Results Of Model Research Articles

Prediction of Severe Acute Pancreatitis at a Very Early Stage of the Disease Using Artificial Intelligence Techniques, Without Laboratory Data or Imaging Tests: The PANCREATIA Study.

To evaluate machine learning models' performance in predicting acute pancreatitis severity using early-stage variables while excluding laboratory and imaging tests. Severe acute pancreatitis (SAP) affects approximately 20% of acute pancreatitis (AP) patients and is associated with high mortality rates. Accurate early prediction of SAP and in-hospital mortality is crucial for effective management. Traditional scores such as APACHE-II and BISAP are complex and require laboratory tests, while early predictive models are lacking. Machine learning (ML) has shown promising results in predictive modelling, potentially outperforming traditional methods. We analysed data from a prospective database of AP patients admitted to Vall d'Hebron Hospital from November 2015 to January 2022. Inclusion criteria were adults diagnosed with AP according to the 2012 Atlanta classification. Data included basal characteristics, current medication, and vital signs. We developed machine learning models to predict SAP, in-hospital mortality, and intensive care unit (ICU) admission. The modelling process included two stages: Stage 0, which used basal characteristics and medication, and Stage 1, which included data from Stage 0 and vital signs. Out of 634 cases, 594 were analysed. The Stage 0 model showed AUC values of 0.698 for mortality, 0.721 for ICU admission, and 0.707 for persistent organ failure. The Stage 1 model improved performance with AUC values of 0.849 for mortality, 0.786 for ICU admission, and 0.783 for persistent organ failure. The models demonstrated comparable or superior performance to APACHE-II and BISAP scores. The ML models showed good predictive capacity for SAP, ICU admission, and mortality using early-stage data without laboratory or imaging tests. This approach could revolutionise AP patients' initial triage and management, providing a personalised prediction method based on early clinical data.

The associations of n-3 fatty acid intake with handgrip strength and muscle mass indices in older adults: A cross-sectional study from UK Biobank

ObjectivesTo investigate the associations of n-3 fatty acid intake with handgrip strength and muscle mass indices in older adults. A secondary aim was to investigate whether these associations differed by physical activity status. Research methods & proceduresA cross-sectional study included 53,170 participants aged 60 years and over from the UK biobank (25,324 men and 27,846 women). The primary outcomes were grip strength index and muscle mass index, the predictor variable was n-3 fatty acid intake and the covariates were age, ethnicity, Townsend deprivation index, physical activity, multimorbidity count, total energy intake, body fat percentage and the month of assessment. Multivariable linear regression analyses were performed across 5 models. Model 1 was unadjusted; model 2 adjusted for age, ethnicity, deprivation index and month of assessment; model 3 adjusted as in model 2 plus total energy intake; model 4 as in model 2 plus multimorbidity count; and model 5 as in model 4 plus body fat percentage. ResultsIn model 5, higher n-3 fatty acid intake was positively associated with the grip strength index in women. For each additional gram of n-3 fatty acid consumed per day, there was an increase of 0.03 kg/m2 (95% CI: 0.00 to 0.06 kg/m2) in active women and 0.04 kg/m2 (95% CI: 0.00 to 0.07 kg/m2) in inactive women. However, no significant associations were observed in men, whether active (p = 0.405) or inactive (p = 0.323). Additionally, no significant associations were found between n-3 fatty acid intake and muscle mass index in either active (p = 0.858) or inactive (p = 0.250) men, or in active (p = 0.909) or inactive (p = 0.187) women. ConclusionAlthough n-3 fatty acid intake was associated with grip strength index in older women, regardless of their activity status, the magnitude of this association was very small and unlikely to be clinically relevant. Additionally, n-3 fatty acid was not associated with muscle mass index.

Study of spatial spillover effects and threshold characteristics of the influence of urbanization on grain green production efficiency in China under carbon constraints.

