bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Nursing Effect of Pelvic Floor Rehabilitation Training on Pelvic Organ Prolapse in Postpartum Pregnant Women under Ultrasound Imaging with Artificial Intelligence Algorithm
Authors: Yin Ping , Wang Hongli
Published: April 2022

This study was aimed at exploring the application value of ultrasound technology and rehabilitation training based on artificial intelligence algorithm in postpartum recovery of pelvic organ prolapse. Sixty patients diagnosed as mild and moderate pelvic organ prolapse by pelvic organ prolapse quantification evaluation were selected as the research objects. The patients were randomly divided into experimental group (30 cases) and control group (30 cases). The patients in the control group were given routine guidance and postpartum health education 42 days after delivery and given no pelvic floor rehabilitation training, waiting for natural recovery. 42 days after delivery, the patients in the experimental group received pelvic floor rehabilitation training based on the patients in the control group. All patients underwent ultrasonography, the convolution neural network (CNN) algorithm was used for image denoising and edge feature extraction, and the performance of the algorithm was evaluated by the Dice coefficient, positive predictive value, sensitivity, and Hausdorff distance. The thickness of levator ani muscle, anterior and posterior diameter of perineal hiatus, pelvic floor muscle strength, and imaging data were compared between the two groups. The results revealed that the thickness of levator ani muscle in the experimental group was significantly greater than that in the control group after one month and three months of treatment (

0.633
±
0.26

 cm vs.

0.519
±
0.234

 cm,

0.7
±
0.214

 cm vs.

0.507
±
0.168

 cm,

P
<
0.05

). After one month and three months of treatment, the anterior and posterior diameter of perineal fissure in the experimental group was obviously smaller than that in the control group (

4.76
±
0.513

 cm vs.

5.002
±
0.763

 cm,

4.735
±
0.614

 cm vs.

4.987
±
0.581

 cm,

P
<
0.05

). The pelvic floor muscle strength of the experimental group was remarkably higher than that of the control group after one month and three months of treatment (

3.183
±
1.47

vs.

2.41
±
1.57

,

3.365
±
1.53

vs.

2.865
±
1.69

,

P
<
0.05

). The ultrasonic image was clearer, the focus was more prominent, and the image quality was significantly improved after being processed by artificial intelligence algorithm. The Dice coefficient, positive predictive value, sensitivity, and Hausdorff distance of the proposed algorithm were better than those of the traditional algorithm. Thus, artificial intelligence algorithm had a good effect in ultrasonic image processing. Pelvic floor rehabilitation training had a good effect on postpartum nursing of patients with pelvic organ prolapse.
Construction of 3D Design Model of Urban Public Space Based on ArcGIS Water System Terrain Visualization Data
Authors: Liu Chunjing , Yu Xueyong
Published: April 2022

On the premise of being familiar with ArcGIS Server technology, we build the architecture of the entire platform, including the basic support layer, data layer, service platform layer, and application layer, and build the entire environment of the platform. We make electronic maps through Arc Map, and collect, organize, and improve spatial data and attribute data, so as to achieve satisfactory accuracy and visual comfort. This study implements various map services under the Dojo framework, including basic map operations, information display and query, marker points, eagle eye diagrams, measurement, printing, and other functions, and uses JavaScript technology to improve user experience. We publish various services through ArcGIS Server, and realize fast and error-free invocation of each service. Based on the theory of runoff and runoff, ArcGIS software was used to study the hydrological information of the watershed, and to determine the catchment area threshold and hydrological response unit. Combined with the GIS spatial analysis method, the numerical simulation of rainfall and runoff in the study case area was carried out, and the variation of the annual rainfall-runoff coefficient was obtained. This study selects an area where stock planning was first proposed as the object of this research. Briefly, we introduce the construction of three-dimensional public space in a certain area, select thirteen typical three-dimensional public spaces as representatives for public evaluation, and explore their existing problems, mainly including the lack of adaptability of space functions and the lack of diversity in space design, privatization of operation management, low level of public perception, etc. Since then, in response to the public problems of the three-dimensional public space in a certain area, a targeted three-dimensional public space optimization strategy is proposed from the four levels of planning policy, urban design, management subject, and user subject.
Application of Internet of Things Technology in Vocal Music Teaching Recording Equipment Assisted by Machine Learning
Authors: Guo Tingjun
Published: April 2022

