bluuebunny/crossref_metadata_2025 huggingface dataset:
Global Exponential Stability of Both Continuous-Time and Discrete-Time Switched Positive Time-Varying Delay Systems with Interval Uncertainties and All Unstable Subsystems
Authors: Mouktonglang Thanasak , Yimnet Suriyon
Published: May 2022

The global stability problem for a class of linear switched positive time-varying delay systems (LSPTDSs) with interval uncertainties by means of a fast average dwell time (FADT) switching is analyzed in this paper. A distinctive feature of this research is that all subsystems are considered to be unstable. Both the continuous-time and the discrete-time cases of LSPTDSs with interval uncertainties and all unstable subsystems (AUSs) are investigated. By constructing a time-scheduled multiple copositive Lyapunov-Krasovskii functional (MCLKF), novel sufficient conditions are derived within the framework of the FADT switching to guarantee such systems in the case of continuous-time to be globally uniformly exponentially stable. Based on the above approach, the corresponding result is extended to the discrete-time LSPTDSs including both interval uncertainties and AUSs. In addition, new stability criteria in an exponential sense are formulated for the studied systems without interval uncertainties. The efficiency and validity of the theoretical results are shown through simulation examples.
Prediction of Renal Function Damage in Patients with Essential Hypertension Based on Stepwise Regression Equation Scanning by AASI
Authors: Wu Yaqiong , Ma Guangyu , Sun Hongzhen , Zhang Sijie , Li Xingtao
Published: May 2022

Detection of arterial stiffness is an important method to predict the occurrence of hypertension complications and to screen patients with high cardiovascular risk. In order to predict the damage of AASI to the renal function of patients with essential hypertension, the prediction of AASI based on stepwise Regression equation scanning for renal function damage in patients with essential hypertension is proposed. Measure the 24 h ambulatory blood pressure of the selected subjects, establish a linear Regression equation scanning, and calculate the slope of the straight line, and finally, the slope is AASI. According to the quartiles, AASI is divided into four parts:

group
 
I
<
0.53

(

n
=
49

);

0.53
≤
group
 
II
<
0.60

(

n
=
51

);

0.60
≤
group
 
III
<
0.69

(

n
=
48

);

group
 
IV
≥
0.69

(

n
=
44

). Experiment result shows the following: with the increase of AASI, cystatin (CysC) also increased significantly, while CysC-eGFR decreased significantly (

P
<
0.05

). Compared with groups I, II, and III, Scr and CysC in group IV increased (

P
<
0.05

), and Ccr, CysC-eGFR, and (CKD-EPI)-eGFR all decreased (

P
<
0.05

). AASI is positively correlated with CysC performance, and the correlation coefficient

r

is 0.637. It is negatively correlated with Ccr performance, and

r

is -0.361. It is negatively correlated with CysC-eGFR, and

r

is -0.698. And it is negatively correlated with (CKD-EPI)-eGFR, and

r

is -0.331. Age and 24 h PP also showed an increasing trend with the increase of AASI, and it suggests that age may be an influencing factor that promotes kidney damage caused by hypertension; it also suggests that AASI can be used as a new indicator of arterial compliance; AASI is linearly related to various indicators of renal damage and can be used as a predictive indicator of renal damage caused by essential hypertension; cystatin C and the estimated glomerular filtration rate CysC-eGFR based on cystatin C are better than other indicators reflecting glomerular filtration rate, more sensitively assess the degree of early renal damage. Obesity may also be a factor that promotes kidney damage caused by hypertension.
Practice of Artificial Intelligence and Virtual Reality Technology in College English Dialogue Scene Simulation
Authors: Luo Xiaoxia
Published: May 2022

English is regarded as one of the most important business languages in the world. Various technologies have been developed to aid language acquisition in this field. The academic and corporate worlds are both responding favorably to technology intrusion in business communication abilities. The new normal condition is presenting a plethora of options to study a language, particularly English for nonnative speakers, using one of the immersive platforms, virtual reality. Striving people must improve their fluency and proficiency in order to stay competitive in the commercial sector. Learners might benefit from considering business-specific English in this situation. The research examines how virtual reality (VR) allows learners to experience business-related situations in a hands-on manner. It also addresses scenarios in which learners might maximize their professional potential. A mixed methodology study demonstrates how virtual reality technologies may be used to enhance English communication abilities. It discusses how different VR applications may pique learners’ attention and passion, as well as several strategies for improving Business English.
Research on Online Learners’ Course Recommendation System Based on Knowledge Atlas in Smart Education Cloud Platform
Authors: Feng Jianwen , Lin Xuan
Published: May 2022

At present, there are still some problems in the application of recommendation algorithm in course, such as data sparseness, lack of knowledge logical relationship, and lack of explanation. Therefore, in this paper, the recommendation system of course for online learners based on knowledge map is implemented where different functional modules are designed, and the recommendation model is optimized based on the loss function. In addition, a recommendation algorithm is designed, which selects the data set, tests the algorithm based on knowledge map, and mainly evaluates the performance from the rate of click-through predicted evaluation, accuracy rate, and recall rate. The results show that the overall performance of online learner course recommendation system based on knowledge is excellent.
Study on Dynamic Performance of Standard Sand Based on KTL Dynamic Triaxial Apparatus
Authors: Li Bo , Li Xiaofei , Xing Xueyang
Published: May 2022