Food security is a basic guarantee for maintaining social stability, and improving green and healthy grain production is the way to promote food security. China is currently experiencing rapid urbanization that has an impact on grain production and security, with reduced arable land, environmental pollution, insufficient agricultural population, and inadequate resource allocation. How to deal with the relationship between urbanization and grain production becomes the key to solve this problem. Therefore, this study constructs an evaluation system of grain green production efficiency (GGPE) with non-expected output containing carbon emissions, and uses the super-efficient SBM model to measure the level of GGPE, and constructs a spatial econometric model to examine the spatial correlation and spillover effect of urbanization on GGPE; then constructs a panel threshold model to analyze the nonlinear threshold characteristics between urbanization and GGPE. It was found that (1) from 2000 to 2019, China's GGPE showed an overall upward trend, and the performance of GGPE varied among different provinces. (2) According to the results of the spatial econometric model, China's GGPE shows obvious spatial characteristics, and the level of urbanization not only directly affects regional GGPE but also can indirectly affect neighboring regions' GGPE through spatial spillover effects, and the indirect effects among regions are larger than the direct effects. (3) Spatial threshold effect results, there is a significant non-linear threshold characteristic of the impact of urbanization rate on GGPE. In terms of the influence effects of other variables, the influence of each variable on GGPE is roughly in the same direction as the influence under the spatial spillover effect, but the degree of influence is slightly different.

A novel intrasaccular aneurysm device with high complete occlusion rate: initial results in a rabbit model

BackgroundIntrasaccular flow-disrupting devices are a safe and effective treatment strategy for intracranial aneurysms. We utilized high-frequency optical coherence tomography (HF-OCT) and digital subtraction angiography (DSA) to evaluate SEAL Arc, a...

Esophageal oxyhemoglobin saturation as a resuscitative metric in hemorrhagic shock

BackgroundMixed venous saturation (SvO2) is considered the gold standard to assess the adequacy of tissue oxygen delivery (DO2) in shock states. However, SvO2 monitoring is challenging as it requires an...

Does change in trauma-related shame predict change in PTSD symptomatology?

Trauma-related shame (TR-shame) is associated with Posttraumatic Stress Disorder (PTSD) symptoms. However, research findings are inconsistent regarding the role of TR-shame in PTSD treatment. The purpose of this study was to examine if change in TR-shame predicted change in PTSD symptoms in treatment. A sample of 462 adults who received PTSD treatment at a Partial Hospitalization Program completed questionnaires measuring TR-shame (i.e., Trauma-Related Shame Inventory; TRSI) and PTSD symptoms (PTSD Checklist for DSM-5; PCL-5). Latent growth curve models were estimated to examine if the rate of change in TRSI predicted the rate of change in PCL-5 by using structural equation modeling. Furthermore, a latent regression model was estimated to predict the intercept and slope of the PCL-5. Model fits for the PCL-5 and the TRSI linear models were acceptable and both linear slopes were significant. On average, PCL-5 scores reduced 22.18 points from admission to discharge, while TRSI scores reduced 21.9 points from admission to discharge. The results of latent curve regression model suggested that the TRSI linear slope and intercept predicted the PCL-5 linear slope and intercept, respectively. Additionally, post-hoc analyses suggested that the variance in PCL-5 factors at discharge that were explained by TRSI intercept and linear slope ranged from 18.6% to 34.9%. The results of this study indicated that the rate of change in TR-shame predicted the rate of change in PTSD symptoms. Given the negative impact of TR-shame on PTSD symptoms, TR-shame should be a target in treatment for PTSD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Analytical Analysis of the Bladeless Rotor Turbine Design for Enhanced Performance

The growing desire for clean and efficient energy solutions has fueled considerable advances in turbine technology. This study proposed a conceptual approach to enhancing the performance of bladeless turbines using an analytical technique, and the analytical solutions bear a resemblance to the experimental setup for a fluid dynamics investigation of a rotor for real-flow effects. The Navier-Stokes equations for fluid flow in cylindrical coordinates were simplified to two-dimensional flow by ignoring axial direction and velocity, and direct integration was used in conjunction with series expansion solutions. The experiment data were used to verify the rotor's fluid dynamics analytical solutions. The model's result was compared to previously calculated solutions of the fluid dynamics of a disc rotor in a bladeless turbine. The disc turbine models were used to predict radial velocity, tangential velocity, pressure gradient, volumetric flow rate, rotor torque, shear stress on the inner and outer disc walls, and rotor efficiency. The model was validated using experimental data with an efficiency of 23.9%, the theoretical solution model was 34%, and the analytical efficiency was 24.3%. The efficiency comparison of the analytical solutions model to the theoretical solutions model revealed a substantial difference, however, the correlation between computed theoretical and analytical results is significant. Previous studies used computed solutions for models, but current analytical solutions outperformed them. The model's output will be valuable to engineers building the disc turbine. It demonstrates a strong link between the analytical and experimental research of the bladeless turbine.