Vocal music teaching is a professional, technical, and practical subject. It is also an important part of music and art education and has certain social and educational significance. In vocal music teaching, recording equipment is an essential teaching tool. It plays a pivotal role in vocal music teaching. In recent years, with the rapid development of China’s social economy, education has also achieved great development, and it has also brought development opportunities to vocal music education. More and more advanced technologies and equipment have appeared to assist the smooth progress of vocal music teaching, especially recording equipment. This paper is aimed at studying the application of IoT technology in vocal music teaching recording equipment assisted by machine learning. Combined with machine learning and Internet of Things technology, the experiment of recognition effect of vocal music teaching recording equipment was carried out. It designs an end-to-end vocal music teaching recording device recognition model based on the Internet of Things technology assisted by machine learning. The experimental results show that the use of this model improves the recording recognition accuracy of vocal music teaching recording equipment by 20%.
A Raman Spectroscopic Study of Calcium Silicate Hydrate (CSH) in the Cement Matrix with CNTs and Oxide Additives
Authors: Azeem Muhammad , Saleem Muhammad Azhar
Published: April 2022

The calcium silicate hydrate (CSH) concentration in the cement paste mixed with different types of carbon nanotubes (CNTs) and oxide additives is compared by using Raman spectroscopy. The pristine, hydroxyl and carboxyl functionalized CNTs are used in this work. The oxide additives are zinc oxide (ZnO), gadolinium oxide (Gd2O3), and silicon oxide (SiO2). A laser wavelength of 785 nm was used to collect the Raman spectra. It was observed that the concentration of calcium silicate hydrate (CSH) is unaffected in CNTs-OPC matrices regardless of the type and weight percentage of the CNTs. The oxides, as expected, show significant effects on the concentration of the CSH in the matrices. An increase in the CSH concentration is observed in the ZnO and Gd2O3 matrices with cement. For the SiO2 cement paste matrix, however, the CSH concentration appears to be decreased. This study shows CSH concentration can be controlled by using oxide additives whereas CNTs do not react chemically with the cement composites.
Effect and Significance of High-Quality Nursing on Blood Glucose, Pregnancy Outcome, and Neonatal Complications of Patients with Gestational Diabetes Mellitus
Authors: Zhong Wenjuan , Li Chunxia , Liu Jing , Zhou Jinman , Xiao Zhong , Li Cuimei , Wu Hui
Published: April 2022

Objective. To explore the role of high-quality nursing on blood glucose, pregnancy outcome, and neonatal complications in patients with gestational diabetes mellitus. Methods. Altogether, 148 patients with GDM admitted to our hospital were selected as the research participants, 83 of whom received high-quality nursing as the observation group (OG). Another 65 patients received only routine care as the control group (CG). The blood sugar level, blood pressure value, and adverse pregnancy outcomes of the OG and the CG of patients after intervention were compared. The changes of psychological state and nursing satisfaction of the OG and the CG of patients were observed. The blood lipid level and neonatal complications of the OG and the CG were detected. Results. The blood sugar level of the OG was lower than that of the CG (

P
<
0.05

). The systolic pressure and diastolic pressure of patients in the OG were lower than those in the CG after nursing intervention (

P
<
0.05

). The abnormal delivery rate in the CG was evidently higher than that in the OG (

P
<
0.05

). After intervention, SAS and SDS scores in the OG were lower than those in the CG (

P
<
0.05

). The number of people who needed improvement and were dissatisfied in the OG was evidently lower than that in the CG (

P
<
0.05

). TG and LDL-C in the OG were lower than those in the CG (

P
<
0.05

). The total incidence of complications in the CG was evidently higher than that in the OG (

P
<
0.05

). Conclusion. High-quality nursing can reduce blood sugar and blood pressure of GDM patients and has a great protective effect on maternal and infant health, which is worthy of clinical practice.
[Retracted] COVID‐19 Epidemic Analysis in India with Multi‐Source State‐Level Datasets
Authors: Wang Qirui
Published: April 2022

