines. However, prior works for defect inspection are limited by accuracy, efficiency, and economic cost. In addition, the current loss functions in object detection approaches are unable to handle the imbalanced data well. To address the above drawbacks, this paper proposes an automatic defect detection framework that accurately identifies and localizes eight types of defects in closed‐circuit television videos based on a deep neural network. First, an effective attention module is introduced and used in the backbone of the detector for better feature extraction. Then, a novel feature fusion mechanism is presented in the neck to alleviate the problem of feature dilution. After that, an efficient loss function that can reasonably adjust the weight of training samples is proposed to tackle the imbalanced data problem. Also, a publicly available dataset is provided for defect detection tasks. The proposed detection framework is robust against the imbalanced data and achieves a state‐of‐the‐art mean average precision of 73.4%, which is potentially applied in realistic sewer defect inspections. [ABSTRACT FROM AUTHOR]

Currently, object detection on remote sensing images has drawn significant attention due to its extensive applications, including environmental monitoring, urban planning, and disaster assessment. However, detecting objects in the aerial images captured by remote sensors presents unique challenges compared to natural images, such as low resolution, complex backgrounds, and variations in scale and angle. Prior object detection algorithms are limited in their ability to identify oriented small objects, especially in aerial images where small objects are usually obscured by background noise. To address the above limitations, a novel framework (ORCNN-X) was proposed for oriented small object detection in remote sensing images by improving the Oriented RCNN. The framework adopts a multiscale feature extraction network (ResNeSt+) with a dynamic attention module (DCSA) and an effective feature fusion mechanism (W-PAFPN) to enhance the model's perception ability and handle variations in scale and angle. The proposed framework is evaluated based on two public benchmark datasets, DOTA and HRSC2016. The experiments demonstrate its state-of-the-art performance in aspects of detection accuracy and speed. The presented model can also represent more objective spatial location information according to the feature visualization maps. Specifically, our model outperforms the baseline model by 1.43% mAP50 and 1.37% mAP12 on DOTA and HRSC2016 datasets, respectively. [ABSTRACT FROM AUTHOR]

Sucker rod constructed of FRP (fiber-reinforced plastic) is currently used in offshore oil and gas industries due to its high tensile strength, lightweight, and resistance to corrosion. Due to the fact that the surface of FRP sucker rod cannot be threaded, it is often attached to steel joints via adhesive technology, and the bonded joint of FRP sucker rod is particularly prone to breaking and coming off. The current research is primarily concerned with the mechanical properties of FRP sucker rods, with little emphasis on the adhesive performance of bonded connections. This research developed a numerical model with a novel bonded joint structure based on the cohesion failure criteria. An indoor test was used to determine and confirm the expected bonded joint failure load. Additionally, an orthogonal test was conducted to maximize the adhesion performance. The findings indicated that a) theaccuracy of numerical model was within 2% and b) when the groove angle was 30°, the length ratio was 2/23, each groove was 14 mm in length based on a triangular groove, and the anticipated failure stress of the bonded joint was around 443 kN, an increase of 10%. [ABSTRACT FROM AUTHOR]

In the beam pumping system, the broken valve cover of the oil well pump frequently leads to oil well failure. This paper examined the alternating load of the oil well pump valve cover and predicted its fatigue life. First, the fluid–structure coupling simulation model of valve ball motion was established through the fluid dynamics simulation software Fluent to investigate the velocity of the valve ball impacting the valve cover. Based on the simulation results about the impact velocity of the valve ball, the mechanical model of the valve ball impacting the valve cover was constructed through ANSYS/LS-DYNA software, and the impact stress and change rule of the valve cover were analyzed. Then, the S-N fatigue curve of the common material of the valve cover was experimentally obtained. Based on the impact stress simulation results of the valve cover and the fatigue curve of the common material of the valve cover, the modified Miner's Rule was applied to evaluate the fatigue life of the oil well pump valve cover. The results showed that the impact stress of the valve cover was positively related to the impact velocity of the valve ball, and the fatigue life of the 17-4 PH valve cover was longer than that of other materials. The above results not only provide a theoretical basis for the performance improvement and material selection of the valve cover but also contribute to the optimization of production parameters of the oil well. [ABSTRACT FROM AUTHOR]

Mineral precipitation or scale formation in oil and gas wellbore is recognized as an obstacle to hydrocarbon recovery. A method using computational fluid dynamics for structure optimization of downhole descaling tools is presented in this paper. The effects of key parameters, such as the nozzle structure style, cone angle, and incidence angle on the descaling performance, are systemically investigated. Numerical results indicate that the cone nozzle has higher jet efficiency because of its smoother velocity and pressure changes, as well as less energy loss. Under the conditions considered, key parameters of the downhole descaling tool are optimized as an incidence angle of 10° and a cone angle of 14°. Furthermore, the numerical results are validated with experimental results, in which the average error between the experimental and numerical results is ∼7.1%. The proposed numerical approaches can accurately predict the jetting status, which consequently has technical guiding significance for the design of downhole descaling tools. [ABSTRACT FROM AUTHOR]

Introduction: Recent studies have revealed that inflammation is a key factor in the causation of opioid analgesic tolerance. Opioids can induce a massive release of inflammatory cytokines and disruption of intestinal barrier function by activating Toll-like receptors 2/4 (TLR2/4), eventually resulting to sustained bacterial transmission and persistent systemic inflammation. However, most of the relevant analyses available were conducted at the level of animal experiments. It is necessary to explore the potential association between opioid tolerance and inflammatory cytokines and gut microbiota in patients with cancer pain. Methods: We retrospectively analyzed cytokines, lymphocyte subsets and blood cells in 186 cancer patients to examine the effect of oral opioids on inflammatory cytokines in patients with moderate to severe cancer pain. The control group constituted tumor patients without cancer pain, while patients with moderate to severe cancer pain taking oral opioids made up the observation group. Fecal samples collected from 25 cancer patients were also analyzed for the composition and diversity of gut microbiota using 16S rRNA sequencing to explore the association between oral opioids and dynamic changes in gut microbiota. Results: Patients with moderate to severe cancer pain taking oxycodone had significantly higher levels of IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ than those in the control group (p < 0.001). The difference in the relative abundance of Lactobacillus (p = 0.025), Anaerostipes (p = 0.034), Megamonas (p = 0.0080), Monoglobus (p = 0.0080), and the Rikenellaceae_RC9_gut_group (p = 0.022) between the opioid and control group was significant. Conclusion: Oral oxycodone can cause abnormal changes in cytokine levels and gut microbiota of patients with moderate to severe cancer pain, prompting chronic systemic inflammation. Analgesic tolerance induced by long-term oxycodone use could be closely related to the consistent upregulation of IL-6 and TNF-α levels. [ABSTRACT FROM AUTHOR]

Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed. [ABSTRACT FROM AUTHOR]

Agriculture has undergone a remarkable transformation, transitioning from traditional methods that were used for centuries to technology-driven practices. The advent of image processing and computational intelligence has revolutionized crop production and plant health monitoring. From drones capturing detailed crop growth data to sensors meticulously measuring soil moisture levels, the possibilities are boundless. This review delves into the cutting-edge research advancements in the application of image processing and computational intelligence techniques for botanical fields, with a particular focus on plant health monitoring. First, it provides a comprehensive overview of the diverse imaging sensors employed in agriculture, including visible, near-infrared, thermal, and hyperspectral imaging. Subsequently, it carefully analyzes the advantages and limitations of each sensor type, along with illustrative examples of their utilization in plant health monitoring. The review further explores the application of machine learning and deep learning for automated plant disease identification, highlighting the critical need for standardized datasets, benchmarking protocols, and domain-specific knowledge for effective implementation. In conclusion, the review emphasizes the future challenges and trends in this rapidly evolving field. It serves as a valuable resource, providing insights into the latest trends in computer vision-based plant disease monitoring and identifying gaps that demand further attention from the scientific community. [ABSTRACT FROM AUTHOR]

