Particles migrating and plugging mechanism in loosen sandstone heavy oil reservoir and the strategy of production with moderate sanding

Fine rock particles is easy to be suspended and carried in loosen sandstone heavy oil reservoir due to the higher density and viscosity of heavy oil. The sand particles settle down, bridge and clog in pore and throat, as the result, the filtration resistance in reservoir will be redistributed. It significantly impacts on the well productivity. In this paper, the process of sand particles transporting and clogging in tunnels of rock is observed utilizing a microscopic visualization model with the unconsolidated sandpack. Furthermore, the mechanism of fine particles migration and clogging and the effects to percolation capacity of porous medium is investigated through the dynamic permeability changes in the weak-consolidated sandpack tube is monitored under different conditions of particles suspended fluid injection. It is shown that the performance of permeability decline with particles migration is affected by the size and sorting of mobile particles and throats, concentration of suspended particles, total amount of particles and the pressure drawdown or fluid flowing velocity, the maximum permeability reduction and the clogging transition time is determined by the minimum size of bridging particles. As a field application example, the strategy of production with moderate sanding in loosen sandstone heavy oil reservoir is discussed at the end of this pape

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

IntroductionThe identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.MethodsTo address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.Results and discussionThe MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking

Gut microbiota alterations are associated with functional outcomes in patients of acute ischemic stroke with non-alcoholic fatty liver disease

IntroductionPatients with acute ischemic stroke (AIS) with non-alcoholic fatty liver disease (NAFLD) frequently have poor prognosis. Many evidences suggested that the changes in gut microbiota may play an important role in the occurrence and development of AIS patients with NAFLD. The purpose of this study was to explore microbial characteristics in patients of AIS with NAFLD, and the correlation between gut microbiota and functional outcomes.MethodsThe patients of AIS were recruited and divided into NAFLD group and non-NAFLD group. The stool samples and clinical information were collected. 16 s rRNA sequencing was used to analyze the characteristics of gut microbiota. The patients of AIS with NAFLD were followed-up to evaluate the functional outcomes of disease. The adverse outcomes were determined by modified Rankin scale (mRS) scores at 3 months after stroke. The diagnostic performance of microbial marker in predicting adverse outcomes was assessed by recipient operating characteristic (ROC) curves.ResultsOur results showed that the composition of gut microbiota between non-NAFLD group and NAFLD group were different. The characteristic bacteria in the patients of AIS with NAFLD was that the relative abundance of Dorea, Dialister, Intestinibacter and Flavonifractor were decreased, while the relative abundance of Enorma was increased. Moreover, the characteristic microbiota was correlated with many clinical parameters, such as mRS scores, mean arterial pressure and fasting blood glucose level. In addition, ROC models based on the characteristic microbiota or the combination of characteristic microbiota with independent risk factors could distinguish functional dependence patients and functional independence patients in AIS with NAFLD (area under curve is 0.765 and 0.882 respectively).ConclusionThese findings revealed the microbial characteristics in patients of AIS with NAFLD, and further demonstrated the predictive capability of characteristic microbiota for adverse outcomes in patients of AIS with NAFLD

Partial Oxygen Pressure Affects the Expression of Prognostic Biomarkers HIF-1 Alpha, Ki67, and CK20 in the Microenvironment of Colorectal Cancer Tissue

Hypoxia is prognostically important in colorectal cancer (CRC) therapy. Partial oxygen pressure (pO2) is an important parameter of hypoxia. The correlation between pO2 levels and expression levels of prognostic biomarkers was measured in CRC tissues. Human CRC tissues were collected and pO2 levels were measured by OxyLite. Three methods for tissue fixation were compared, including formalin, Finefix, and Finefix-plus-microwave. Immunohistochemistry (IHC) staining was conducted by using the avidin-biotin complex technique for detecting the antibodies to hypoxia inducible factor-1 (HIF-1) alpha, cytokeratin 20 (CK20), and cell proliferation factor Ki67. The levels of pO2 were negatively associated with the size of CRC tissues. Finefix-plus-microwave fixation has the potential to replace formalin. Additionally, microwave treatment improved Finefix performance in tissue fixation and protein preservation. The percentage of positive cells and gray values of HIF-1 alpha, CK20, and Ki67 were associated with CRC development (P<0.05). The levels of pO2 were positively related with the gray values of Ki67 and negatively related with the values of HIF-1 alpha and CK20 (P<0.05). Thus, the levels of microenvironmental pO2 affect the expression of predictive biomarkers HIF-1 alpha, CK20, and Ki67 in the development of CRC tissues