The determination of dynamic properties is of great scientific significance to the constitutive modeling and parameter determination of large deformation for sand liquefaction. For Fujian standard sand, a new high-precision KTL bidirectional dynamic triaxial test system was used to carry out liquefaction tests under equal consolidation and different equal amplitude cyclic stresses. The dynamic characteristics of Fujian standard sand, such as dynamic stress and dynamic strain test results, pore water pressure model, effective stress path, and stress-strain relationship curve, were studied. The main results are as follows: the whole vibration process of the sample could be basically divided into four stages: the greater the dynamic stress is, the easier the sample is to liquefy. With the increasing vibration times, the samples under different dynamic stress amplitudes meet the qualitative analysis of the first three stages of liquefaction; that is, the pore water pressure increases rapidly at the beginning of vibration, and the growth is stable in the middle stage. When the effective pore water pressure increases to 60 kPa, it begins to increase sharply and finally reaches the effective confining pressure, and then, the soil sample liquefies. The axial strain begins to accumulate in the compression direction and gradually increases. After liquefaction, the strain accumulates in the tensile direction in equal amplitude. During the whole vibration process, the axial strain of the sample can develop greatly in the tensile and compressive direction. Under the action of different cyclic stresses, the stress path of the soil sample tends to zero, and the rate of effective stress state varies greatly, indicating that the dynamic stress has a great influence on the dynamic liquefaction process of Fujian standard sand. In the dynamic triaxial test of standard sand, the difference in dynamic stress amplitude will lead to great changes in the stress-strain relationship curve. But it has little effect on the analysis of liquefaction dynamic characteristics of sand.
Thermophysical Studies of Chlorpheniramine with 1-Propanol at 303 K, 308 K, and 313 K in the Atmospheric Pressure
Authors: Elangovan Sampandam
Published: May 2022

A binary liquid system that consists of chlorpheniramine and 1-propanol is prepared at various concentrations by the mole fraction method. The density (ρ), viscosity (η), and ultrasonic velocity (U) of the system are observed at 303 K, 308 K, and 313 K. From the experimental observations, various thermophysical parameters such as adiabatic compressibility (β), free length (Lf), free volume (

V

f

), viscous relaxation time (τ), and Gibbs free energy (

Δ
G

) are determined. The deviations of the excess parameters (βE,

L
f
E

,

V
f
E

, τE, and

Δ

G

E

) from their ideal values are calculated. The excess values are fitted with the Redlich–Kister polynomial function, and the corresponding coefficients are derived. Moreover, the standard deviations of the excess parameters are evaluated. The experimental and theoretical data confirmed the existence of hydrogen bonding interactions among the functional groups of the liquid mixture. The strength of intermolecular interactions decreased with increasing the temperature of the mixture. The strength of intermolecular interaction is noticed as 303 K > 308 K > 313 K.
The Effect of Exercise Training on the Detection of Heart Rate and Body Temperature Sensors in Adolescents
Authors: Xiao Wei
Published: May 2022

The aim of this study is to study the effect of exercise training on heart rate and body temperature sensor detection in adolescents. For the methods of logical analysis, test, and mathematical statistics, this paper takes 60 students aged 13-15 in the experimental middle school as the research subjects to explore the characteristics of heart rate changes of middle school students under different high-intensity intermittent exercise schemes. Then, it analyzes the characteristics of heart rate change in exercise center and heart rate change in high-intensity intermittent exercise. The experimental results show that the high-intensity intermittent exercise of adolescents aged 13-15 presents the stage of rapid recovery and buffer recovery. Whether in the quiet heart rate buffer stage or the rapid heart rate increase stage, as well as the “extreme heart rate maintenance stage” and heart rate recovery stage, the heart rate change point of 15-year-old adolescents is 1-3 seconds later than that of 13-year-old and 14-year-old adolescents. In addition, in the heart rate maintenance stage of 15-year-old adolescents, the heart rate fluctuates greatly. The rapid recovery stage of boys is 1 minute, and the heart rate can reach the quiet heart rate after 4-5 minutes. The rapid recovery stage of girls is 1.5 minutes, and the heart rate can reach the quiet heart rate after 4-5 minutes. The older the age of high-intensity intermittent training of adolescents aged 13-15, the longer the rapid rise stage of heart rate change, and there was no significant change in quiet heart rate buffer stage and body temperature.
High-Performance Concrete Strength Prediction Based on Machine Learning
Authors: Liu Yanning
Published: May 2022