This study proposes a microwave heating energy supplement technology for gas hydrate reservoir. The microwave radiation simulation model of the leaky coaxial antenna is established by HFSS. Based on the simulation results of microwave antenna structure parameters on the radiation performance, the optimized shape, angle, length, width and fillet of the slot are rectangle, 80°, 38 mm, 12 mm and 2 mm, respectively. To compare the heating performance of microwave antenna before and after optimization, a microwave heating simulation model for hydrate reservoir is developed, which is validated by experimental results. The comparison results illustrate that, after microwave heating 10 h with the optimized antenna structure, the average temperature within a 1-m radius of natural gas hydrate reservoir increases to 10.613 °C, which is about 5.5 °C higher than that before the optimization. The aforementioned results suggest that the optimized microwave antenna structure significantly increases the temperature of the hydrate reservoir, providing the necessary energy to drive hydrate decomposition. The proposed microwave energy supplementation technique holds promise for advancing the efficient development of natural gas hydrates, the further investigation of effect of which on gas production within hydrate reservoirs is needed for future application. • The microwave heating energy supplement technology for hydrate reservoir is designed. • Investigating the structure parameters' effect on radiation performance of antenna. • The optimal microwave antenna structure is obtained. [ABSTRACT FROM AUTHOR]

As a tertiary oil recovery technology, CO 2 injection is a promising method to rejuvenate the oil reservoirs after the primary production. However, with the injection of low-temperature liquid CO 2 into deep geological structures for the purpose of CO 2 storage and/or enhanced oil recovery (CO 2 -EOR), some of the injection wells experienced problems with tubing and casing failures. The objective of this paper is to find the root cause of tubing and casing failures. The thermo-mechanical coupling numerical simulation model and laboratory experiments are conducted to investigate the effect of frost heaving on tubing and casing. The simulation results well agree with those experimental data, which implies the numerical model is valid and can describe the thermo-mechanical process in the CO 2 injection well. Then, the effect of tubing coupling, annulus width, thickness and operation pressure are explained and analyzed by combining the production parameters and numerical simulation. The results indicate that: (a) The frost heaving generated by volume expansion with freezing of annulus liquid during the low-temperature CO 2 injection is the root cause of tubing and casing failures. (b) The decrease of annulus width and increase of thickness will lead to a dramatically decrease of tubing and casing stress. (c) Increase tubing pressure can help to reduce the tubing stress. Finally, based on the failure mechanism and frost heaving characteristics, an optimal tubing-casing combination is proposed to prolong the operation life of tubing and casing. • Frost heaving pressure is the root cause of tubing and casing failures. • Effect of different structural and operational parameters on frost heaving characteristic are analyzed. • Decreasing annulus width, increasing tubing thickness and pressure are effective measures to reduce frost heaving effects. • An optimal tubing-casing combination is obtained. [ABSTRACT FROM AUTHOR]

The underground sewer network is a vital public infrastructure in charge of large-scale wastewater collection and treatment. Complex defects can occur in sewer pipes due to various internal and external factors, which increase the demand for frequent inspection. Previous defect detection research mainly depended on manual inspection, which is tedious, costly, and error-prone. This study suggests an automatic pixel-level sewer defect segmentation framework based on DeepLabV3+, which can recognize the defect's type, location, geometric information and severity. The impacts of various backbones and pre-processing methods on the model's performance were carefully evaluated. In addition, four state-of-the-art segmentation models (U-Net, SegNet, PSPNet, and FCN) were compared with the presented model to demonstrate its superiority. The experimental results revealed that the DeepLabV3+ with the Resnet-152 backbone structure efficiently identified ten defect types under challenging conditions. The obtained mean pixel accuracy and mean intersection over union (IoU) were 0.97 and 0.68, respectively. In terms of severity analysis, it was revealed that the framework outputs were consistent with the NASSCO pipeline assessment certification program (PACP). In addition, during the testing process, the proposed frame reduction algorithm only required about 16% of the original time required to process an input video. Finally, with a generated detailed report for an inspection video, the suggested framework can offer a decision-making base for more precise and efficient defect inspection and maintenance. • A manually collected sewer defect segmentation dataset that contains over 11,124 images. • An efficient sewer defect segmentation framework based on DeepLabv3+. • A frame reduction is introduced to reduce the computational complexity. • Automatic report generation module to support real-life applications. • Defect severity analysis based on the NASSCO PACP program. [ABSTRACT FROM AUTHOR]

A method using multi-fluid Eulerian computational fluid dynamics (CFD) model to study the migration of pulverized coal in coal-bed methane (CBM) wellbore is presented in this paper. The study aims to predict the condition of discharging pulverized coal out of CBM wellbore and decrease downhole failures such as low efficiency, pump leakage and stuck pump caused by the deposition of pulverized coal. On basis of production conditions of CBM wells in Ordos Basin of China, the transient model of pulverized coal-liquid two-phase flow in the annulus between sucker rod and tubing is established by CFD software, and the effects of pulverized coal size and well liquid inflow velocity on the output of pulverized coal are investigated. On the process of simulation, the distribution and volume fraction changes over time of pulverized coal in the annulus are analyzed, the critical drainage volume of CBM well for discharging pulverized coal is determined, and the output of pulverized coal in every case is predicted. Based on simulation results, the recycle water supplement technology to cleanout pulverized coal in the wellbore is put forward and tested in CBM field. The satisfactory performances of tests show the validity of the CFD study results and the practicability of the recycle water supplement technology. [ABSTRACT FROM AUTHOR]

[Display omitted] • A manually collected masonry crack segmentation dataset that contains approximately 2670 samples. • An efficient DeepLabV3-based masonry crack segmentation framework. • Actual length measurement of the segmented cracks based on brick unit's size. • The proposed model outperformed previous state-of-the-art crack segmentation models. While there have been a considerable number of studies on computer vision (CV)-based crack detection on concrete/asphalt public facilities, such as sewers and tunnels, masonry-related structures have received less attention. This research seeks to implement an automated crack segmentation and a real-life crack length measurement of masonry walls using CV techniques and deep learning. The main contributions include (1) a large dataset of manually labelled images about various types of Korea masonry walls; (2) a careful performance evaluation of various deep learning-based crack segmentation models, including U-Net, DeepLabV3+, and FPN; and (3) a novel algorithm to extract real-life crack length measurement by detecting the brick units. The experimental results showed that deep learning-based masonry crack segmentation performed significantly better than previous approaches and could provide a real-life crack measurement. Therefore, it has a huge potential for motivating masonry-based structure investigation. [ABSTRACT FROM AUTHOR]

