syedzayyan/paper_mill_benchmark huggingface dataset:
Hemodynamic-Based Evaluation on Thrombosis Risk of Fusiform Coronary Artery Aneurysms Using Computational Fluid Dynamic Simulation Method

Coronary artery aneurysms (caas) have been reported to associate with an increased risk for thrombosis.Distinct to the brain aneurysm, which can cause a rupture, caa’s threat is more about its potential to induce thrombosis, leading to myocardial infarction.Case reports suggest that thrombosis risk varied with the different caa diameters and hemodynamics effects (usually wall shear stress (wss), oscillatory shear index (osi), and relative residence time (rrt)) may relate to the thrombosis risk.However, currently, due to the rareness of the disease, there is limited knowledge of the hemodynamics effects of caa.The aim of the study was to estimate the relationship between hemodynamic effects and different diameters of caas.Computational fluid dynamics (cfd) provides a noninvasive means of hemodynamic research.Four three-dimensional models were constructed, representing coronary arteries with a normal diameter (1x) and caas with diameters two (2x), three (3x), and five times (5x) that of the normal diameter.A lumped parameter model (lpm) which can capture the feature of coronary blood flow supplied the boundary conditions.Wss in the aneurysm decreased 97.7% apparently from 3.51 pa (1x) to 0.08 pa (5x).Osi and rrt in the aneurysm were increased apparently by two orders of magnitude from 0.01 (1x) to 0.30 (5x), and from 0.38 pa−1 (1x) to 51.59 pa−1 (5x), separately.Changes in the local volume of the caa resulted in dramatic changes in local hemodynamic parameters.The findings demonstrated that thrombosis risk increased with increasing diameter and was strongly exacerbated at larger diameters of caa.The 2x model exhibited the lowest thrombosis risk among the models, suggesting the low-damage (medication) treatment may work.High-damage (surgery) treatment may need to be considered when caa diameter is 3 times or higher.This diameter classification method may be a good example for constructing a more complex hemodynamic-based risk stratification method and could support clinical decision-making in the assessment of caa.

Journal: Complexity (2020)
Publication types: Research Article;
Function Extraction Based on CFPS and Digital Financial Index: Data Mining Techniques for Prognosis of Operational Risks of Financial Institutions

Financial deregulation, financial globalization, and the increasing variety and technological sophistication of the commodities offered by financial services have made the operations of financial institutions more complex.Compared with credit risk and market risk, financial institutions’ transaction risk management plays an increasingly important role in financial practice.As an emerging technology, big data mining technology has a unique advantage in optimizing the processing and management of large amounts of data.Big data mining technology not only has the common functions of finding, comprehensively managing all kinds of information, collecting and analyzing data, and conducting statistics but also should have the ability to process information that is hidden and useful in the database through data mining technology.Based on cfps and data mining technology, this paper analyzes the operational risk of financial institutions, analyzes the causes of the operational risk of financial institutions, discusses the measures to avoid the operational risk of financial institutions, and draws corresponding conclusions.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
FMT Selector: Fourier-Mellin Transformer with High Speed Rotating in Ship Target Detection and Tracking Based on Internet of Things and Wireless Sensor Network

The terminal guidance tracking always has high speed rotating and scale changing.It makes a large margin of error in current tracking algorithms, which can transmit wrong position in the wireless sensor networks of the internet of things.In this paper, we propose a selector based on fourier‐mellin transformer, which can accurately track the target with high speed rotating.Firstly we use existing detector to get all proposal target per frame.Then, we use fourier‐mellin matching to select the best target in the new frame.Our dataset is the ship video which is created by hrsc2016 in rotating.Experimental results show that the accuracy of this method is up to 89.8%.Compared with other traditional tracking algorithms, it has a leap forward.The fmt selector can be applied to the back end of the detector, and it can achieve a feasible effect in the field of terminal guidance tracking.

Journal: Journal of Sensors (2023)
Publication types: Research Article;
Flood Simulation Analysis of the Biliu River Basin Based on the MIKE Model

The biliu river is the largest river in dalian.The occurrence of floods and droughts in this basin has extremely important impacts on local industry, agriculture, and urban development.For a long time, the annual distribution of precipitation in the biliu river basin is extremely uneven, the river runoff varies greatly from year to year and season to year, floods and droughts occur frequently, and serious soil erosion results in fragile ecological environment and severe shortage of water resources.In this paper, the spatial and temporal changes of rainfall and runoff in the biliu river basin are studied through the coupling of the mike 11 model and the mike she model.The hydrological changes in the biliu river basin are simulated.The coupled model is verified by monthly runoff data from 1996 to 2015, and the simulation values are found to be true.The values match well.Based on the cyclical pattern of precipitation and runoff in the biliu river basin, the rainfall and runoff data in the biliu river basin from 2016 to 2030 are derived.The mike she/mike 11 coupling model is used to predict the biliu river from 2016 to 2030.The results show that flood disasters are expected to occur in august 2020, july 2025, and july 2030, which can provide a basis for hydrological management in the biliu river basin.