High-performance concrete is a new high-tech concrete, produced using conventional materials and processes, with all the mechanical properties required for concrete structures, with high durability, high workability, and high volume stability of the concrete. The compressive strength of high-performance concrete has exceeded 200 MPa. 28-d average strength between 100 to 120 MPa of high-performance concrete has been widely used in engineering. Compressive strength is one of the important parameters of concrete, and carrying out concrete compressive strength prediction is of high reference value for concrete design. Eight variables related to concrete strength are used as the input of the machine learning algorithm, and the compressive strength of HPC is used as the object of study. 60 samples are constructed as the dataset by concrete preparation, and the prediction of compressive strength of HPC is carried out by combining the XGBoost algorithm. In addition, SVR algorithm and RF algorithm are also performed on the same dataset. The results show that the XGBoost model has the highest prediction accuracy among the three machine learning models, and the XGBoost algorithm scores 0.9993 for

R

2

and 1.372 for RMSE on the test set. The XGBoost algorithm has high prediction accuracy in predicting the compressive strength of HPC, and the choice of model is important for improving the prediction accuracy.
Local Well-Posedness to the Cauchy Problem for an Equation of the Nagumo Type
Authors: Lizarazo Vladimir , De la cruz Richard , Lizarazo Julio
Published: May 2022

In this paper, we show the local well-posedness for the Cauchy problem for the equation of the Nagumo type in this equation (1) in the Sobolev spaces

H

s

ℝ

. If

D
>
0

, the local well-posedness is given for

s
>
1
/
2

and for

s
>
3
/
2

if

D
=
0

.
Effect and Prognosis Factors of Combining Laparoscopic Radical Resection of Colon Adenocarcinoma with Docetaxel Therapy in Treating Middle and Advanced Colon Adenocarcinoma
Authors: Gao Qi , Zhang Caifeng , Dong Zhichao , Guo Yan , Zhang Li
Published: May 2022

Objective. The aim of the study is to explore the clinical efficacy and prognosis factors of joint application of laparoscopic radical resection of colon adenocarcinoma (COAD) and docetaxel therapy in treating COAD of middle and advanced stages. Methods. The clinical data of 103 COAD patients of middle and advanced stages treated in our hospital from July 2016 to July 2018 were selected for the retrospective analysis, all patients received the treatment scheme of combining laparoscopic radical resection of COAD with docetaxel therapy for the observation of short-term efficacy, follow-up was conducted to record their 3-year survival, and relevant factors affecting patient prognosis were analyzed by the logistic regression model. Results. After treatment, the total remission rate of patients was 75.73% (78/103), the total incidence rate of adverse reactions was 16.50% (17/103); patients’ level values of various serum tumor markers after treatment were significantly lower than those before treatment (

P
<
0.001

); according to the univariate analysis results, for COAD patients with different tumor diameters, differentiated degrees, TNM stages, perineural invasion degrees, pathological types, and depths of invasion, their modality rates were statistically different (

P
<
0.05

); and the logistic regression analysis showed that tumor diameter ≥5 cm, poor differentiation, TNM stage IV, perineural invasion, pathologically signet-ring cell carcinoma, and T3-invasion were the independent risk factors affecting patient prognosis (

P
<
0.05

). Conclusion. Combining laparoscopic radical resection of COAD with docetaxel therapy in treating COAD of middle and advanced stages achieves affirmed short-term efficacy, which can reduce patients’ level of serum tumor markers and ensure high safety and good survival prognosis. Tumor diameter, differentiated degree, TNM stage, perineural invasion, pathological type, and T3-invasion are the relevant factors affecting the prognosis of middle and advanced COAD.
Optimization of Bus Scheduling and Bus-Berth Matching at Curbside Stops under Connected Vehicle Environment
Authors: Ou Shiqi , Ma Wanjing , Yu Chunhui
Published: May 2022

It is commonly seen that buses are blocked by the ones in front serving passengers and have to queue outside a curbside bus stop although there are vacant berths at the stop. The resultant bus delays degrade the service level of urban public transportation. A potential solution is to reschedule the arrivals of the buses at the stop for full utilization of the berths with the aid of connected vehicle technologies. This study proposes a mixed-integer linear programming model to optimize the scheduling of bus arrivals and the bus-berth matching at a curbside stop under the connected vehicle environment. The objective is the minimization of the bus delays weighted by the number of passengers on the buses. Bus arrival times at the stop and the assignment of berths are optimized together with bus departure times from the stop. Bus punctuality is also taken into consideration. The proposed model could be applied dynamically to cater to time-varying traffic conditions. Numerical studies validate the advantages of the proposed model over the “first-come-first-service” strategy and the relaxed model without bus punctuality in terms of weighted bus delays and bus punctuality. Sensitivity analyses show that (1) the proposed model is robust to the fluctuation of bus service time and (2) a smaller number of berths may be preferred on the condition that the bus demand does not exceed the stop capacity.
Evaluation Model of Tourism-Driven Real Estate Economic Growth Point Based on AHP
Authors: Zheng Du
Published: May 2022