Microglial activation, including classical (M1) and alternative (M2) activation, plays important roles in the development of several central nervous system disorders and promotes tissue reconstruction. Toll-like receptor ( TLR)4 is important for microglial polarization. TIR domain-containing adaptor protein ( TIRAP) is an intracellular adaptor protein, which is responsible for the early phase of TLR4 activation. The role of TIRAP in BV2 cell M1 polarization is still unknown. In this study, we showed that TIRAP expression is greatly elevated in lipopolysaccharide ( LPS)/interferon ( IFN)-γ-treated microglia. TIRAP overexpression promoted BV2 microglial M1 polarization by increasing M1-related marker production (inducible nitric oxide synthase, CD86, interleukin-6, interleukin-1β and tumour necrosis factor-α). In contrast, TIRAP knockdown prevented M1-related marker production. Mechanistically, TIRAP could interact with TNF Receptor-Associated Factor 6 (TRAF6) to increase M1-related marker production in TIRAP overexpressed and LPS/ IFN-γ-treated BV2 cells. In addition, silencing of TIRAP effectively inhibited the activation of the Transforming Growth Factor-Beta-Activated Kinase 1/I-Kappa-B Kinase /Nuclear Factor of Kappa Light Polypeptide Gene Enhancer in B-Cells (TAK1/IKK/NF-κB) signalling pathway and the phosphorylation of Akt and mitogen-activated protein kinases, which were activated by LPS/ IFN-γ stimulation. Thus, our results suggest that TIRAP positively regulated BV2 microglial M1 polarization through TLR4-mediated TAK1/ IKK/ NF-κB, mitogen-activated protein kinases and Akt signalling pathways. [ABSTRACT FROM AUTHOR]

[Display omitted] • A massive tunnel crack segmentation dataset with over 170,000 images. • A lining crack segmentation model integrates the ResNet152 into the U-Net encoder. • Automated measurement of segmented cracks. • Robust against noise in challenging tunnel environment. A tunnel is an imperative underground passageway that supports fast and uninterrupted transportation. Over time, various factors, such as ageing, topographical changes, and excessive force, slowly affect the tunnel's internal structure, which causes tunnel defects that can reduce the structure's stability and eventually lead to enormous damage. Therefore, the tunnels need to be checked regularly to detect and fix the cracks promptly. Earlier inspection approaches mainly relied on the operators who directly observed videos to detect the cracks and determine their seriousness, which is laborious, error-prone, and tedious. This research suggests a deep learning-based tunnel lining crack segmentation framework for tunnel images taken by high-resolution cameras. The primary contributions are (1) a lining crack segmentation framework, which is motivated by U-Net architecture, where the encoder is replaced by a ResNet-152 model, (2) the automated measurement of the segmented cracks, which include length, thickness, and type, and (3) a huge lining crack segmentation database. The experimental results showed that the framework obtained comparable performance compared to existing crack segmentation models and supported the automated measurement of the segmented cracks. [ABSTRACT FROM AUTHOR]

• Designed a lightweight attention module to focus on the informative features. • The optimized SR method improved detection and segmentation performances. • Provided a pest dataset that contains 9,872 images and corresponding annotations. • The proposed network outperformed previous studies for pest localization. Prompt and specialized pest management involving localization and recognition has become a crucial means to prevent pest attacks in modern agriculture. Traditional pest monitoring methods are inaccurate and inefficient due to the hand-crafted features and the low-resolution images. As a result, this study presents an automatic framework that can precisely detect 10 species of pests in the natural environment and assist humans in identifying the locations and contours of pests efficiently. The main contributions of this paper include (1) a novel attention module that encourages the network to focus on the important features; (2) an optimized super-resolution approach that is used for both training and testing images to enhance the image quality; (3) a pest monitoring network is proposed by improving the D2Det's structure and adjusting the parameters; and (4) a dataset containing pest images and manually annotated files. Experiments showed that the proposed Pest-D2Det model achieved state-of-the-art performances in terms of the mean Average Precision (mAP) values of detection (78.6%) and segmentation (77.2%). Meanwhile, the performance of our efficient channel and spatial attention network (ECSA-Net) indicated that it is lightweight and effective, which can be integrated into deep learning-based models without computation burden. [ABSTRACT FROM AUTHOR]

The sanitary sewer is a crucial underground infrastructure of any country that collects wastewater and carries it to the treatment plant. The damage triggered by various factors, such as external interference, long-term corrosion, and uneven distribution of pressure, could lead to various types of defects inside the sewer pipe. Previous studies primarily relied on human visual perception to evaluate the sewage system, which was tedious, time-consuming, and costly. As a result, an efficient and robust sewer defect localization framework was proposed in this manuscript. The main contributions include (1) a novel sewer defect detection system motivated by the state-of-the-art detection transformer (DETR) architecture, which views object localization as a set prediction topic; (2) a defect severity analysis approach based on the transformer's self-attention operation to analyze defect zone of influence and defect grade; and (3) a manually validated sewer defect localization dataset that contains 10 types of commonly appeared sewer defects. The experimental results suggested that the proposed system outperformed the previous standard object detection approaches with the highest mean Average Precision (mAP) of 60.2% on the collected dataset. • A manually collected sewer defect detection dataset that contains over 47,000 images. • An efficient transformer-based sewer defect detection framework. • Severity analysis of the localized defect using the transformer's self-attention weights. • The proposed model outperformed previous state-of-the-art object detection models. [ABSTRACT FROM AUTHOR]

• A dehazing algorithm is integrated into the proposed defect inspection framework in order to increase the detection rate. • An efficient backbone structure (Res2Net-Mish-BN-101) is proposed to improve the learning ability of SOLOv2. • An enhanced BiFPN (EBiFPN) is designed for better feature fusion. • A deep learning-based segmentation model (Pipe-SOLO) and a manually annotated dataset are firstly proposed to segment sewer defects at the instance level. The inspection of underground sewer defects plays a considerable role in estimating the structural integrity and avoiding various unforeseen functional failures. However, the conventional sewer defect inspection approaches suffer from the blurry and vaporous environment inside the sewer pipes, which significantly lowers the performance. Besides, it is challenging to achieve efficient and accurate condition assessment by the common manual inspection. Therefore, this manuscript introduces an automatic instance segmentation-based defect analysis framework. The main contributions include 1) a novel defect segmentation model called Pipe-SOLO is firstly presented to segment six common types of defects at the instance level by proposing an efficient backbone structure (Res2Net-Mish-BN-101) and designing an enhanced BiFPN (EBiFPN) , 2) a GAN-based dehazing model is applied to effectively solve the image blurring problem, and 3) a publicly available sewer defect segmentation dataset. The experimental results show the proposed Pipe-SOLO achieved an improvement of 7.3% compared with the state-of-the-art method in terms of the mean Average Precision (mAP). Therefore, the proposed defect segmentation method is promising to be integrated with real-life applications that require defect localization and estimation. [ABSTRACT FROM AUTHOR]

The effect of surface roughness on corrosion behaviour of carbon steel under intermittent oil/water wetting with different flow conditions was investigated by an "alternate wetting cell", combining with potentiostatic polarisation, contact angle measurement, and computational fluidic dynamics (CFD) analysis. Under static flow conditions, the rough surface increased the oil film lifetime and consequently mitigated the corrosion as the oil film has good adhesion and acted as a blocking barrier. However, under dynamic flow conditions, the smooth surface polished by 6 Micron diamond suspension shows the longest oil film lifetime and highest corrosion mitigation efficiency, caused by small turbulence eddies and low shear stress. • Rough surface mitigates corrosion under intermittent oil/water wetting at static conditions. • However, the rough surface accelerates the corrosion process at dynamic conditions. • The stability of the oil film caused by wetting hysteresis plays an important role in corrosion. • At static conditions, small steel-oil surface tension induces stable oil film and mitigates corrosion. • At dynamic conditions, the large turbulence eddy deteriorates the stability of the oil film and accelerates corrosion. [ABSTRACT FROM AUTHOR]