The COVID‐19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID‐19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID‐19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID‐19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID‐19 infection.
Study on Modified Dynamic Constitutive Model for Calcareous Sand considering Particle Breakage
Authors: Chen Huibin , Hou Heying , Jiang Pengming , Nong Zhenzhen , Li Tianxiang , Wang Qing , Pan Zhuojie
Published: April 2022

Calcareous sand is special sand widely distributed in the South China Sea, which is characterized by the irregular particle shape, high porosity, easy-breaking, etc. Through a series of dynamic triaxial tests, this paper will study the influence of particle breakage on the dynamic deformation characteristics of calcareous sand and explore a modified dynamic constitutive model considering particle breakage suitable for calcareous sand. The research shows that the dynamic deformation characteristics of calcareous sand can be described by the Hardin-Drnevich model. The maximum dynamic shear modulus and average effective stress of calcareous sand obey Janbu empirical formula, with approximate a linear relationship. Through screening experiment, it is found that there is a linear relationship between particle crushing rate and effective confining pressure. On the basis of Hardin-Drnevich, the dynamic constitutive model of calcareous sand considering the influence of particle breakage is established by introducing relative crushing rate Br and constitutive model parameters. By comparing with the experimental data, it is found that the relative error between the calculated value and the experimental value of the model is no more than 12%, and the average error is no more than 8%, indicating that the modified dynamic constitutive model can better predict the dynamic characteristics of calcareous sand.
Numerical Simulation Research on the Pressure Relief and Permeability Enhancement Mechanism of Large-Diameter Borehole in Coal Seam
Authors: Nian Jun , Zhao Bo , Zhang Wei
Published: April 2022

In order to achieve high-efficiency extraction of coal seams with large-diameter borehole and large flow rates, and to rapidly reduce coal seam gas content and pressure, a large-diameter borehole coal seam pressure relief and permeability enhancement technology is proposed. In this paper, numerical simulation is used to study the mechanism of pressure relief and permeability enhancement in coal seams with large-diameter boreholes, and the evolution of stress, cracks, gas, and permeability of coal bodies around boreholes with different diameters is discussed. The research results show that the stress changes in the coal body around the borehole are symmetrically distributed around the borehole. The stress evolution controls the evolution of the coal body’s cracks. Firstly, damage occurs around the borehole and then extends to the upper left and right corners, like a butterfly spreading its wings. The crushing zone increases with the increase of the hole diameter, and the impact radius of the 100-mm, 200-mm, 300-mm, and 350-mm diameter borehole rupture is 0.375 m, 0.65 m, 1.0 m, and 1.25 m, respectively. The rupture radius of the double 350-mm diameter borehole can reach 2.4 m. The larger the diameter of the borehole, the more fissures will be produced, and the air permeability of the coal body in the affected coal seam area will obviously increase. The research results provide theoretical support for gas drainage in soft, low-permeability, high-gas coal seams.
Determinants of Production Efficiency of Maize-Dominated Farmers in Western Parts of Ethiopia in Gudeya Bila District: Evidence under Shifting Cultivation Area
Authors: Tesema Tolesa
Published: April 2022

Background. In Ethiopia, maize is produced as major food crop that is based on traditional methods of production, and there exists inefficiency in the use of available scarce resources. Thus, poor people are failing to achieve rapid economic growth, development, and food security still today in the country. Hence, the best possible means of achieving development is through increasing the production efficiency of farmers. Thus, to estimate the levels of production efficiency, this study specifically used only data of farmers who are producing without ploughing by oxen and without using fertilizers in the study area under shifting cultivation. Method. Stochastic frontier production is used to estimate the technical efficiency score, and the cost frontier model is used to estimate production efficiency. To determine the determinants of production efficiency, the Tobit model was used in this study. Result. The Tobit model results show that loss due to wild animals, experience of household, and off-farm income had a negative impact on production efficiency of farmers. Regarding the positive determinants of production efficiency, land conservation practice and mobile use have a positive influence. Conclusion and recommendation. The farmers in the study area are inefficient in the production of maize. Since the loss of maize products is high due to wild animals such as pigs, apes, and monkeys that results in production inefficiency, the agricultural policies and strategies of Ethiopian governments should be directed toward providing tourism to protect those wild animals. Additionally, to increase the production efficiency, construction of terraces and soil bunds to conserve land and supporting the farmers by providing network facilities for mobile usage that boost maize production efficiency of farmers is essential for policymakers.
A Novel Acoustic Uroflowmetry‐Based Mobile App Voiding Diary: Comparison with Conventional Paper‐Based Voiding Diary
Authors: Kim Jung Kwon , Kim Hwanik , Ho Jin-nyoung , Jeong Seong Jin , Lee Sangchul
Published: April 2022