Subsurface Imaging by a Post-Stimulation Walkaway Vertical Seismic Profile Using Distributed Acoustic Sensing at the Utah FORGE Enhanced Geothermal System Site

A 2D walkway vertical seismic profile (VSP) survey was conducted using a distributed acoustic sensing (DAS) system in southwest Utah, which is part of an enhanced geothermal system (EGS) project. The VSP was undertaken to obtain detailed structural information for a better understanding of the area’s subsurface geology and associated fracture development. By combining a 3D composite velocity model from previous studies and considering the complex geological structure beneath this region, we processed the data to create P-P depth image. We also modified the interval Q calculation using a moving window over the gauge-length corrected DAS record to generate the velocity profile and the comparable interval attenuation curve. The correlated P-P images from two DAS records successfully indicate not only the main contact between shallow unconsolidated sediments and the metamorphic basement rocks at 2650 ft (807.72 m) but also several distinct reflections related to the geological contacts. The refined velocity profiles and the depth images can provide baseline results for further seismic modeling and time-lapse imaging.

Refined implicit characterization of engineering geology with uncertainties: a divide-and-conquer tactic-based approach

In engineering geology, a reasonable assessment of the spatial distribution of uncertainty in a region is vital in guiding research, saving money, and shortening the period. However, the traditional modeling process requires a lot of manual interaction, and the uncertainty of the geological model cannot be accurately quantified and utilized. This paper proposes a novel implicit geological modeling and uncertainty analysis approach based on the triangular prism blocks, which is divided into data point acquisition, ensemble model with divide-and-conquer tactic (EMDCT), uncertainty analysis, and post-processing. By employing machine learning algorithms, the EMDCT gives superior results for implicit modeling. The sensitivity analysis of the prediction results is further evaluated via information entropy. According to the distribution of uncertainty, supplementary boreholes are selected as additional knowledge to retrain the local components of the model to enhance their performances. The implicit modeling method is applied to real hydraulic engineering problems by employing the EMDCT, and the proposed model has obvious advantages in the implicit geological characterization. The overall accuracy in the working area with sparse boreholes reaches 0.922, which is 0.013 higher than the traditional method. By evaluating the distribution of uncertainty, an accuracy of 0.962 can be achieved, which is equivalent to reducing 10 boreholes.

Empowered corrosion-resistant products through HCP crystal network: a topological assistance

Human computer interaction (HCI) aims to enhance product effectiveness and efficiency by empowering users. This research examines corrosion resistance in alloys, a concern due to technological advancements. Metals and alloys are susceptible to degradation, leading to functionality loss, structural collapse, and environmental contamination. Improving corrosion resistance is crucial for product efficiency. In this paper, HCI identifies requirements that emphasize taking an existing hexagonal closely packed (HCP) network, investigating the network for requirements in the form of vertices and edges, mapping different vertices and edges of the network graph with topological invariants, solving the network graph by invariants, and providing results for modeling and design of advanced networks and architectures. The HCI also ensures and investigates the optimization of results produced under the specifications. The study examines network graph results for irregularities, providing guidelines for engineers and manufacturers to create advanced alloy architectures with characteristics through mathematical and graphical methods.

Automatically Temporal Labeled Data Generation Using Positional Lexicon Expansion for Focus Time Estimation of News Articles

Many facts change over time, which is a fundamental aspect of our physical environment. In the case of pandemic articles, the user is not interested in the creation date of the document but in the facts and the cause of the last pandemic. Fake news can be better combated by having a document with a temporal focus. Currently, neither the sequence of events nor the temporal focus is considered when obtaining news documents. Despite the limited number of temporal aspects in the available datasets, it is difficult to test and evaluate the temporal conclusions of the model. The goal of this work is to develop a temporal focus news article retrieval model based on co-training to advance research in semi-supervised learning. A mapping of the dataset is performed using (1) the evolving focus time of news articles and (2) the semi-supervised method based on coincidence contexts for learning low-dimensional continuous vectors for learning neural contrast embedding models generating focus time-based query in sequential news articles to facilitate temporal understanding by learning low-dimensional continuous vectors. A diverse dataset of news articles is used to evaluate the effectiveness of the proposed method. With semi-supervised learning and lexicon expansion, the result of the developed model can achieve 89%. The method performed better than previous baselines and traditional machine learning models with improvements of 12.65% and 4.7%, respectively.