Natural gas hydrate (NGH) is widely considered as an alternative energy source due to its tremendous reserves, cleanness and high energy density. During the exploitation process, discharging the gas-liquid phase to the surface for separation not only affects the pump performance but also increases the space requirements of the offshore platform. Taking NGH production test data in South China Sea for example, a downhole spiral gas-liquid separator is designed for high gas-liquid ratio (liquid content is less than 10%) in this paper. The forces of droplets in the separator are analyzed and the CFD model for gas-liquid two phase flow is established. The RNG k-ε turbulence model is used for analyzing the velocity field distribution and the pressure field distribution in the separator. The influence of structure parameters (pitch, cycle number of spiral piece, and the outer diameter of spiral pipe) and scale on the separation performance is investigated. The energy loss increases sharply after scaling in the separator and some necessary measures should be taken to avoid the generation of the sale and the secondary hydrate. Based on steepest ascent design, response surface methodology and particle swarm optimization, the optimal structure of the separator with high separation efficiency 87.26% and low pressure drop 2614.19Pa is obtained. The experimental platform is built for testing the gas-liquid separator. The comparison of numerical simulations and experiments show that the calculation model for the hydrate gas-liquid separation is correct and feasible. The separation device with the optimal structural parameters has good separation performance. • A spiral gas-liquid separator suitable for NGH underground mining is proposed. • The flow field and the separation performance of the separator are analyzed. • A parametric study on the separation effect yields an optimal separator. • The optimal geometry has a better separation performance for NGH wells. [ABSTRACT FROM AUTHOR]

Microwave heating is an efficient thermal stimulation approach in developing coalbed methane (CBM). In this paper, a new microwave heating technology for CBM is proposed and two structure schemes of waveguide antennas for heating are designed. After characterizing the mathematical model of the microwave coaxial antenna radiation, the numerical model of coal reservoir heated by microwave is established, which couples both the electromagnetic field and the heat transfer. The electromagnetic field distribution and the temperature changing law in coal reservoir during microwave heating process are solved numerically and verified by the designed experiment. A parametric study on heating effect yields an optimized structure of the microwave antenna, which is SD 6 × R8 and α = 15°. After irradiated by the optimal antenna for 120 min, the average temperature rises to 62 °C (from 20 °C) and the energy efficiency reaches 85%. In different coal reservoirs, the maximum temperature ranges from 169 °C to 1586 °C and the maximum temperature gradient ranges from 2.52 °C/mm to 37.19 °C/mm, which are beneficial to the crack propagation. The microwave heating technology can provide fast and sufficient energy to raise the temperature of coal reservoir and is feasible for enhancing the CBM recovery. [Display omitted] • A microwave heating technology for CBM recovery is designed and simulated. • The mathematical models of antenna radiation and microwave heating are established. • The microwave heating performance under different factors is investigated. • An optimal structure of microwave antenna (SD 6 × R8 and α = 15°) is obtained. [ABSTRACT FROM AUTHOR]

Fault diagnosis of the sucker rod pumping system (SRPS) is a challenging problem in the oil industry. Recently, the computer-aided indicator diagram (ID) recognition techniques are becoming useful measurements to help engineers monitor the wells. However, the slow variation of SRPS state makes it difficult for a well to experience all the fault conditions. Using the IDs collected from other oil wells as training data will lead to a large difference between the distribution of training data and target data because of the interference information including noise and system operation characteristics in IDs. Moreover, the different occurrence probability of each fault leads to the imbalance of training data, therefore the fault tolerance of the diagnosis models will result in lower accuracy in identifying the fault IDs with less training samples. In addition, due to one-off training, the parameters of diagnostic model are always fixed resulting in lack of adaptive ability. To address this issue, an evolutional support vector machine (SVM) method for SRPS diagnosis is proposed. First, to obtain balanced training data with similar distribution to the target data, a novel model describing the working process of SRPS under various fault conditions is established. Subsequently, the static apparent stiffness feature and its extraction algorithm is proposed to fully retain the fault information of SRPS in IDs. Then, the incremental SVM is used to construct the diagnosis model based on generated IDs. At last, the proposed method is verified experimentally through the system parameters and IDs of many wells collected from oilfields, and then some conventional techniques are employed in the comparison studies. The obtained results show that the accuracy of the diagnosis model trained by simulated IDs is 11.2% higher than that trained by collected IDs. Besides, the parameters of proposed diagnosis model can be continuously evolved to improve the diagnosis accuracy and generalization ability. Furthermore, the proposed diagnosis model also has advantages in training efficiency and memory consumption. • A novel model describing the working process of SRPS under various fault conditions is established to generate the training data for diagnosis model. • A novel feature of static apparent stiffness and its extraction algorithm is proposed to eliminate the interference component in indicator diagram. • Incremental algorithm and generated indicator diagram are used to continuously improve the performance of SVM diagnosis model. [ABSTRACT FROM AUTHOR]

With the rapid rise of private vehicles around the world, License Plate Recognition (LPR) plays a vital role in supporting the government to manage vehicles effectively. However, an introduction of new types of license plate (LP) or slight changes in the LP format can break previous LPR systems, as they fail to recognize the LP. Moreover, the LPR system is extremely sensitive to the conditions of the surrounding environment. Thus, this paper introduces a novel deep learning-based Korean LPR system that can effectively deal with existing challenges. The main contributions of this study include (1) a robust LPR system with the integration of three pre-processing techniques (defogging, low-light enhancement, and super-resolution) that can effectively recognize the LP under various conditions, (2) the establishment of two original Korean LPR approaches for different scenarios, including whole license plate recognition (W-LPR) and single-character license plate recognition (SC-LPR), and (3) the introduction of two Korean LPR datasets (synthetic data and real data) involving a new type of LP introduced by the Korean government. Through several experiments, the proposed LPR framework achieved the highest recognition accuracy of 98.94%. [ABSTRACT FROM AUTHOR]

Up to date, compression packers have been widely utilized to seal annular space between tubing and casing in the fracturing process of low permeability reservoirs. However, packer end damage under great annulus pressure difference and internal shear failure caused by excessive pressure in tubing are the main failure reasons. Current research works mainly focus on the theoretical model of sealing property with contact stress as evaluation index and less consideration was given to pressure-bearing property in rubber. This paper established a novel analytical model which took into account material properties, geometric dimension and borehole expansion ratio setting the maximum shear stress as evaluation criteria, which can investigate the distribution of shear stress along the axial diameters on compression packer under one end loaded and other end fixed. Also, the accuracy of the analytical model was verified by FEM. Main results illustrated that a) the analytical results can describe the mechanical behavior of the compression process well and the recommended setting pressure was less than 40 MPa; b) with the materials hardened, the compression distance reduced smoothly and the maximum shear stress almost kept unchanged; c) the compression deformation and maximum shear stress rose significantly as the inner diameter of the open hole well increased; d) the height of the packer played a negative role both in maximum compression distance and maximum shear stress of the compression packer. In this paper, a novel analytical modeling of the pressure-bearing performance of compression packer was established. The HNBR specimens were manufactured and the mechanical property test was carried out. Besides, an analytical modeling of the compression distance and shear stress along the axial diameters on packer was established. The accuracy of analytical modeling was verified and the influence factors on pressure-bearing performance were discussed. [Display omitted] • A novel analytical model to calculate the distribution of compression distance and shear stress along the axial diameters was established. • The maximum shear stress inside rubber was proposed as evaluation criterion to judge the pressure-bearing performance. • The reliability and application range of analytical model was verified by FEM. [ABSTRACT FROM AUTHOR]

