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The epoxidation process of soybean oil (SBO) and peracid produced by 50 wt% hydrogen peroxide (H2O2) and formic acid (FA) was studied with sulfuric acid (H2SO4) as a catalyst. Three reaction parameters, including reaction temperature, FA-to-SBO ratio, and H2O2-to-SBO ratio, were investigated, along with the combined effect on oxirane value (OV). Based on response surface methodology (RSM), the Box-Behnken design (BBD) was used to optimize the process parameters. According to the results, the calculated OV (7.34%) and the experimental OV (7.31%) were signifi cantly in agreement. The product was confi rmed as epoxidized soybean oil (ESO) by IR and NMR characterization methods. These results demonstrated the reliability of RSM to optimize the SBO reaction to produce ESO with high oxygen content.

In the production process of oil and gas wells, the migration of solid particles, such as mud and sand, often leads to screen pipe clogging, resulting in production reduction or even shutdown. Controlled plasma pulse plugging removal technology is a new physical method for unplugging. Plasma, generated by the electrical explosion of the wire, then triggers a shock wave, which can remove the plugging. In this paper, the performance of controllable plasma pulse plugging removal technology is evaluated by simulating the plugging state of the downhole screen through laboratory experiments. The test results of screen plate unplugging effect evaluation fully show that the plasma pulse decongestion technology can carry out effective decongestion, can effectively reduce the pressure behind the clogging zone and increase the flow rate of the clogging zone, and is suitable for different screen plate blockage states such as mild, moderate and severe. Thus, this technology has a good application prospect. [ABSTRACT FROM AUTHOR]

The pores and fissures in loaded rock masses are the main channels for underground flow, and may cause serious accidents during the development of groundwater resources. This work presents an efficient method for analyzing the microstructure of the loaded rock mass using fractal theory and computed tomography (CT) scanning. A relation between the microstructure features of the sandstone porosity, fractal dimension, and loading stress is developed using an image identification technique. The results demonstrate that the distribution trends of sandstone samples' slice porosities in the xz- and yz-directions are nearly identical, and the distribution in the xy-direction differs significantly from those in xz- and yz-directions. The total and connected porosities increase with the increase of stress, and the change can be fitted to straight lines. The fractal dimensions of the pores change significantly with stress or loading stress in the xy-direction. [ABSTRACT FROM AUTHOR]

Previous studies have established that the selection of gas extraction borehole parameters is crucial for the effectiveness of gas extraction. To more accurately determine the reasonable extraction radius of gas extraction boreholes in the coal seam, this study was based on the actual occurrence conditions of the coal body. A coal-seam gas-seepage model, considering dynamic changes in permeability under gas–solid coupling conditions, was constructed. It was combined with FLAC3D numerical simulations to develop a borehole extraction model closer to the field's natural needs. The study revealed the influence of borehole diameter and spacing on gas extraction, obtained the radius of effect of borehole extraction, and optimized the gas extraction borehole parameters based on data simulation experiments. Multiple sets of experimental results indicated that the optimal parameters are a borehole diameter of φ = 113 mm and a borehole spacing of 5 m. Applying these parameters in on-site tests at the 14,303 working faces of a particular mine significantly improved gas extraction efficiency, with a 29.7% increase in gas extraction concentration. This verified the accuracy of the simulation results and provides a scientific basis for cost reduction and efficiency enhancement in wellbore mining. [ABSTRACT FROM AUTHOR]