Acceleration mechanisms of energetic ion debris in laser-driven tin plasma EUV sources

Laser-driven tin plasmas are driving new-generation nanolithography as sources of extreme ultraviolet (EUV) radiation centered at 13.5 nm. A major challenge facing industrial EUV source development is predicting energetic ion debris produced during the plasma expansion that may damage the sensitive EUV channeling multilayer optics. Gaining a detailed understanding of the plasma dynamics and ion acceleration mechanisms in these sources could provide critical insights for designing debris mitigation strategies in future high-power EUV sources. We develop a fully kinetic model of tin-EUV sources using one-dimensional particle-in-cell simulations to study ion debris acceleration, which will be valuable for cross-validation of radiation-hydrodynamic simulations. An inverse-bremsstrahlung heating operator is used to model the interaction of a tin target with an Nd:YAG laser, and thermal conduction is included through a Monte Carlo Coulomb collision operator. While the large-scale evolution is in reasonable agreement with analogous hydrodynamic simulations, the significant timescale for collisional equilibration between electrons and ions allows for the development of prominent two-temperature features. A collimated flow of energetic ions is produced with a spectrum that is significantly enhanced at high energies compared to fluid simulations. The dominant acceleration mechanism is found to be a large-scale electric field supported mainly by the electron pressure gradient, which is enhanced in the kinetic simulations due to the increased electron temperature. We discuss the implications of these results for future modeling of tin-EUV sources and the development of debris mitigation schemes.

Minimally Invasive Delivery of Percutaneous Ablation Agent via Magnetic Colloidal Hydrogel Injection for Treatment of Hepatocellular Carcinoma.

Percutaneous thermotherapy, a minimally invasive operational procedure, is employed in the ablation of deep tumor lesions by means of target-delivering heat. Conventional thermal ablation methods, such as radiofrequency or microwave ablation, to a certain extent, are subjected to extended ablation time as well as biosafety risks of unwanted overheating. Given its effectiveness and safety, percutaneous thermotherapy gains a fresh perspective, thanks to magnetic hyperthermia. In this respect, an injectable- and magnetic-hydrogel-construct-based thermal ablation agent is likely to be a candidate for the aforementioned clinical translation. Adopting a simple and environment-friendly strategy, a magnetic colloidal hydrogel injection is introduced by a binary system comprising super-paramagnetic Fe3O4 nanoparticles and gelatin nanoparticles. The colloidal hydrogel constructs, unlike conventional bulk hydrogel, can be easily extruded through a percutaneous needle and then self-heal in a reversible manner owing to the unique electrostatic cross-linking. The introduction of magnetic building blocks is exhibited with a rapid magnetothermal response to an alternating magnetic field. Such hydrogel injection is capable of generating heat without limitation of deep penetration. The materials achieve outstanding therapeutic results in mouse and rabbit models. These findings constitute a new class of locoregional interventional thermal therapies with minimal collateral damages.

Development and Validation of an Algorithm Model for Predicting Heat Sink Effects during Pulmonary Thermal Ablation

The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols. The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time. To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and vascular structure and measure the distance between the nodule and vascular. First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the difference between the two results. In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference between the two groups (p=0.687). The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and provide more reference information for surgeons to make ablation plans to improve the ablation effect.

Role of international remittances in poverty alleviation: A panel data study of developing countries

Remittances are the funds sent by individuals to their home country from the host country and are the main source of foreign funding after FDI. Remittances are a crucial source of subsistence for the countries that are still in developing phase. Due to of the increasing scale and effect on the origin country, the international remittance flows into the developing country have gained the attention of numerous analysts from all over the world. This study examines the role of foreign remittances in poverty alleviation utilizing the panel data set of Asian countries, precisely nine developing countries. For the dependent variable this study uses poverty gap and the independent variables are Remittances as percentage of GDP, Per capita GDP, External debt as percentage of GNI and Trade openness as percentage of GDP. The findings reveal that foreign emittances have a significant role in alleviating poverty gap. The results of the random effect model showed that a 1% increase in international remittances as percentage of GDP leads to a decrease of 65.932% in poverty gap at $1.90 poverty line in the sample developing economies from 1990-2019. In addition, the results indicate, trade openness and per capita GDP decrease poverty and external debt seems to increase poverty.

Emotion language use in narratives of the 9/11 attacks predicts long-term memory.