The carbon/glass hybrid rod exhibits high tensile strength, light weight, and strong corrosion resistance. However, the bottleneck of interlayer splitting exists in the interlayer hybrid rod, severely impeding safe operation. To solve the problem of rod splitting, this paper proposed a new scheme of an intralayer carbon/glass hybrid rod. Based on the material constitutive model, a full‐scale simulation model was developed. The stress distribution and deformation of the hybrid rod were analyzed and the progressive damage mechanism of the hybrid rod was explained. The results indicated that, for the intralayer carbon/glass hybrid rod, (a) the ultimate experimental tensile load witnessed a significant increase, rising by 14.0% from 452.4 to 515.7 kN compared to the interlayer hybrid rod; (b) the numerical model predicted the tensile load of 525.9 kN, with an error of 1.9% compared to the test result, meeting engineering accuracy requirements; (c) the elastic deformation stage saw a 146.7% increase from 280.2 to 691.3 MPa in the maximum axial stress of the carbon‐fiber layer, and a 120% increase from 89.8 to 198.0 MPa in the maximum radial stress of the glass fiber layer as the tensile displacement increased from 0.16 to 0.32 mm. In conclusion, the intralayer carbon/glass hybrid rod proposed in this paper can significantly improve the tensile strength compared to the interlayer hybrid rod, which can accelerate the application of carbon/glass hybrid rods in oil and gas fields. Highlights: According to the problem of interlayer splitting in the interlayer carbon/glass hybrid rods, a novel scheme of an intralayer carbon/glass hybrid rod was proposed.Based on the material constitutive model, a full‐scale simulation model of an intralayer carbon/glass hybrid rod was developed to analyze the stress distribution and deformation response under tensile load.The dynamic progressive damage analysis method for intralayer carbon/glass hybrid rods was proposed to reveal the damage evolution mechanism. [ABSTRACT FROM AUTHOR]

The nonlinear constitutive model of the proppant column was established through laboratory experiments on the stability of the proppant column. Based on reservoir geomechanics and the finite element method, a fracture-proppant column interaction model was established for high-conductivity channel fracturing. The effects of in situ stress, reservoir rock elastic parameters, and spatial distribution characteristics of the proppant column on the closure deformation of the high-conductivity fracture channel and the stability of the proppant column were studied. The higher the in situ stress, the higher the contact stress on the rock plate; the lower the height and the larger the diameter of the proppant column, the more prone to deformation and breakage, while the more the effective support decreases with the increase of the in situ stress. Under the condition of constant in situ stress, with the increase of the reservoir elastic modulus, the relative axial displacement of the two slabs decreases gradually, the effective propping ratio of fractures increases, and the reservoir elastic modulus has little effect on the stability of the proppant column. The effective propping ratio decreases with the increase of the proppant column diameter, increases with the increase of the proppant column height, and increases with the increase of the ratio of the reservoir elastic modulus to in situ stress. When the proppant column diameter (proppant column spacing) is less than 3 m, the effective propped fracture ratio increases significantly. Through the above research, the optimal proppant cluster diameter was finally optimized. [ABSTRACT FROM AUTHOR]

To reduce the threat of explosions to personnel safety, it can be considered to retrofit the existing buildings in high-risk areas of process industry in an economical and effective way. The mechanical behaviors of two types of polyureas were studied through a series of laboratory tests and field gas explosion tests, and the full-field explosion responses of polyurea were measured by a high-speed, three-dimensional digital image correlation (DIC) technique. It was found that polyurea exhibited a significant strain-rate-hardening effect, and the fracture strain of polyurea decreased significantly with the increase of strain rate. The failure criterion of the polyurea-coated fiber-reinforced cement board (FRCB) under gas explosion load conformed to the overpressure criterion. Polyurea could significantly improve the explosion resistance of the FRCB, and the reinforcement ability could be further improved by increasing the polyurea thickness of the rear face and strengthening on both sides. Dynamic thermodynamic and microscopic observations revealed that under gas explosions, the polyurea was in the rubber–glass transition zone, which was beneficial for improving the ability of the material to attenuate shock waves. • Field tests of polyurea-strengthened FRCBs under gas explosions were conducted. • Structural behaviors of polyurea-coated FRCB were investigated. • Explosion resistance performances of different types of polyurea-coated specimens were quantitatively evaluated. • The strain-rate-hardening and induced crystallization of polyurea improved the explosion resistance. [ABSTRACT FROM AUTHOR]

It is a wildly accepted approach to study the working mechanism of the single abrasive grain by experiment and numerical simulation to improve the working performance of the milling tool in the development of unconventional oil and gas resources. In this study, a mechanical analysis model and a 2D numerical simulation model of single abrasive grain were established to analyze the working mechanism of the milling tool. Then a milling tool with single abrasive grain and an experimental system for milling tools were developed to verify the numerical results. Also, the working process was recorded by a high-speed camera to analyze the chip formation mechanism and the thermal field was obtained by the 2D numerical simulation model to improve service life. Besides, the influence of different working parameters including weight on bit (WOB) and rotating speed was conducted to improve efficiency and safety of the milling tool. Study results indicate that the error between simulation and experiment is within 7.43%, which verifies the accuracy of the 2D numerical simulation model. Both the chips of simulation and experiment are jagged chips without cooling and the error of the chip morphology is only 5.88%. The temperature on the cutting edge increased faster than other positions with some fluctuation while that on the rake face reached a peak at 658.5 °C with some fluctuation. These studies are significant for the design and application of the milling tool with high performance. A mechanical analysis model and a 2D numerical simulation model of single abrasive grain was established to analyze the working mechanism of the milling tool. Then a milling tool with single abrasive grain and an experimental system for milling tools were developed to verify the numerical results. Also, the working process was recorded by a high-speed camera to analyze the chip formation mechanism and the thermal field was obtained by the 2D numerical simulation model to analyze the heat generation mechanism. Image 1 • A analysis model and a 2D numerical simulation model of single abrasive grain were established. • A milling tool with single abrasive grain and an experimental system for milling tools were developed to verify the numerical results. • The working process was recorded by a high-speed camera and the thermal field was obtained by the 2D numerical simulation model. • Influence of different working parameters was conducted to improveworking performance of the milling tool. [ABSTRACT FROM AUTHOR]

Carbon dioxide injection or storage in mature or partially depleted oil reservoirs is an effective way to reduce carbon dioxide emissions and improve oil recovery. However, the corrosion defects greatly reduce the residual mechanical strength of the tubing with the effect of frost heaving force, which seriously limits the carbon dioxide enhanced oil recovery technology widely application. Therefore, the residual mechanical strength of corroded tubing in cryogenic carbon dioxide injection well is investigated in this paper. The results indicate that: (a) The residual mechanical strength of corroded tubing with the effect of frost heaving force is about 68% less than that without frost heaving force. (b) The effect of corrosion depth and location are sensitive, while the corrosion shape is relatively weak. (c) It is a meaningful method to increase working pressure to improve the residual mechanical strength of corroded tubing in the frozen section. • Frost heaving force reduces the strength of the tubing by about 68%. • Corrosion depth and location are more sensitive than corrosion shape. • Increasing working pressure is conducive to improve the residual mechanical strength. [ABSTRACT FROM AUTHOR]

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset. [ABSTRACT FROM AUTHOR]