Journal: Complexity (2021)
Publication types: Research Article;
FH-YOLOv4 with Constrained Aspect Ratio Loss for Video Face Detection and Public Safety

Video face detection is a crucial first step in many facial recognition and face analysis systems.It should serve postprocessing steps as much as possible while satisfying high‐accuracy real‐time detection.In this paper, we first introduce the constrained aspect ratio loss (carloss) for better facial boxes regression and incorporate it into the modified fh‐yolov4, then the io u tracker‐based video face image deduplication algorithm is proposed on the detection level.Extensive experiments and comparative tests show the effectiveness of our method.

Journal: Discrete Dynamics in Nature and Society (2022)
Publication types: Research Article;
Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

The application of traditional feature extraction methods in subway mechanical fault diagnosis has attracted the attention of researchers.Based on the 5g intelligent sensor network signal processing theory, this paper constructs a fault diagnosis model for subway machinery and equipment and analyzes the effectiveness of the information redundancy calculation method proposed in this paper by using the on-site vibration data of subway units.The model is obtained by analyzing the entropy characteristics of the vector vibration signal in each bearing section of the subway machinery and equipment.The entropy value quantitatively reflects the vibration complexity of the rotor in the section from different angles and solves the problem of data quantitative analysis.In the simulation process, based on the 5g intelligent sensor network signal and the fuzzy mean clustering information fusion method, in the process of identifying the fault state of the subway unit, a relatively optimistic identification result was obtained.The experimental results show that, no matter whether the gaussian kernel or the polynomial kernel is selected, the number of kernel principal components whose cumulative contribution rate is greater than 0.85 decreases with the increase of the kernel parameter, and the fault identification support rate is 85%, that is, the diagnosis results of 3 sets of sample data are nonrolling element faults, which significantly improves the performance of the bearing.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
Exploration on the Role of Transformational Leadership Linkage Psychological Empowerment in Stimulating Employees’ Innovation Behavior in an Uncertain Environment: Based on Entrepreneurship Perspective

Employee innovative behavior (eib) in a stable environment is often based on the company’s planned policy orientation and has higher innovation performance under the transactional leadership behavior characterized by task orientation.In an uncertain environment, transformational corporate leadership behaviors will stimulate eib.The essence of entrepreneurship is innovation.Enterprises provide employees with spiritual support through entrepreneurship, which can generate more effective long-term incentives for employees.From the perspective of entrepreneurship, based on the uncertainty-identity theory (uit), through the analysis of 287 pairs of valid sample data from 7 companies, it is found that environmental uncertainty (eu) has a positive moderating effect on the relationship between transformational leadership (tl) and psychological empowerment (pe).Pe positively promotes eib.The indirect effect size of pe is 0.2221, 0.3495, and 0.4450 from low to high, respectively.It is inferred that the interaction between tl and eu can positively promote eib through the mediating effect of pe.The research conclusions expand the boundary conditions of the relationship between tl and eib and innovatively provide theoretical guidance for the management of eib from the perspective of entrepreneurship in an uncertain environment.

Journal: Occupational Therapy International (2022)
Publication types: Research Article;
Evaluation of MWCNT Particles-Reinforced Magnesium Composite for Mechanical and Catalytic Applications

Aluminum, magnesium, and copper materials must have increased mechanical strength with enhanced wear and corrosion resistance.Substantial research focused on reinforcing hard particles into low‐strength materials using stir casting or powder metallurgy.This work is intended to develop the magnesium hybrid matrix with the dispersion of boron carbide (b 4 c) and multiwall carbon nanotubes (mwcnts).Hybrid magnesium composites are prepared, although the powder metallurgy route considers different process parameters.Statistical analysis such as taguchi l16 orthogonal array is involved in this work.It is used to find the magnesium hybrid samples’ minimum and maximum wear, corrosion, and microhardness levels.Powder metallurgy parameters are b 4 c (3%, 6%, 9%, and 12%), mwcnt (0.2%, 0.4%, 0.6%, and 0.8%), ball milling (1, 2, 3, and 4 h), and sintering (3, 4, 5, and 6 h).The ball milling parameters are extremely influenced in the wear test analysis.Minimum wear losses are obtained as 0.008 g by influencing the 4 h ball milling process.Similarly, 3 h of sintering time offered a minimum corrosion rate of 0.00078 mm/yr.In microhardness analysis, the percentage of mwcnts is highly implicated in narrow hardness resulting in the hardness value of 181.The hardness value is recorded using 0.2% mwcnts in the magnesium alloy az80.