The economy of real estate is intimately connected to people’s quality of life, living standards, and living situations, all of which are extremely important in economic life. In order to better understand the impact of tourism-driven real estate on economic growth, based on AHP, this paper establishes an evaluation model of tourism-driven real estate economic growth. Based on the influencing factors of tourism real estate development, the evaluation model is built in this study of the tourism real estate development model by AHP and determines the optimal model according to the evaluation results. Through the application of the AHP model, the effect of real estate regulation policy is evaluated. The housing prices are affected not only by the influencing factors of housing prices in the pressure indicators, but also by real estate control policies. The model can effectively assess the economic growth point of real estate, propose real estate-specific guiding alternatives for economic growth and development, and ensure that the real estate market develops in a healthy and orderly manner.
The Influence of Heavy Metals on Gastric Tumorigenesis
Authors: Wang Liang , Miao Congxiu , He Yuan , Li Hanglong , Zhang Shasha , Li Keyan , Liu Huimin , Li Wushuang , Zhao Jiangman , Xu Yue , Tang Hui , Zhao Qiang
Published: May 2022

Objectives. This study aimed to observe the relationship among heavy metals concentration, microsatellite instability (MSI), and human epidermal growth factor receptor type 2 (HER2) gene amplification in gastric cancer (GC) patients. Methods. The concentrations of 18 heavy metals in the plasma of GC patients and healthy controls were measured by inductive coupled plasma emission spectrometry (ICP-MS). MSI detection was conducted by detecting 5 microsatellite repeat markers by PCR analysis. HER2 gene amplification was detected by fluorescence in situ hybridization (FISH). The relationship among heavy metal elements, tumor biomarkers, HER2 amplification, and MSI status was analyzed by Pearson correlation analysis. Results. A total of 105 GC patients and 62 healthy controls were recruited in this study. The concentration of arsenic (As), chromium (Cr), cuprum (Cu), mercury (Hg), manganese (Mn), lead (Pb), stibium (Sb), selenium (Se), stannum (Sn), strontium (Sr), thallium (Tl), vanadium (V), and zinc (Zn) were significantly different between GC patients and controls. Among 105 GC patients, including 87 microsatellite-stable (MSS) samples and 18 MSI samples, the concentration of Ga is significantly higher in the MSI group than that in the MSS group. Meanwhile, in 97 GC patients having detected HER2 gene amplification, 69 of 97 had negative HER2 gene amplification and the rest 28 GC patients had positive HER2 gene amplification. The concentration of Hg, Sn, and Tl is noticeably higher in the HER2 positive group than in the HER2 negative group. Only Sb was positively correlated with MSI, but none of these heavy metals was correlated with HER2 gene amplification. Conclusions. The results indicated that Sb has significant positive correlation with the MSI status, which suggests that Sb may cause MSI in GC. However, further research studies are required to elucidate the mechanisms in the near feature.
Identification of New Chickpea Virus and Control of Chickpea Virus Disease
Authors: Cun Zihui
Published: May 2022

Objective. The objective of the study was to discuss the classification, virus characteristics, detection methods, and control measures of chickpea virus, with an aim to provide a theoretical basis for identification of new chickpea virus and control of chickpea virus disease. Methods. The domestic and foreign studies were reviewed, and the virus coat protein or nucleic acid sequence was identified by immunological and molecular diagnostic techniques. Results. There were 14 main types of chickpea viruses attacking, and seven Luteoviridae viruses were reported, namely, chickpea chlorotic stunt virus (CpCSV), bean leafroll virus (BLRV), beet western yellows virus (BWYV), soybean dwarf virus (SbDV), cotton leafroll dwarf virus (CLRDV), cucurbit aphid-borne yellows virus (CABYV), and phasey bean mild yellows virus (PhBMYV). The family Geminiviridae includes chickpea chlorotic dwarf virus (CpCDV), chickpea chlorosis virus (CpCV), chickpea redleaf virus (CpRLV), chickpea yellows virus (CpYV), and mastrevirus. The family Nanoviridae is dominated by the faba bean necrotic yellows virus (FBNYV). The family Bromoviridae includes cucumber mosaic virus (CMV) and alfalfa mosaic virus (AMV). Conclusion. At present, there are mainly 12 types of viruses infecting chickpeas, which are transmitted by leafhoppers or aphids and are associated with symptoms such as yellowing, chlorosis, and stunted pod development, resulting in serious yield loss. Correct use of various molecular diagnostic tools to detect and identify chickpea virus can accurately assess chickpea virus infection and provide a basis for the prevention and treatment of chickpea virus disease.
[Retracted] Optimized CNN‐Based Recognition of District Names of Punjab State in Gurmukhi Script
Authors: Sharma Sandhya , Gupta Sheifali , Gupta Deepali , Juneja Sapna , Turabieh Hamza , Sharma Lokesh , Kiros Bitsue Zelalem
Published: May 2022