Despite considerable cognitive neuroscience research demonstrating that emotions can influence the encoding and consolidation of memory, research has failed to demonstrate a relationship between self-reported ratings of emotions collected soon after a traumatic event and memory for the event over time. This secondary analysis of data from a multisite longitudinal study of memories of the September 11, 2001 terrorist attacks, asked the question of whether emotional language use could predict memory over time. In the 2 weeks following the 9/11 attacks, participants (N = 691; Mage = 36.8; 72% identifying as male; 76% identifying as white) wrote narratives about how they learned of the attacks and the impact of the attacks on them. Language features of these narratives were extracted using the Linguistic Inquiry Word Count program and used to predict three types of memory: (a) event memory accuracy, (b) flashbulb memory consistency, and (c) emotion memory consistency. These outcomes were assessed at the time of writing, 1, 3, and 10 years after the 9/11 attacks. Results of linear mixed-effects models indicate that greater use of negative emotion words in narratives predicts better event memory accuracy 3 and 10 years after the attacks and worse flashbulb memory consistency 10 years after the attacks. However, emotion word use does not predict emotion memory consistency across time. We also examine whether other exploratory linguistic predictors are associated with memory over time. These findings suggest that written language may serve as a potential early indicator of memory over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Detection of diseases in rice leaf using convolutional neural network with transfer learning based on ResNeXt

Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. Occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers’ income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model’s performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.

Identification and Verification of Potential Ferroptosis-Related Biomarkers in Cervical Cancer

This study screened important genes contributing to morbidity from differential ferroptosis-related genes (FRGs) in cervical cancer and to establish a risk assessment model with ferroptosis-related LncRNAs. Total RNA sequencing data were extracted from The cancer genome atlas (TCGA), Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx). By differential analysis, we identified ferroptosis-related hub genes close to prevalence of cervical cancer. According to receiver operator curves (ROC) curves, hub genes have good diagnostic performance. The diagnostic potential of hub genes for occurrence of the disease was further assessed and verified. Further, a risk-assessing model based on ferroptosis-related LncRNAs was established. Finally, the differential expressions of hub genes were verified through qRT-PCR. Five hub genes were identified, and we found through GO, KEGG and immune infiltration, that the hub genes are connection with cervical cancer. The Area Under Curve (AUC) values were all greater than 0.8 in ROC curve, and the hub genes presented differences between disease and control groups in validation set GSE29570. We created a risk assessment model with 16 ferroptosis-related LncRNAs. There was a difference in survival between high-risk and low-risk groups. The AUC result for risk assessment model reached 0.792, and there were significant expression differences of Hub genes in Huvec and Hela cells. The study screened 5 hub genes and constructed the risk-assessment model based on 16 LncRNAs associated with ferroptosis genes.

Implementation of a PAFV turbulence model for airfoil flow simulation on OpenFOAM

To further develop a more effective turbulence model and to improve the calculation accuracy of the flow around airfoil, a new PAFV turbulence model has here been constructed by using a deformation rate tensor and the grouping of an average fluctuation velocity. To evaluate the applicability of the PAFV turbulence model, the numerical calculations of flow around the airfoil have here been implemented, which was based on the OpenFOAM calculation platform. On the basis of grid independence research, the model was used to calculate the low-speed flow-around problem for the plano-convex airfoil NACA4412 and the symmetric airfoil NACA0012. It was also compared with the S-A (Spalart-Allmaras) and SST (Shear Stress Transport) k-ω turbulence models. Firstly, the maximum lift angle-of-attack case of the NACA4412 airfoil was calculated. Thereafter, numerical calculations were performed for the flow around the airfoil in the multi-angle-of-attack case of the NACA0012 airfoil. The results showed that the NACA4412 airfoil had an obviously separated vortex at the trailing edge of the airfoil at the maximum lift angle of attack. Also, there was a certain velocity loss downstream of the trailing edge, which was calculated by all three models. However, the results of the PAFV turbulence model were found to be better than those of the S-A and SST turbulence models. The three turbulence models showed comparable accuracies for the calculations of the surface pressure coefficients of the NACA0012 airfoil. However, the S-A and SST k-ω turbulence models were slightly better for the calculations of the mean velocity profiles of the NACA0012 airfoil. Also, the PAFV turbulence model was more accurate for the calculations of the lift and drag coefficients. In conclusion, the PAFV model can make effective predictions of the airfoil low-speed flow around the problem at hand, which in turn preliminarily verifies the applicability of this turbulence model for the low-speed flow around the airfoil problems.