FULLY-AUTOMATED QUANTITATIVE ANALYSIS OF CARDIAC AND LUNG DISEASES FROM THORACIC LOW-DOSE CT IMAGES

Quantitative image biomarkers are emerging as a method for precision medical diagnosis. Fully-automated computer algorithms are explored to provide clinically useful biomarker measurements for the assessment of cardiovascular and lung diseases from low-dose thoracic computed tomography (CT) images. The recent regulatory approval of annual lung cancer screening (LCS) provides the opportunity for the application of these methods to a large at-risk population that will already be receiving annual low-dose chest CT scans. These computer algorithms must specifically address the high image noise levels concordant with the low-dose imaging protocol. Quantitative evaluation of cardiovascular disease is facilitated by automated segmentations of cardiac organs (aorta, heart region, pulmonary trunk); primarily coronary artery calcification (CAC), a major indicator of coronary heart diseases, is scored. For lung disease assessment, the automated detection of interstitial lung disease (ILD) at its earliest detectable stage is performed. In addition, CT image quality (noise and calibration) is automatically assessed from segmented homogeneous regions for quality control and increased measurement precision. Automated CAC scores have shown a 0.90 correlation with reference measurements provided by radiologists. The automated ILD detection algorithm is able to distinguish between early-stage ILD and normal cases with an Area Under the ROC curve of 0.95. The image quality assessment method has also shown to be repeatable and robust when evaluated on phantom images and a large LCS cohort. This research advances the state-of-the-art of computer algorithms for precise region segmentation and biomarker measurements that permit the evaluation of cardiac and lung health in the context of LDCT. The successful outcomes of these algorithms have demonstrated the possibility of automated chest health monitoring on an annual basis for a large population through the LCS process

Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning

Crown rot is one of the major stubble soil fungal diseases that bring significant yield loss to the cereal industry. The most effective crown rot management approach is removal of infected crop residue from fields and rotation of nonhost crops. However, disease screening is challenging as there are no clear visible symptoms on upper stems and leaves at early growth stages. The current manual screening method requires experts to observe the crown and roots of plants to detect disease, which is time-consuming, subjective, labor-intensive, and costly. As digital color imaging has the advantages of low cost and easy use, it has a high potential to be an economical solution for crown rot detection. In this research, a crown rot disease detection method was developed using a smartphone camera and machine learning technologies. Four common wheat varieties were grown in greenhouse conditions with a controlled environment, and all infected group plants were infected with crown rot without the presence of other plant diseases. We used a smartphone to take digital color images of the lower stems of plants. Using imaging processing techniques and a support vector machine algorithm, we successfully distinguished infected and healthy plants as early as 14 days after disease infection. The results provide a vital first step toward developing a digital color imaging phenotyping platform for crown rot detection to enable the management of crown rot disease effectively. As an easy-access phenotyping method, this method could provide support for researchers to develop an efficiency and economic disease screening method in field conditions

LOW-FREQUENCY INTERNAL FRICTION OF As-CAST MANGANESE-COPPER ALLOYS

Low-frequency internal friction and elastic modulus were studied for cast manganes-copper alloys with Mn-content from 60 to 90%. Transformation peak and relaxation peak (due to twin boundaries) were observed for all the cast alloys in the temperature range from -150 to 300°C. The results are compared with those of homogenized specimens

The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat

Crown rot disease is caused by Fusarium pseudograminearum and is one of the major stubble-soil fungal diseases threatening the cereal industry globally. It causes failure of grain establishment, which brings significant yield loss. Screening crops affected by crown rot is one of the key tools to manage crown rot, because it is necessary to understand disease infection conditions, identify the severity of infection, and discover potential resistant varieties. However, screening crown rot is challenging as there are no clear visible symptoms on leaves at early growth stages. Hyperspectral imaging (HSI) technologies have been successfully used to better understand plant health and disease incidence, including light absorption rate, water and nutrient distribution, and disease classification. This suggests HSI imaging technologies may be used to detect crown rot at early growing stages, however, related studies are limited. This paper briefly describes the symptoms of crown rot disease and traditional screening methods with their limitations. It, then, reviews state-of-art imaging technologies for disease detection, from color imaging to hyperspectral imaging. In particular, this paper highlights the suitability of hyperspectral-based screening methods for crown rot disease. A hypothesis is presented that HSI can detect crown-rot-infected plants before clearly visible symptoms on leaves by sensing the changes of photosynthesis, water, and nutrients contents of plants. In addition, it describes our initial experiment to support the hypothesis and further research directions are described