Glabridin is a prenylated isoflavonoid with considerable anticancer property. Reactive oxygen species (ROS) have evolved as regulators of many cellular signaling pathways in prostate cancer (PC). However, the role of ROS signaling in the anticancer activity of glabridin has not been investigated. Here, we attempted to evaluate the effect of glabridin on PC and the involvement of ROS signaling. Intracellular ROS and mitochondrial ROS (mitoROS) production in PC cell lines, DU‐145 and LNCaP, were measured by H2DCFDA and MitoSOX Red staining, respectively. MTT assay was used to analyze the cellular viability. EdU staining assay was conducted to analyze the cell proliferation. To analyze apoptotic rate, TUNEL assay was performed. Caspase‐3 activity was detected to reflect cell apoptosis. Western blot was carried out to detect the expression levels of Akt and p‐Akt. We found that intracellular ROS and mitoROS levels were dose‐dependently upregulated after glabridin treatment in both DU‐145 and LNCaP cells, which was reversed by the treatment of ROS inhibitor, N‐acetyl‐L‐cysteine (NAC). Glabridin inhibited the cell viability and reduced the number of EdU‐positive DU‐145 and LNCaP cells, which were respectively proved by MTT assay and EdU staining assay. Glabridin promoted cell death with increased apoptotic rate and caspase‐3 activity in DU‐145 and LNCaP cells. The effects of glabridin on cell proliferation and apoptosis were reversed by NAC. Moreover, glabridin suppressed the ratio of p‐Akt/Akt, while NAC mitigated the decreased p‐Akt/Akt ratio. In addition, the effects of glabridin on cell proliferation and apoptosis were also attenuated by Akt activator, SC79. Collectively, our results demonstrated that glabridin suppressed proliferation and induced apoptosis in PC cells via regulating ROS‐mediated PI3K/Akt pathway. These findings suggested that glabridin might hold a promising prospective as a therapeutic agent against PC. [ABSTRACT FROM AUTHOR]

The distribution of beddings varies greatly in shale reservoirs. The influence of beddings on hydraulic fracture propagation has often been studied using simplified geological models, i.e., uniformly distributed bedding models. However, the propagation processes of hydraulic fractures in shale reservoirs with complicated distributed beddings remains unclear. In this research, an outcrop-data-based bedding model of a continental shale formation in the Ordos Basin, China, is built. A mathematical model for fracture propagation is built using the discrete element method and is then verified by a hydraulic fracturing experiment. Reservoir-scale simulations are employed to investigate the influence of geological factors and engineering factors on fracture geometry. The study finds that beddings have significant inhibitory effects on fracture height growth; hydraulic fractures have difficulty in breaking through zones with densely distributed beddings. If a hydraulic fracture encounters a bedding plane with a larger aperture, it is more likely to be captured and expand along the weak interface. High vertical stress difference and a high fluid injection rate can promote the vertical penetration of hydraulic fractures through beddings and activate the bedding system to yield a complex fracture network. Increments in fluid viscosity can increase the resistance of fracture propagation, thereby reducing fracture complexity. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to identify the increased value of integrating computed tomography (CT) radiomics analysis with the radiologists' diagnosis and clinical factors to preoperatively diagnose cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients. Methods: A total of 178 PTC patients were randomly divided into a training (n = 125) and a test cohort (n = 53) with a 7:3 ratio. A total of 2553 radiomic features were extracted from noncontrast, arterial contrast-enhanced and venous contrast-enhanced CT images of each patient. Principal component analysis (PCA) and Pearson's correlation coefficient (PCC) were used for feature selection. Logistic regression was employed to build clinical–radiological, radiomics and combined models. A nomogram was developed by combining the radiomics features, CT-reported lymph node status and clinical factors. Results: The radiomics model showed a predictive performance similar to that of the clinical–radiological model, with similar areas under the curve (AUC) and accuracy (ACC). The combined model showed an optimal predictive performance in both the training (AUC, 0.868; ACC, 86.83%) and test cohorts (AUC, 0.878; ACC, 83.02%). Decision curve analysis demonstrated that the combined model has good clinical application value. Conclusions: Embedding CT radiomics into the clinical diagnostic process improved the diagnostic accuracy. The developed nomogram provides a potential noninvasive tool for LNM evaluation in PTC patients. [ABSTRACT FROM AUTHOR]