Objectives. To evaluate the usefulness of a novel acoustic uroflowmetry‐ (UFM‐) based mobile application (app) voiding diary (VD) focusing on the (1) compliance and (2) correlation with a conventional paper‐based VD. Materials and Methods. A total of 78 patients were included between December 2019 and June 2020, and a subsequent review of all data was performed. The analyzed data were as follows: (1) survey of convenience/satisfaction/preference comparing the two methods, (2) compliance regarding the completeness of both methods, and (3) correlation of each metric (24‐hour urine volume, nocturnal urine volume, nocturnal polyuria index, total number of voids, number of daytime voids, number of nocturnal voids, and maximal bladder capacity) between the two methods. Results. The survey results of convenience, satisfaction, and preference were as follows. With regard to convenience and satisfaction area, higher scores are reported in the mobile app VD (mean ± standard deviation (SD); convenience: 7.47 ± 2.19 [app] vs. 4.20 ± 2.49 [paper]; satisfaction: 7.36 ± 2.17 [app] vs. 5.07 ± 2.65 [paper]). The median score of the overall preference for using the mobile app instead of the paper‐based VD was 9 out of 10 (mean ± SD7.82 ± 2.68). We also found a good correlation between the two methods for nocturnal urine volume (r = 0.55, p = 0.04), nocturnal polyuria index (r = 0.66, p = 0.23), total number of voids (r = 0.9, p = 0.02), number of nocturnal voids (r = 0.83, p = 0.02), and maximal bladder capacity (r = 0.89, p = 0.04). Conclusion. The acoustic UFM‐based mobile app VD demonstrated favorable findings compared to the conventional paper‐based VD.
A New Model for Microbial Desalination Cells: Model Formulation and Validation under Different Operating Conditions
Authors: Hesham Merna , Elawwad Abdelsalam , Abd El Meguid Ahmed , Nour Mohamed Hamdy
Published: April 2022

In this paper, a dynamic mathematical model was developed to simulate the processes in Microbial Desalination Cells (MDCs) operated in cyclic batch flow mode using ordinary differential equations found in the literature. In contrast to previous models, the proposed model was developed for fed-batch operations and considers the effects of temperature and substrate inhibition using simple equations for quick simulation. Local sensitivity analysis was performed to determine the parameters with the least impact on current, COD, and salt removal, which were then eliminated from the simplified model. These parameters were found to be the decay rates of anodophilic and methanogenic microorganisms (

k

d
,
a

and

k

d
,
m

) and the internal resistance parameters (

R

anolyte

and

R

membrane

). In addition, the best-performing parameters based on the sensitivity analysis results were selected for reestimation for model fitting. The reestimated parameters were mediator yield (

Y

), membrane salt transfer coefficient (

d

), maximum substrate utilization rate by methanogenic microorganisms (

μ

s

,

m

, max), and maximum anodophilic growth rate (

μ

a

, max). The predictions of the model were consistent with both our previous experimental data and experimental studies found in the literature and can be easily used by experimentalists for the rapid simulation and prediction of an MDC’s performance under different operating conditions.
Gandan Oral Liquid Improves Exudative Pneumonia by Upregulating Bacteria Clearance via Regulating AQP5 and MUC5AC in Rats
Authors: Wang Yinlong , Yan Jing , Wang Peijia , Xu Xiaoqin
Published: April 2022