In the coalbed methane (CBM) exploration and development, microwave treatment technology can be used to improve the reservoir permeability and enhance coalbed methane recovery. In this paper, the mechanism of microwave heating on micro-cracks in coal and rock is studied. And to research the interactions between microwave and coal, a mathematical model of electromagnetic field and heat transfer in solid coupling is established. Combined with numerical simulation and experimental research, the electromagnetic field distribution and the temperature changing law in coal samples collected from Ordos basin of China during microwave heating process are analyzed. Through the comparisons of appearance identification and internal CT scanning before and after microwave treatment, there are a large number of obvious cracks generated by microwave heating on the surface and inside of coal samples. Three main generation mechanisms of micro-cracks in coal and rock are obtained by SEM analysis. Finally, a new microwave heating technology for CBM recovery is proposed and the structure of waveguide antennas (coaxial) for heating is designed. The simulation results show that the reservoir temperature has been greatly improved and the temperature change rates in the top half of the reservoir are larger which can easily cause the generation of micro-cracks. • Microwave has more significant effect on lower rank coals than higher rank coals. • Cracks and permeability increase with the increase of microwave heating time. • The mechanism of micro-cracks generated by microwave heating is studied by SEM. • A microwave heating technology for CBM recovery is designed and simulated. [ABSTRACT FROM AUTHOR]

Remote monitoring of sucker rod pumping system (SRPS) based on polished rod dynamometer cards (DC) is an effective means to reduce the operating costs and realize the real-time control. Nevertheless, how to obtain the working process and the production rate of SRPS from polished rod DC is always a puzzle because of complex down-hole motions. The typical solution for this problem is deriving the pump DC via the wave equation to achieve the effective stroke of plunger. But some key parameters involved in this approach such as valve leakage and the fill factor are difficult to determine accurately, especially under fault conditions, which may influence the accuracy of output metering. In order to address this tough issue, in this paper, a novel method of output metering with DC is proposed based on quantitative analysis of fault. The method consists of two parts, the first part is an approach to simulate the working process of SRPS under fault conditions, specifically it's an improved simulation model characterized by a set of fault parameters, which is presented through analyzing the mechanisms of sucker rod pumps at normal and several faulty scenarios. And the second part is a method implemented by genetic optimization algorithm to determine the fault parameters set of the actual SRPS. Using parameters of typical well, the analysis of effects of faults on SRPS is carried out. The simulation results show that valves leakage reduces the flow rate of liquid significantly in the period of effective stroke, which leads to the distortion of output metering results of effective stroke method. Compared with single faults, coupling faults aggravate the production reduction of SRPS. Furthermore, when the well fluid contains gas, the impact of traveling valve leakage on the reduction of production is greater than that of standing valve leakage, but the consequence is opposite when formation energy is insufficient. Besides, the carbon fiber rod and fiberglass rod SRPS are more sensitive to valve leakage, while the steel rod and wire rope SRPS are more sensitive to the condition of insufficient liquid supply. Eventually, the proposed method is verified experimentally through the productive parameters and measured DC of actual wells collected from an oilfield. The obtained results demonstrate the effectiveness of the proposed method for output metering and fault diagnosis as well. Image 1 • An improved model of SRPS is presented to obtain the working process and production rate under fault conditions. • A novel method of output metering with DC for SRPS under fault conditions is proposed based on optimization algorithm. • Field data is adopted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The downhole working conditions of oil fields are harsh and complex, and the working performance of tools is affected by erosion and wear after being washed by sand laden mud. In order to improve the service life of downhole tools and optimize the material with excellent anti-erosion properties, the anti-erosion properties of three common metal materials are studied in this paper. By analogy with the difficulty of abrasive water jet cutting materials, the erosion wear ability of sand laden mud on the shell of downhole tools is equivalent evaluated, and the time dimension is shortened, which is prone to analyze the erosion wear resistance of different materials. Then, a numerical model of abrasive particles impact on the material surface, based on the finite element method–smoothed particle hydrodynamics coupled method, is established, the deformation and material removal amount of different materials under the same erosion condition are compared and analyzed, and the reasons for the difference in anti-erosion properties of different materials are quantitatively analyzed from the microscopic point of view. The results showed that under the same erosion conditions, the material removal amount 40CrNi2Mo < 45Steel < Ti6Al4V, and the anti-erosion properties decreases sequentially, which is not affected by erosion velocity V (jet pressure P) and waterjet sand ratio ζ. Among them, the material deformation and removal of 40CrNi2Mo and 45Steel are similar, and the anti-erosion properties of the two are approximately the same. [ABSTRACT FROM AUTHOR]

• A manually collected and validated crop pests dataset (10 classes/5629images). • A fine-tuned GoogLeNet model for the identification of 10 crop pests types. • Combine several pre-processing techniques to deal with the complex background. • The average detection accuracy for the proposed model is over 98%. • The fine-tuned GoogLeNet model obtained an improvement of 6.22% compared to the state-of-the-art method. Crop diseases and insect pests are major agricultural problems worldwide, because the severity and extent of their occurrence causes significant crop losses. In addition, traditional crop pests recognition methods are limited, ineffective, and time-consuming due to the manual selection of the useful feature sets. This paper introduces a crop pest recognition method that accurately recognizes ten common species of crop pests by applying several deep convolutional neural networks (CNNs). The main contributions of this paper are (1) a manually collected and validated crop pest dataset is described and shared; (2) a fine-tuned GoogLeNet model is proposed to deal with the complicated backgrounds presented by farmland scenes, with pest classification results better than the original model; and (3) the fine-tuned GoogLeNet model obtains an improvement of 6.22% compared to the state-of-the-art method. As a result, the proposed model has the potential to be applied in real-world applications and further motivate research on crop disease identification. [ABSTRACT FROM AUTHOR]

• Two erosion mechanisms of solid particle on the grinding tool in polymer-flooding wastewater treatment process are proposed. • The mathematical model between operating parameters and erosion rate of the grinding tool is established. • Reasonable recommendations of operating parameters for the application of the grinding tool in the treatment processes is provided. The particles in polymer-flooding wastewater can cause the grinding tool erosion failure during the grinding process, resulting in the grinding tool not meeting the treating requirement. In this paper experimental and numerical analysis methods are used to explore erosion failure phenomenon, erosion mechanisms, for reducing the erosion damages of the grinding tool during polymer-flooding wastewater treatment processes. Based on the three-dimensional numerical simulation, the maximum erosion surface is found and the erosion rates of different surfaces of the grinding tool are calculated. The validity of the simulation model is verified by on-site experimental value. Combined with erosion morphology observation of samples cut from the grinding tool by SEM and the flow characteristics analysis by CFD, two erosion mechanisms are proposed basing on different flow fields. Furthermore, the effect between operating parameters (rotating speed, flow rate) and the erosion rate of the grinding tool is investigated and the mathematical model is built. Based on this mathematical model, operating parameters are optimized to improve the corrosion resistance and extend service life of the grinding tool during the polymer-flooding wastewater treatment. [ABSTRACT FROM AUTHOR]