Automation of Postal systems has the major research scope in the field of automation. To create Postal Automation set‐up for countries like India is a tedious task if compared with other countries because of India’s multiscript and multilingual behavior. This work will help in recognizing the “Gurmukhi” handwritten district names of the State Punjab. To recognize the district names, a CNN‐based architecture is proposed by employing a Holistic approach. For this, an image database of 22000 samples is prepared having 1000 sample images for every district name which is collected from 500 different writers. Maximum accuracy on validation data achieved by the proposed Model is 99%.
Econometric Modelling Based on Dynamic Count Regression and China Power Supply Dataset
Authors: Yan Yixin , Hu Jiliang , Chen Xiding , Kumar A. P. Senthil
Published: May 2022

Traditionally, economic data of power supply is often analyzed through the count regression model due to the type of empirical data in the decision-making process. However, in reality, it is difficult to use count data model for data with autoregressive features. The main reason is that the time series features and autoregressive attributes cannot be controlled through the count regression model, which violates the assumptions set by the model. Therefore, there may be errors in the empirical analysis results. This letter firstly describes the characteristic of the count regression model and the problem, and then we refine the multiplicative autoregressive count model for dynamic count data. The model has desirable theoretical properties and is trivial to incorporate into existing models for the count data. In this study, the multiplicative autoregressive counting model for dynamic counting data is improved. The model has ideal theoretical properties and can be easily incorporated into existing economic models of counting data, especially for power supply policy analyses.
Diagnostic Value of Vaginal Ultrasound under Improved Clustering Algorithm Combined with Hysteroscopy in Abnormal Uterine Bleeding
Authors: Wang Yuhui , Long Qionghui
Published: May 2022

In order to explore the diagnostic value of the improved clustering algorithm of vaginal ultrasound combined with hysteroscopy in abnormal uterine bleeding (AUB), 128 patients diagnosed with AUB in the hospital were selected as the research objects. A K-means improved clustering color image segmentation algorithm was designed and applied to AUB vaginal ultrasound image processing. The running time, mean square error (MSE), and peak to signal noise ratio (PSNR) were calculated to evaluate the algorithm, and the sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio were used to evaluate the diagnostic accuracy of the detection method. In addition, combined with hysteroscopy, a comprehensive evaluation of the diagnostic value of abnormal uterine bleeding diseases was implemented. The results showed that compared with the traditional K-means clustering algorithm, the running time of the improved K-means clustering color image segmentation algorithm in the training set was significantly shortened, the MSE was significantly decreased, and the PSNR was significantly increased (

P
<
0.05

). The sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio (90.5%, 93.2%, 84.3, and 96.3%) of AUB diagnosis were significantly improved in the algorithm of vaginal ultrasound combined with hysteroscopy (

P
<
0.05

). In summary, the combination of vaginal ultrasound and hysteroscopy based on K-means improved clustering color image segmentation algorithm can significantly improve the clinical diagnostic accuracy of AUB patients.
Physical Model Test on the Influence of Karst Cave under and in front of Pile on the Stability of Embedded End of Antislide Pile
Authors: Liang Jiahui , Fan Qiuyan , Liu Menghan , Li Tianyu
Published: May 2022

With the extensive use of antislide piles in engineering, the stability of the embedded end of antislide piles in karst areas has become increasingly prominent. When there is a cave in front of the antislide pile, the cave may be deformed under horizontal load, causing the stability of the embedded end of the antislide pile to lose its stability. Based on the similarity model theory, this study establishes two different shapes of caves (horizontal in the longitudinal direction and vertical in the longitudinal direction) beneath and in front of the rock-socketed section of the antislide pile. Using horizontal loading tests, the influence of the karst cave beneath and in front of the elliptical pile on the stability of the antisliding pile embedded end was studied, and the following relevant conclusions were obtained. (1) Compared with the model without cavities, the presence of cavities in front of and under the pile significantly reduces the horizontal bearing capacity of the antislip pile. The fracture trace initially develops from the bottom of the pile against the side of the active zone, roughly at 90° to the horizontal direction through the top plate of the cave and continues to develop under the cave until the boundary. (2) The horizontal bearing capacity of horizontal bearing pile is greatly reduced by karst cave. The karst cave under the pile has a greater influence on this property than that in front of pile and the influence of the two karst caves has a common effect. (3) Both sides of the bottom for pile and the roof of karst cave are prone to damage. Besides, the roof of the karst cave under the pile is more prone to damage for more obvious stress concentration. (4) The loading failure processes of all model can be roughly divided into three stages: gentle deformation, stable expansion of deformation, and accelerated expansion of deformation. (5) Compared with the case where there is no karst cave in front of pile, the karst cave in front of pile will increase the deformation of pile under horizontal load and the same level of load. (6) Based on the limit equilibrium method, a check calculation method for the bearing capacity of the anchored end when a cave existing in front of the pile is proposed. The verification method was verified.
Design of Assessment Judging Model for Physical Education Professional Skills Course Based on Convolutional Neural Network and Few-Shot Learning
Authors: Chen Qingjie , Dong Minkai
Published: May 2022