Gandan oral liquid (GOL) is a mixture of crude extracts from licorice and Radix isatidis. Clinically, it has been widely used in the treatment of exudative pneumonia (EP) in animals. But the molecular mechanism of these effects is unclear. Therefore, antibacterial activity and therapeutic effect were tested in vitro and in vivo. Exudative pneumonia was established with the intraperitoneal injection of LPS, followed by continuous intranasal inoculation of Klebsiella pneumoniae (KP). After that, Gandan oral liquid, acetylcysteine, and levofloxacin were given through the intragastric route for five days, and clinical symptoms were observed and counted. The bacterial content of alveolar lavage fluid was determined, hematology analysis was performed, and lung histology examination was performed. Western blotting, immunohistochemistry, and immunofluorescence were used to detect the expression levels of AQP3, AQP5, and MUC5AC in lung tissues. ELISA kit was used to detect serum and BALF cytokines levels. The results showed that GOL (242 mg/mL) had no antibacterial activity on Klebsiella pneumonia (KP), and the effect was significantly worse than levofloxacin. However, the therapeutic test in vivo of the rat model of bacterial EP showed different results. After treatment, GOL administration ameliorated EP and increased the expression of mucoprotein -5AC (MUC5AC), and GOL promoted water secretion of the respiratory tract by increasing the expression of aquaporin-5 (AQP5) and decreasing the levels of proinflammatory cytokines (TNF-α, IL-6, and IL-1β). Conclusion. GOL accelerates the water secretion of respiratory tract, inhibits the inflammatory response, induces removal of bacteria of respiratory tract via the AQPs/MUC pathway, and ultimately ameliorates EP.
Active and Passive Earth Pressure Calculation Method for Double-Row Piles considering the Nonlinear Pile Deformation
Authors: Zhou Yijun , Chen Yulong
Published: April 2022

The double-row pile supporting structure has been widely used in foundation pit excavations. When analyzing the effect of earth pressure on the pile structure, previous research only considered the double-row piles as the rigid body and the pile-soil interaction has not been examined. In this study, a theoretical model was developed based on Duncan-Chang’s hyperbolic theory to calculate earth pressures in the active and passive zones of the double-row pile supporting structure. The model considered the nonlinear effect of the pile deformation on the active and passive earth pressures. The macroscopic pile-soil interaction was converted into a microscopic stress-strain relationship at a certain point in the soil body, reflecting the nonlinear effect of pile deformation on earth pressure. Numerical simulation and large-scale field tests have been conducted to verify the proposed model. The results show that the average values of the parameters obtained by numerical simulation are

a
¯

=
0.38

,

b
¯

=
−
0.253

for the active zone and

a
¯

=
0.00612

,

b
¯

=
−
0.729

for the passive zone. Based on the values of

a
¯

and

b
¯

, the predicted active and passive earth pressures stemming from the developed model agreed well with those obtained from field tests. The developed model in this study can be used to predict the distribution of active and passive earth pressures for double-row pile supporting structures.
An Improved Multitask Learning Model with Matching Network and Its Application in Traditional Chinese Medicine Syndrome Recommendation
Authors: Wang Yingshuai , Xu Jing-Han , Zhang Meng , Zhang Dezheng , Wulamu Aziguli
Published: April 2022

Multitask learning (MTL) is an open and challenging problem in various real-world applications, such as recommendation systems, natural language processing, and computer vision. The typical way of conducting multitask learning is establishing some global parameter sharing mechanism among all tasks or assigning each task an individual set of parameters with cross-connections between tasks. However, for most existing approaches, the raw features are abstracted step by step, semantic information is mined from input space, and matching relation features are not introduced into the model. To solve the above problems, we propose a novel MMOE-match network to model the matches between medical cases and syndrome elements and introduce the recommendation algorithm into traditional Chinese medicine (TCM) study. Accurate medical record recommendation is significant for intelligent medical treatment. Ranking algorithms can be introduced in multi-TCM scenarios, such as syndrome element recommendation, symptom recommendation, and drug prescription recommendation. The recommendation system includes two main stages: recalling and ranking. The core of recalling and ranking is a two-tower matching network and multitask learning. MMOE-match combines the advantages of recalling and ranking model to design a new network. Furtherly, we try to take the matching network output as the input of multitask learning and compare the matching features designed by the manual. The data show that our model can bring significant positive benefits.
Existence and Uniqueness Study of the Conformable Laplace Transform
Authors: Younis Jihad , Ahmed Bouchenak , AlJazzazi Mazin , Al Hejaj Rasha , Aydi Hassen
Published: April 2022