The driving concept of students' sports training involves a unique activity that is often tightly correlated to students' efficiency and varies with the momentum of sports training. Supervised learning is one of the smart methods with positive results in the fields of classification techniques. Due to the excessive currency unit associated with sports, sports forecasting is a growing area that must be well predicted. Therefore, in this paper, sports training based on the supervised learning (STSLM) model has been proposed to evaluate and predict student sports efficiency. STSLM models are based on various variables, such as traditional student ratings, performance, and efficiency. The emphasis is on the efficiency of students predicting sports outcomes. STSLM defines evaluation methods, information sources, effective models for testing students' sports training, and unique challenges to forecast sports outcomes. The experimental results have been performed. The suggested STSLM model enhances the efficiency ratio of 96.3%, injury prevention level of 98.2%, fitness level of 95.5%, evaluation ratio of 98.8%, and training optimization ratio of 97.2% compared to other existing approaches. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a common chronic disease that affects a large number of people. In the real world, however, Parkinson's disease can result in a loss of physical performance, which is classified as a movement disorder by clinicians. Parkinson's disease is currently diagnosed primarily through clinical symptoms, which are highly dependent on clinician experience. As a result, there is a need for effective early detection methods. Traditional machine learning algorithms filter out many inherently relevant features in the process of dimensionality reduction and feature classification, lowering the classification model's performance. To solve this problem and ensure high correlation between features while reducing dimensionality to achieve the goal of improving classification performance, this paper proposes a recurrent neural network classification model based on self attention and motion perception. Using a combination of self-attention mechanism and recurrent neural network, as well as wearable inertial sensors, the model classifies and trains the five brain area features extracted from MRI and DTI images (cerebral gray matter, white matter, cerebrospinal fluid density, and so on). Clinical and exercise data can be combined to produce characteristic parameters that can be used to describe movement sluggishness. The experimental results show that the model proposed in this paper improves the recognition performance of Parkinson's disease, which is better than the compared methods by 2.45% to 12.07%. [ABSTRACT FROM AUTHOR]

Sports injuries will have an impact on the consistency and systemicity of the training process, as well as athlete training and performance improvement. Many talented athletes have had their careers cut short due to sports injuries. Preventing sports injuries is the best way for basketball players to reduce sports injuries. Many coaches and athletes on sports teams, on the other hand, are unaware of the importance of sports injury prevention. They only realize that the body's sports functions are abnormal when it suffers from sports injuries. As a result, this paper proposes a gray theory neural network-based athlete injury prediction model. First, from the standpoint of a single model, the improved unequal interval model is used to predict sports injury by optimizing the unequal interval model in gray theory. The findings show that it is a good predictor of sports injuries, but it is a poor predictor of the average number of injuries. Following that, in order to overcome the shortcomings of a single model, a gray neural network combination model was used. A combination model of the unequal time interval model and BP neural network was determined and established. The prediction effect is significantly improved by combining the gray neural network mapping model and the coupling model to predict the two characteristics of sports injuries. Finally, simulation experiments show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

The study of motor skills is an essential course item for college students. Many college teachers try to introduce the flipped classroom mode into the teaching of motor skills. However, the influence of this mode on the learning effect of college students' motor skills is uncertain. This paper builds a research model on the basis of literature review. This model is used to analyze the influence factors of flipped classroom on the learning effect of college students' motor skills. Through questionnaire survey and empirical analysis, we verified the research hypothesis of each factor. The results show that video technical action display, video feedback of students' technical action, and teaching interaction have a significant positive impact on the learning effect of college students' motor skills in the flipped classroom, while video theory teaching has no significant impact. This study expands the application of flipped classroom in the field of motor skills and enriches the relevant literature of flipped classroom mode. Based on the findings of this study, we make the following recommendations: (1) teachers can use the way of video recording to display technical actions of motor skills, (2) students can use video feedback to communicate with the teacher about the learning of motor skills, and (3) flipped classroom needs to pay attention to the teaching interaction between teachers and students. [ABSTRACT FROM AUTHOR]