In recent years, the promotion of quality education and the development of curriculum and teaching materials reform have put forward higher goals and requirements for professional skills in physical education. However, there are still many shortcomings in the nuclear assessment of physical education professional skills, such as lack of clarity in evaluation objectives, lack of scientific in evaluation indexes, lack of systematization in evaluation contents, lack of diversity in evaluation methods, lack of authority in evaluation results, and lack of timely prediction and analysis of students’ mastery of classroom teaching skills, thus not giving good play to all the functions that the nuclear assessment should have, thus to a certain extent fettering the further enhancement of physical education. With the development of information technology, artificial intelligence, as a new technology, can guide the improvement of the assessment and judging mode of physical education professional skills courses, and is also an important guiding idea for physical education majors to meet the development demands of information-based society. Based on the analysis of the connotation and characteristics of deep learning, this paper points out the insufficiency of the assessment and evaluation of traditional physical education professional skills courses and proposes a method of assessment and evaluation of physical education professional skills courses based on convolutional neural networks and small sample learning. In the case of a small amount of data in the course assessment, we use a small number of samples to learn, and only need a small number of samples to learn quickly. Using the improvement measures under the teaching concept of deep learning, physical education personnel are required to truly change in terms of professional skills mastery and evaluation. We effectively implement improvement measures, promote the improvement of physical education professional skills, and realize the migration and innovation of sports knowledge and skills.
Adaptive Integration Algorithm of Sports Event Network Marketing Data Based on Big Data
Authors: Wu Jiatong , Zhang Jun , Qiao Jing
Published: May 2022

To address the issues of low-data integration accuracy and efficiency, as well as a lack of data integration impact, an adaptive data integration algorithm for sports event network marketing data based on big data is presented. The fundamental theory of tensor is researched by examining the notion and features of big data and by using the associated technologies of the big data framework. Collect network-marketing data from a variety of sporting events and feed it to a big data platform. Combined with MapReduce parallelization mode, tensor represents the online marketing data of sports events according to the structured, semistructured, and unstructured characteristics of different big data. Integrate each tensor model based on semitensor product, build a unified data adaptive integration tensor model, and realize the adaptive integration of sports event network marketing data. The experimental results show that the proposed algorithm has a good effect on the adaptive integration of sports event network marketing data and can effectively improve the accuracy and efficiency of data adaptive integration.
Estimation of the Student Employment in the Aviation Industry Based on Novel Fractional Error Accumulation Grey Model
Authors: Zhang Yali , Yang Xiaojiang , Cui Wei
Published: May 2022

To improve the employment forecasting accuracy of traditional grey models, the grey model with the fractional error accumulation is proposed. The estimation error is accumulated. The proposed model can make use of the initial value x(1) and can give more attention to the error of new data. The monotonicity of the simulative value by the proposed model is data‐driven and uncertain. The comparison results show that the proposed model can enhance the forecasting accuracy of traditional grey model. It deserves to be applied to employment forecasting.
Postoperative Examination of Laryngeal Malignant Tumor Based on Narrowband Imaging Resolution Enhancement Technology
Authors: Yang Neng , Zhao Chenchen , Lin Fenfen , Wu Jiaojiao , Liu Ben
Published: May 2022

The application of endoscopic imaging in the biopsy of malignant laryngeal lesions is one of the current research hotspots in the medical field. Based on the narrowband imaging resolution enhancement technology, a model for postoperative examination of laryngeal malignant tumor was constructed in this paper. The article calculated the biopsy detection rate of malignant lesions and the correct biopsy detection rate of the two groups and made a statistical comparison. In the NBI mode group, the mucosal morphology and superficial mucosal microvascular morphology of the same lesion under two different modes of white light and NBI were compared, which solved the problem of data processing of cases. During the case comparison process, patients who needed biopsies to be sent for pathology were selected for inclusion in the study and were randomly divided into two groups. The coincidence rate of EUS combined with NBI diagnosis was significantly higher than that of ordinary white light gastroscopy (47.92%), and the difference was statistically significant (P = 0.000 < 0.05). The experimental results compared the accuracy of the normal white light mode and the NBI mode to diagnose the nature of the lesions: according to the Kudo classification criteria, 23 cases of tumor lesions were to be diagnosed in the normal white light mode, with an accuracy rate of 69.70%, and the NBI mode was to be used to diagnose tumors. There were 81 cases of sexual lesions, with an accuracy rate of 93.94%. The NBI mode was more accurate in diagnosing the nature of the lesions under the Kudo classification standard (P < 0.05). In 64 cases, the accuracy rate was 63.63%. Under the NBI mode, 29 cases of tumor lesions were proposed to be diagnosed, and the accuracy rate was 87.88% to promote the application of NBI endoscopy in the biopsy of malignant laryngeal lesions.
Interaction Design of Educational App Based on Collaborative Filtering Recommendation
Authors: Xu Ying , Neo Tse-Kian , Hew Soon Hin
Published: May 2022