Journal: Bioinorganic Chemistry and Applications (2022)
Publication types: Research Article;
Evaluation of Innovation Efficiency of High-Tech Enterprise Knowledge Supply Chain Based on AHP-DEA

This paper introduces a qualitative analysis on the efficiency evaluation of the knowledge supply chain by combining the analytic hierarchy process (ahp) with data envelopment analysis (dea), drawing on existing literature to determine the index weight through the scoring of industry experts, and selecting appropriate input and output indicators to construct a knowledge supply chain efficiency evaluation system.The system was then applied to the supply chain of a number of high‐tech enterprises.The results identified innovation efficiency differences of the knowledge supply chain in these enterprises, along with best practices and suggestions for the current knowledge supply chain efficiency.

Journal: Journal of Mathematics (2022)
Publication types: Research Article;
Estimation and Analysis of Sports Energy Consumption Based on Gait Tactile Parameters

The morbidity of obesity and related metabolic syndrome is on the rise, which may be related to the decrease of physical activity.Therefore, keeping energy balance is the basic premise to prevent multiple metabolic syndromes, and the research on the composition and application of energy consumption has become a hot spot.The combination of expectation-maximization algorithm and map reduce computing model realizes the migration of traditional algorithm to “cloud computing” platform.The physical fitness evaluation algorithm based on collaborative filtering is constructed, and a gait tactile recognition algorithm is proposed by feature selection based on the mems sensor.Finally, the algorithm is tested, and a conclusion is drawn.This algorithm is effective in monitoring and recognizing human gait.With the increase of weightlessness characteristics, the sensitivity of detection remains unchanged, and the specificity will increase.In a word, it is scientific and effective.Thus, the reference for establishing the index system of tactile biomechanical parameters of adolescent gait and studying the low-cost and portable energy measurement method of multiparameter indexes is provided.

Journal: Discrete Dynamics in Nature and Society (2021)
Publication types: Research Article;
Enhance-Net: An Approach to Boost the Performance of Deep Learning Model Based on Real-Time Medical Images

Real‐time medical image classification is a complex problem in the world.Using io t technology in medical applications assures that the healthcare sectors improve the quality of treatment while lowering costs via automation and resource optimization.Deep learning is critical in categorizing medical images, which is accomplished by artificial intelligence.Deep learning algorithms allow radiologists and orthopaedic surgeons to make their life easier by providing them with quicker and more accurate findings in real time.Despite this, the classic deep learning technique has hit its performance limits.For these reasons, in this research, we examine alternative enhancement strategies to raise the performance of deep neural networks to provide an optimal solution known as enhance‐net.It is possible to classify the experiment into six distinct stages.Champion‐net was chosen as a deep learning model from a pool of benchmark deep learning models (efficient net: b0, mobile net, res net‐18, and vgg‐19).This stage helps choose the optimal model.In the second step, champion‐net was tested with various resolutions.This stage helps conclude dataset resolution and improves champion‐net performance.The next stage extracts green channel data.In the fourth step, champion‐net combines with image enhancement algorithms clahe, hef, and um.This phase serves to improve enhance‐performance.The next stage compares the enhance‐net findings to the lightness order error (lo e).In enhance‐net models, the current study combines image enhancement and green channel with champion‐net.In the final step, radiologists and orthopaedic surgeons use the trained model for real‐time medical image prediction.The study effort uses the musculoskeletal radiograph‐bone classification (mura‐bc) dataset.Classification accuracy of enhance‐net was determined for the train and test datasets.These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively.The 96.74% accuracy was achieved during real‐time testing with the unseen dataset.

Journal: Journal of Sensors (2023)
Publication types: Research Article;
Empirical Study on the Grain Output Based on Regression Analysis

Based on a literature review of influencing factors and forecasting methods for grain production, the empirical analysis of the influencing factors of china’s grain output is performed using the full subset regression method, the ridge regression method, and the lasso regression method.The results show that (1) the increase in the sown area of grain crops is the main reason for the increase in grain output, (2) the use of agricultural fertilizers and the increase in rural electricity consumption are the driving factors for the increase in grain output, (3) the impact of total power of agricultural machinery is limited, and (4) natural disasters have a certain negative impact on food production.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
Empirical Analysis for Improving Food Quality Using Artificial Intelligence Technology for Enhancing Healthcare Sector