Computer-aided fault diagnosis based on the dynamometer card (DC) of the sucker-rod pumping system (SRPS) is a crucial technology to reduce operating costs and increase yield. Currently, the conventional method to implement this technology is the pattern recognition of the DC features. However, the training set of DC that determines the diagnostic accuracy of the method is difficult to obtain because of the differences between oil wells. Moreover, this method can only obtain the type of single fault without quantitative analysis, which may affect the formulation of the adjustment measures. Therefore, in the present study, a quantitative diagnosis method independent of the training data is proposed. In order to obtain the operation process of SRPS under the comprehensive conditions of the fault, a simulation model involving fault effects is established according to the fault mechanism. Subsequently, the framework of the optimization inversion method is established to determine the fault parameters by minimizing the difference between the measured DC and the DC generated by the fault-mechanism model. Then, the strategy of the partition parallel optimization is utilized to improve the stability and efficiency of the inversion algorithm. Meanwhile, fifteen indicating parameters that directly reflect the type and degree of faults are defined. Finally, the proposed diagnosis method is verified experimentally through the data of 20 actual wells. The obtained results demonstrate the effectiveness of the proposed method for diagnosing the types of single and coupling faults, as well as predicting the production rate. • A novel fault diagnosis process different from pattern recognition and machine learning is presented based on fault-mechanism model and inversion algorithm. • A quantitative diagnosis method of sucker rod pump system without training set of dynamometer cards is proposed. • The strategies of partition parallel optimization is adopted to improve the computational efficiency of the inversion algorithm. • The proposed method is capable of obtaining not only the types of single fault and coupling faults, but also the fault degree and output performance of rod pump system. [ABSTRACT FROM AUTHOR]

On March 11, 2020, the World Health Organization declared its assessment of coronavirus disease 2019 (COVID‐19) as a global pandemic. However, specific anti‐severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) drugs are still under development, and patients are managed by multiple complementary treatments. We performed a retrospective analysis to compare and evaluate the effect of low molecular weight heparin (LMWH) treatment on disease progression. For this purpose, the clinical records and laboratory indicators were extracted from electronic medical records of 42 patients with COVID‐19 (21 of whom were treated with LMWH, and 21 without LMWH) hospitalized (Union Hospital of Huazhong University of Science and Technology) from February 1 to March 15, 2020. Changes in the percentage of lymphocytes before and after LMWH treatment were significantly different from those in the control group (P = 0.011). Likewise, changes in the levels of D‐dimer and fibrinogen degradation products in the LMWH group before and after treatment were significantly different from those in the control group (P = 0.035). Remarkably, IL‐6 levels were significantly reduced after LMWH treatment (P = 0.006), indicating that, besides other beneficial properties, LMWH may exert an anti‐inflammatory effect and attenuate in part the "cytokine storm" induced by the virus. Our results support the use of LMWH as a potential therapeutic drug for the treatment of COVID‐19, paving the way for a subsequent well‐controlled clinical study. [ABSTRACT FROM AUTHOR]

For carrying out the high-quality cropping cross-sections, it is critical to obtain the variable frequency-loading curve rapidly and accurately. This paper proposes a processing method on the loading force acted on the bar based on the separate treatment at the crack initiation stage and the subsection treatment at the crack propagation stage. At the crack initiation stage, the formula of stress concentration factor of V-shaped notch is given by the polygonal section assumption method when the bottom corner radius of V-shaped notch is 0.2-0.5 mm, and the critical initial load acted on the experimental bar is about 1405 N. According to the principle and characteristic of the cropping method, the critical stability-loading curve between the rotation frequency of main shaft and the crack propagation time is given and the transition curve is obtained by the data fitting method; the mutation force is zero when the bottom corner radius is 0.1915 mm. The cropping experimental results show that the critical variable frequency-loading curve, by which the cross-section roughness, the area and the eccentricity of final rupture region are all very small, is an ideal control mode compared with other frequency-loading curves mentioned in this paper. [ABSTRACT FROM AUTHOR]

A new type of low-stress cropping system based on hydraulic compensation is proposed in this paper. In order to solve the sudden change of critical loading force before and after the crack initiation at the tip of V-shaped notch and improve the quality of cropping section, it is proposed that the fracture process of the bar is divided into two stages. At the crack initiation stage, the calculation formula of the critical loading force is established by means of stress concentration factor and extended finite element method (XFEM). At the crack propagation stage, the calculation formula of the critical loading force is built by stress intensity factor and FRANC3D software. The concept of mutation coefficient F p0.2 / F ci is also put forward. The research results show that the influence of bottom corner radius s of V-shaped notch on F p0.2 / F ci is the largest; when s is between 0.12 mm and 0.3 mm, F p0.2 / F ci decreases sharply; when s is equal to 0.12 mm, there is no force mutation. The cropping results show that the high-quality cross-section is obtained by using the variable frequency-loading curve designed in this paper. [ABSTRACT FROM AUTHOR]

Fracturing tools such as sliding sleeves, fracturing balls, and ball seats are extensively used for downhole fluid and pressure control. The ball seat is one of the most important components in the multistage fracturing system. After the hydraulic fracturing treatment is complete, the traditional ball seat restricts the production flow and thus must be removed. Extra mechanical interventions are then required to mill out the ball seat after fracturing treatment. In this work, an innovative fracturing ball seat that can be dissolved was proposed and investigated using a controlled electrolytic method (CEM). The newly designed ball seat can be removed through a specific corrosion rate to facilitate an unrestricted flow path for oil and gas production. Comprehensive experiments on the CEM were conducted. The effects of various electrolytic parameters, such as electrolysis current, electrode distance, electrolytes, and tool thickness, on the material removal rate of the ball seat and sliding sleeve were investigated. Experimental results show that the electrolytic parameters significantly affected the electrolytic characteristics. Aluminum alloy is suitable for the manufacture of dissolvable fracturing tool because of its good strength and high electrolytic rate. The study on the newly proposed CEM could provide insights into the design of novel fracturing tools and intelligent well completion systems. [ABSTRACT FROM AUTHOR]

Wear phenomenon has caused severe damage or failure of fracturing tools in oil and gas industry. In this paper, influence of heat treatment on the mechanical properties and wear resistance of fracturing tool made of lamellar graphite grey cast iron were investigated. The surface composition and microstructure were characterized by X-ray diffraction (XRD) and metallographic microscope. Sliding wear tests were performed to study the tribological behavior. Tests results showed that wear rates of treated specimens decreased by 33 %. Besides, worn morphology and wear debris were analyzed using Scanning electron microscope (SEM) and Energy dispersive X-ray spectra (EDS). Wear failure mechanisms of specimens were identified. Furthermore, on-site experiment results indicated that wear loss of treated samples decreased by 37.5 %. The wear life of hydraulic fracturing tools can be improved obviously by the heat treatment. [ABSTRACT FROM AUTHOR]

With the increasing requirement for energy, unconventional oil and gas resources such as shale gas, shale oil and coalbed methane play a vital role in energy supply. Therefore, the erosion phenomenon which causes serious tool damage during exploitation has attracted tremendous attention and research interest. The objective of this paper is to investigate the effect of flow characteristics on the geometry evolution process of the downhole fracturing tool. Computational fluid dynamics (CFD) simulation with standard k-ε turbulence model and Lagrangian particulate flow model is carried out to investigate the fracturing process. Effects of flow rate, fluid viscosity and proppant size on pressure drop and erosion rate are studied. It is found that erosion rates increase significantly with the increasing flow rate, while decrease with increase of fluid viscosity and proppant size. Furthermore, second and subsequent impacts occur during fracturing. The maximum erosion rate appears around the port of sliding sleeve. The geometry evolution of sliding sleeve can be predicted through the analysis of erosion information. Moreover, a general agreement between numerical results and field data is achieved. These numerical simulations can provide useful guidance for prolonging the erosion life of fracturing tool and improving exploitation efficiency. [ABSTRACT FROM AUTHOR]