With the advent of the 5G digital era, cell phones are becoming ubiquitous in all aspects of our lives, and the increasing demand for remote interaction makes the app interaction experience an indispensable part of our lives. Due to the operational characteristics of gesture interaction in the interface of a smart terminal application (app), this mode of human-computer interaction has become the mainstream mode of human-computer interaction. Educational app is the result of a combination between mobile Internet technology and education, which not only provides a more efficient and convenient method of learning for each subject but also expands the possibilities for teaching each subject through intelligent interaction. On this basis, this paper proposes an educational app design method based on collaborative filtering recommendations and investigates ways to improve the use of mobile apps to create an interactive teaching mode. Simultaneously, this paper combines user activity, item popularity, and time factors to comprehensively measure user visibility of items and incorporates them into the collaborative filtering recommendation algorithm in order to effectively mitigate the effects of data sparsity and user selection bias and improve recommendation results.
Investigation of the Potential Key Genes and the Multitarget Mechanisms of Polygonum cuspidatum against Heart Failure Based on Network Pharmacology and Experimental Validation
Authors: Guo Fang , Xing Zhaoqin , Sun Qingwen
Published: May 2022

In this study, systematic pharmacology and bioinformatic approaches were employed to identify the potential targets of Polygonum cuspidatum (PC) for treating heart failure (HF). The active ingredients of PC were screened by using the TCMSP database, and HF-related genes were identified in the GEO database. Then, the herb-HF targeted-gene networks were constructed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were performed to obtain the enriched molecular pathways associated with the pathogenesis of HF. Finally, in vitro experiment was performed to evidence network pharmacology analysis. 170 intersection genes were obtained, and key genes (FOXO3, NFKB1, and TNF) were identified. Besides, GO and KEGG findings indicated that PC treatment of HF was achieved via regulating apoptosis, IL-17 signaling pathway, TNF signaling pathway, response to oxidative stress, and response to reactive oxygen species. And cell experiment revealed that PC could decrease the expression of NFKB1 and TNF and increase the expression of FOXO3, SOD1, and GPX1 in H9C2 cells. These findings showed that the therapeutic mechanism of PC in the treatment of HF may be associated with the regulation of inflammation-related and oxidative stress-related genes.
An Optimized Discrete Data Classification Method in
N
-Dimensional
Authors: Kim Dongyung
Published: May 2022

We propose a discrete data classification method of scattered data in

N

-dimensional by solving the minimax problem for a set of points. The current research is extended from 2-dimensional and 3-dimensional to

N

-dimensional. The problem can be applied to artificial intelligence classification problems (machine learning, deep learning), point data analysis problems (data science problem), the optimized design of nanoscale circuits, and the location of facility problems, circle detection on 2D image, or sphere detection on depth image. We generalized the discrete data classification methodology in

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-dimensional. Finally, we resolved to find an exact solution of the location of a manifold for our suggested problem in

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-dimensional.
The Role of Extracorporeal Blood Purification in the Treatment of a Patient with Lemierre's Syndrome
Authors: Maričić Lana , Mihić Damir , Turk Tajana , Loinjak Domagoj , Zubčić Vedran
Published: May 2022

Lemierre's syndrome refers to the septic thrombophlebitis of the internal jugular vein. The condition typically begins with an oropharyngeal infection and frequently involves inflammation within the wall of the vein, infected thrombus within the lumen, surrounding soft tissue inflammation, persistent bacteremia, and septic emboli. Lemierre's syndrome is a rare disease; it occurs most commonly in otherwise healthy young adults. The most common etiologic agent is Fusobacterium necrophorum. We present a case of Lemierre's syndrome in a young girl and the role of extracorporeal method of blood purification with continuous venous hemodiafiltration with the use of a highly adsorptive membrane (AN69 HeprAN), thus achieving the combined elimination of cytokines and endotoxins. The use of advanced methods, along with an antibiotic and surgical treatment, will certainly help reduce mortality in this syndrome.
Genetic Algorithm in Multimedia Dynamic Prediction of Groundwater in Open-Pit Mine
Authors: Zhang Runting , Chen Shuzhao , Zhang Zhouai , Zhu Wencheng
Published: May 2022

This study is aiming at the nonlinear mapping relationship between the groundwater level and its influencing factors. Through the design and calculation process of matlab7 platform, taking the monitoring wells distributed in an open-pit mining area as an example, the short-term prediction of groundwater dynamics in the study area is carried out by using BP neural network model and BP neural network model based on genetic algorithm. Root mean squared error (RMSE), Mean absolute percent-age error (MAPE) and Nash–Sutcliffe efficiency (NSE) are used coefficients,, and the results were compared with BP neural network and stepwise regression model. From the results of the comparative analysis, the genetic algorithm optimized the BP neural network model in the training phase and the test phase, the RMSE was 0.25 and 0.36, the MAPE was 6.7 and 8.13%, and the NSE was 0.87 and 0.72, respectively. The BP neural network model optimized by genetic algorithm is obviously superior to the BP neural network model, which is an ideal prediction model for short-term groundwater level. This model can provide a prediction method for groundwater dynamic prediction and has a good application prospect.
Fetal Meconium Peritonitis: A Clinical Study of Nine Cases
Authors: Fu Fengping , Song Xia , Huang Fudan , Yuan Hang , Xiao Li
Published: May 2022