Artificial intelligence or ai has a wide range of applications in healthcare and food industries.Ai helps in different ways in medical industries, such as analysing the disease progression rate, effective prediction of treatment method, and proper disease diagnosis.Advantages of artificial intelligence in the food business include enhanced customer accessibility, improved technological innovation, readily accessible client requirements and comments, strategic advantage through unique products, and plenty others.Different ai technologies such as “machine learning (ml),” “neural language processing (nlp),” “rule-based expert systems (ress),” “deep learning (dl),” and so on are used in healthcare and food industries for big “medical data” analysis.This study has applied three critical variables to measure the application of ai in enhancing food quality (viz., usage of machine learning models, nlp models, etc.).This study has stated that these models support in enhancing the overall food quality in an effective manner.The present research analyses the importance of these ai technologies in enhancing service quality in healthcare and food industries.A primary survey-based data analysis has been done with 153 individuals taken from healthcare industries.Moreover, statistical analysis has been done in this research with spss software.Four independent variables are taken in this research, which are ml, nlp, res, and dl.The service quality of healthcare has been taken as a dependent variable, and the effect of independent variables on “enhancing healthcare service” has been analysed.Secondary thematic analysis has been done to justify primary data.The results show that 43.79% of the individuals have supported dl and 56.86% have supported the treatment prediction ability ai.37.9% of the individuals have also supported ai over traditional medications.Further analysis has shown that independent variables ml, dl, nlp, and res have a strong positive correlation with improving sq.These results have been justified by secondary journals, and it is proved that ai technologies enhance the service quality in healthcare and food sectors.

Journal: Journal of Food Quality (2022)
Publication types: Research Article;
Efficacy of High-Quality Nursing Service for the Patients during the Anesthesia Recovery Period: A Meta-Analysis

To investigate the efficacy of high-quality nursing service for the patients during the anesthesia recovery period.Methods.We used the national library of medicine (pub med), cochrane library of excerpta medica database (embase), china national knowledge infrastructure (cnki), wanfang, and very important person (vip) databases for conducting a systematic literature study.We employed the fixed-effects model for evaluating the standardized mean differences (smds) with 95% confidence intervals (cis).The sensitivity and publication bias were estimated for determining the efficacy of high-quality nursing services during the recovery period of anesthesia.Results.In our study, the result showed that the efficiency of recovery time of spontaneous respiration was significantly improved in the experimental group ( smd = − 1.48 , 95 % ci = − 1.62 , − 1.34 ).In this analysis, the extubation time of the experimental group was lower than that of the control group.In control group [ wmd = − 15.54 , 95% ci (-21.24, -9.83), p &lt; 0.00001 ], the improvement of extubation time was more obvious on high-quality nursing.Moreover, the incidence of agitation in the experimental group was lower than that of the control group, and the score of nursing satisfaction was higher than that in the control group ( p = 0.01 ).The funnel plots identified no publication bias during the identification of efficacy.Conclusions.The high-quality nursing care for patients during the resuscitation period can shorten the recovery time of their self-consciousness and self-breathing, reduce the occurrence rate of restlessness, improve patients’ anxiety and depression, reduce complications, and play a certain clinical application effect.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Meta-Analysis;
Effects of Stone Removal via Different Approaches in the Treatment of Incarcerated Upper Ureteral Calculi: A Comparative Study

The aim of this study is to investigate the clinical effects of percutaneous nephrolithotomy and transurethral ureteroscopic lithotripsy in the treatment of incarcerated upper ureteral calculi.Methods.This study retrospectively reviewed 400 patients with incarcerated upper ureteral calculi admitted to the hospital from january 2016 to december 2021.Among them, 200 patients treated with percutaneous nephrolithotomy were included in the percutaneous group and 200 patients treated with transurethral ureteroscopic lithotripsy were included in the transurethral group.Perioperative indicators and stone clearance rates on day 7 and 1 month after operation and the reoperation rate were compared between the two groups.The incidence of postoperative complications was recorded.Results.The operation time and postoperative hospital stay of the percutaneous group were longer than those of the transurethral group ( p &lt; 0.05 ).There was no significant difference in intraoperative blood loss, 24 h postoperative pain score, stone clearance rates on day 3 and day 14 after operation, or the reoperation rate between the two groups ( p &gt; 0.05 ).Postoperative complications in the two groups were mainly grade i and ii.The total incidence of complications in the percutaneous group was significantly lower than that in the transurethral group ( p &lt; 0.05 ).Conclusion.Both percutaneous nephrolithotomy and transurethral ureteroscopic lithotripsy are effective in the treatment of incarcerated upper ureteral calculi.The former can reduce the incidence of postoperative complications, but the operation time and postoperative hospital stay are longer.

Journal: Emergency Medicine International (2022)
Publication types: Research Article;