At present, fiberglass sucker rod has been gradually utilized in low permeability reservoirs because of their high strength and strong corrosion resistance. However, the bonding joint of fiberglass sucker rod in high water-content well is particularly prone to break and fall off, which influences the service life and construction safety of recovery operation. Current researches mainly focused on the bonding strength at room temperature, and less consideration was given to the residual strength and failure pattern under immersion. This paper proposed a novel diffusion-structure finite element model (FEM) based on transient dynamic method to simulate the process of water into the joint, and the life-death element method was adopted to study the progressive damage of bonding joints verified by indoor experiment in order to predict residual failure strength and obtain failure mode in submerged environment. As immersion duration extended, the results illustrated that a) the cohesive failure gradually became main failure; b) the maximum von Mises stress, shear stress, and end deformation of bonded joint increased and the maximum interfacial stress barely unchanged within the range of 5%; c) the numerically predicted strength decreased from 21.8 to 11.5 MPa, which was in good agreement with the experimental results within a deviation of 8%. [ABSTRACT FROM AUTHOR]

A method using Computational Fluid Dynamics (CFD) for structure optimization of ball seat is presented in this paper. The study aims to explore the erosion failure phenomenon and decrease the erosion rate of ball seat during hydraulic fracturing. To investigate the downhole erosion phenomenon, three dimensional CFD models are performed to analyze flow characteristics such as velocity, pressure, sand concentration and erosion distribution. And, the influences of parameters such as cone angle, structure style of ball seat on the flow characteristics are investigated. Based on simulation results, the maximum erosion area of the ball seat is found and the structure of ball seat is optimized. Furthermore, the validity of the simulation results is confirmed by performing an on-site experiment in a horizontal well, which shows that ball seat after structure optimization results in better erosion resistance and longer erosion life during oil and gas fracturing process. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing treatment has been widely applied to simulate oil and gas reservoirs. Ball seat is the most important component in the ball and seat multistage fracturing system. This work aims to study the proppant-carrying fluid two-phase flow and optimize the outlet structure of ball seat. Computational fluid dynamics (CFD) with standard k-ε turbulence model and particle trajectory model is used to simulate the internal flow when fracturing fluid flows across the ball seat. The influence of outlet structure on velocity streamlines, pressure drop, proppant trajectories, proppant concentration and erosion rate are investigated. Compared with the original structure, it is found that the optimized outlet structure with smooth curve can improve the flow status significantly. On-site fracturing experiments have been conducted to validate the CFD study results in a well of Sheng li Oilfield. The experimental results confirm that the optimized outlet structure contributes to lower pressure drop and better erosion resistance. Based on comprehensive analyses, it can be summarized that a general agreement between the numerical simulation and on-site fracturing experiments is obtained. Besides, the obtained results indicate that the CFD approach combining with experimental approach can be an effective method for the design of ball seat and other downhole fracturing tools. [ABSTRACT FROM AUTHOR]

With the further development of unconventional oil and gas reservoirs, multistage fracturing technique has played an important role in completion process. Wear problem has severely restricted the development of this technique. However, surface modification of fracturing tools has not been studied widely. In this study, ball seats which act as key component in multistage fracturing were nitrided in ammonia (NH 3 ) and the nitride layer characterizations were investigated. Tribological tests were carried out to study the wear resistance of specimens. The test results show that microhardness and wear resistance are improved significantly by gas nitriding treatment. Based on the comprehensive analyses of the wear morphologies and debris, wear mechanisms are identified. The on-site fracturing experiment results indicate that the ball seat shows better wear resistance after gas nitriding treatment. [ABSTRACT FROM AUTHOR]

Introduction: Studies have shown that genetic variation and environmental factors are associated with individual differences in therapeutic efficacy and side effects of opioids. However, the focus of these studies has been on a single factor of single-nucleotide polymorphisms (SNPs) or haplotypes, for which results have rarely been validated. For complex traits, such as cancer pain and opioid response, interactions between multiple genetic variation and environmental factors need to be considered to explain the opioid individual differences. Methods: We conducted an exploratory two-stage cross-sectional study with 1027 Chinese patients who were taking strong opioid medications for their cancer pain, and genotyped 110 SNPs to explore the association of SNPs, haplotypes, gene–gene and gene–environment interactions with opioid dose, pain relief, and opioid-induced constipation. Results: Due to the failure to meet Benjamini–Hochberg criteria in the discovery stage or to be validated in replication stage, no association was found between SNPs, haplotypes, paired SNP–SNP interactions or multi-dimensional gene–gene interactions and opioid response. However, for gene–environment interactions, optimal models have been constructed in all phenotypes of opioid response. Conclusions: This study reveals for the first time that construction of multidimensional gene–environment interactions enables better interpretations of the effect of genetic variation and environmental factors on the opioid response in patients with cancer pain. Trial registration: Chictr.org.cn, identifier, ChiCTR2000033576. [ABSTRACT FROM AUTHOR]

In recent years, orexin (ORX) and melanin-concentrating hormone (MCH) have been demonstrated to exert neuroprotective roles in cerebral ischemia. Hence, this study investigated the regulatory function of ORX and MCH in neurological function following ischemic stroke and explored the molecular mechanism underlying these functions. A rat model of ischemic stroke was developed by middle cerebral artery occlusion (MCAO), and Longa scoring was employed to evaluate the degree of neurological function deficit. The expression patterns of ORX and MCH were examined by real-time polymerase chain reaction in the brain tissues of rats with ischemic stroke induced by middle cerebral artery occlusion (MCAO). Moreover, electroencephalography (EEG) analysis and high-performance liquid chromatography (HPLC) were respectively performed to detect rapid-eye movement (REM) sleep, the glutamate (Glu) uptake, and the expression of γ-aminobutyric acid B receptor (GABAB). Immunoblotting was performed to test the levels of autophagic markers LC3, BECLIN-1, and p62. Immunohistochemistry (IHC) staining and TUNEL assays were respectively used to assess the autophagy and neuronal apoptosis. Results demonstrated that ORX and MCH were lowly expressed in brain of rats with ischemic stroke. ORX or MCH overexpression decreased neuronal apoptosis and autophagy, and improved the sleep architecture of post-stroke rats, while rescuing Glu uptake and GABA expression. ORX or MCH upregulation exerted protective effects on neurological function. Taken together, ORX and/or MCH protect against ischemic stroke in a rat model, highlighting their value as targets for the clinical treatment of ischemic stroke. [ABSTRACT FROM AUTHOR]

Field gas explosion experiments were conducted on two types of polyurea coated metal plates (PCMPs). The explosion resistance performance of PCMP were quantified based on failure criteria and relative deformation. The failure mechanism of PCMP was analyzed from the perspective of stress propagation and polyurea phase transition. The results showed that under the fluctuating upward gas explosion load, the dynamic response process of PCMP gradually transitioned from quasi elastic-plastic vibration to enhanced plastic deformation. The structural response of PCMP in both the elastic and elastic-plastic stages conformed to the overpressure criterion. The wave impedance mismatch existed in PCMP, and the explosion resistance of PCMP was significantly affected by polyurea layer and substrate type. The polyurea of PCMP did not enter a significant glass transition stage under gas explosion load. The weak glass transition of polyurea can maintain its elastic properties, thereby dispersing stress concentration in the substrate, and alleviating metal necking. • Systematic gas explosion tests were carried out on polyurea coated metal plates (PCMPs). • Failure criterion calculation and quantification of blast resistance of PCMP were performed. • Failure mechanism of PCMP was analyzed from the perspective of stress propagation. • The energy dissipation mechanism of polyurea was elucidated. [ABSTRACT FROM AUTHOR]