Objective. To explore the prenatal ultrasonographic characteristics and pregnancy outcomes of fetal meconium peritonitis (FMP). Methods. Nine patients diagnosed with FMP by routine prenatal examination between January 2015 and December 2020 were identified. Both prenatal ultrasonographic characteristics and pregnancy outcomes associated with these patients were retrospectively analyzed. Results. The mean gestational age at the time of FMP diagnosis was 31.3 ± 4.8 weeks, and the mean gestational age of delivery was 35.1 ± 5.1 weeks. Prenatal ultrasonographic findings at the time of diagnosis in these patients included intestinal dilatation (9/9, 100%), intraperitoneal calcification (8/9, 88.9%), fetal ascites (5/9, 55.6%), intraperitoneal pseudocyst (5/9, 55.6%), and polyhydramnios (6/9, 66.7%). Analyses of the etiological basis for meconium peritonitis in 5 of the 8 live births that underwent surgical treatment revealed 4 cases of congenital volvulus and 1 case of jejunal atresia. Conclusion. The prenatal ultrasound manifestations of fetal meconium peritonitis are diverse, and the different grades of prenatal ultrasound manifestations can provide important information for the treatment of perinatal infants.
Mining the Prognostic Role of DNA Methylation Heterogeneity in Lung Adenocarcinoma
Authors: Liao Hongying , Luo Xiaolong , Huang Yisheng , Yang Xingping , Zheng Yuzhen , Qin Xianyu , Tan Jian , Shen Piao , Tian Renjiang , Cai Weijie , Shi Xiaoshun , Deng Xiaofang
Published: May 2022

Purpose. DNA methylation heterogeneity is a type of tumor heterogeneity in the tumor microenvironment, but studies on the identification of the molecular heterogeneity of the lung adenocarcinoma genome with respect to DNA methylation sites and their roles in lung cancer progression and prognosis are scarce. Methods. Prognosis-associated DNA methylation subtypes were filtered by the Cox proportional hazards model and then established by unsupervised cluster analysis. Association analysis of these subtypes with clinical features and functional analysis of annotated genes potentially affected by methylation sites were performed. The robustness of the model was further tested by a Bayesian network classifier. Results. Over 7 thousand methylation sites were associated with lung adenocarcinoma prognosis. We identified seven molecular methylation subtypes, including 630 methylation sites. The subtypes yielded the most stable results for differentiating methylation profiles, prognosis, and gene expression patterns. The annotated genes potentially affected by these methylation sites are enriched in biological processes such as morphogenesis and cell adhesion, but their individual impact on the tumor microenvironment and prognosis is multifaceted. Discussion. We revealed that DNA methylation heterogeneity could be clustered and associated with the clinical features and prognosis of lung adenocarcinoma, which could lead to the development of a novel molecular tool for clinical evaluation.
Novel Evaluation of the Fractional Acoustic Wave Model with the Exponential‐Decay Kernel
Authors: Alyusof Rabab , Alyusof Shams , Iqbal Naveed , Arefin Mohammad Asif
Published: May 2022

This study employs a newly developed methodology called the variational homotopy perturbation transformation method to study fractional acoustic wave equations. The motivation for this study is to extend the variational homotopy perturbation technique to the variational homotopy perturbation transformation technique in the sense of the Yang–Caputo–Fabrizio operator. The suggested method demonstrated a straightforward and accurate technique for investigating fractional‐order partial differential equations. The technique’s validity is demonstrated through the use of several illustrative instances. The obtained answers were found to be extremely near to the precise solutions. Additionally, the proposed strategy achieves the best degree of accuracy. Indeed, the current technique can be seen as one of the analytic strategies for solving nonlinear fractional partial differential equations compared to other analytical techniques.
New Solutions of Nonlinear Equations Constrained by Bounded Variables under Edge Computation
Authors: Yu Wangxun , Wang Aiju
Published: May 2022

Aiming at the problem of low global convergence and local convergence rate of trust region interior points of bounded variable constrained nonlinear equations, a trust region interior point algorithm for bounded variable constrained nonlinear equations under edge calculation is designed. By constructing the basic function form of nonlinear equations constrained by bounded variables, the boundary of nonlinear equations is determined by Gauss Newton iterative process to ensure the global convergence of changes; solve the original objective function, analyze the trust region subproblem of the unconstrained optimization problem, and generate an acceptable region. The region is generated through two-dimensional example interpretation. The interior points in the acceptable region are determined by the primal dual interior point method, and the interior points in the acceptable region are optimized. With the help of edge calculation, the trust region interior point programming model of bounded variable constrained nonlinear equations is designed to realize the algorithm design. The experimental results show that the designed algorithm can improve the trust region interior point global convergence and local convergence rate of nonlinear equations with bounded variable constraints.
Distributed big data analysis using spark parallel data processing
Authors: Omar Hoger Khayrolla , Jumaa Alaa Khalil
Published: May 2022

Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.