This paper tackles the topic of conformable Laplace transform. The authors aim at discussing its existence by exploring and providing the kind of functions that possess a conformable Laplace transform. Furthermore, the comparison theorem of conformable improper integrals is presented to further explain and justify the existence of conformable Laplace transform for some functions. The uniqueness is also established in order to determine the inverse of conformable Laplace transform for functions. Moreover, we present a table of the conformable Laplace transform of the usual functions. Finally, as an application, we use the conformable Laplace transform for solving some fractional differential equations.
Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network
Authors: Liu Can , Sanober Sumaya , Zamani Abu Sarwar , Parvathy L. Rama , Neware Rahul , Rahmani Abdul Wahab
Published: April 2022

Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.
Managing the Gate Assignment Problem in the Hub Airport with Satellite Halls: A Transfer Demand-Oriented Approach
Authors: Wu Runfa , Zhou Wenliang , Zhong Linhuan , Liu Yihan
Published: April 2022

This study focuses on managing the gate assignment in the hub airport with both main terminal and satellite halls. We first formulate the gate assignment problem (GAP) as a binary linear programming model with multi-objective functions, where the practical constraints, e.g., gate time conflict and gate compatibility, are considered. Then, we incorporate the impact of gate assignment on transfer passengers and formulate the transfer demand-oriented gate assignment problem (TGAP) as a nonlinear model. A linearization approach and a heuristic approach are designed to solve the TGAP model. A case study is conducted based on the practical data of the Shanghai Pudong International Airport, where a comparison between the results of GAP and TGAP by the two proposed approaches is demonstrated. It shows that the proposed TGAP model and solution approaches can not only enhance the service for transfer passengers but also improve the gate utilization efficiency in the hub airport.
Cutaneous Wound Healing: A Review about Innate Immune Response and Current Therapeutic Applications
Authors: Adib Yara , Bensussan Armand , Michel Laurence
Published: April 2022

Skin wounds and compromised wound healing are major concerns for the public. Although skin wound healing has been studied for decades, the molecular and cellular mechanisms behind the process are still not completely clear. The systemic responses to trauma involve the body’s inflammatory and immunomodulatory cellular and humoral networks. Studies over the years provided essential insights into a complex and dynamic immunity during the cutaneous wound healing process. This review will focus on innate cell populations involved in the initial phase of this orchestrated process, including innate cells from both the skin and the immune system.
Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm
Authors: Ren Jie , Li Xiaolong
Published: April 2022

In recent years, with the continuous development of IT industry, network virtualization technology has gradually developed into a hotspot of research and application in the field of communication because IT allows the construction of customized virtual network (VN) on shared physical infrastructure. In particular, the proposal of wireless network virtualization for the development of the enhanced Internet. Because these technologies require effective algorithms named virtual network mapping (VNE) to instantiate virtual networks on the SN, on the basis of satisfying the isolation between wireless network slices, a resource allocation mechanism based on bilateral auction is proposed. At present, many researchers have proposed that network virtualization technology is a feasible way to solve the rigid Internet architecture. In the field of existing research focuses on the cable network virtualization, mainly related to the data center network and backbone, then, the side of the wireless access network virtualization the key technology research is still in its infancy, in order to accelerate the process of wireless skill for innovation and meet the demand of the difference of the emerging business, wireless network virtualization becomes the research hotspot and focus of academia and industry. However, no one has applied the dynamic resource allocation algorithm to wireless network virtualization resource sharing field. Here, this paper analyzes and summarizes the existing virtual network resource allocation schemes at home and abroad. The wireless virtual network resource allocation model and cross-domain virtual network resource allocation model are established, and the network centrality theory in social network and complex network is introduced to analyze the topology properties of virtual network and physical network, and two efficient virtual network resource allocation methods are proposed.
Risk Factors of Recurrent Stroke in Young and Middle-Aged Stroke Patients after Interventional Therapy
Authors: Dai Xin , Wang Fang , Lv Haiyang , Cheng Xiuling
Published: April 2022

Objective. To explore the risk factors of recurrent stroke in young and middle-aged stroke patients after interventional therapy. Methods. Retrospective analysis was conducted on the data of 300 young and middle-aged stroke patients treated in our hospital (February 2015–February 2017). All patients received interventional therapy. They were followed up continuously after the interventional therapy, with recurrent stroke as the only endpoint event, and those who did not have the endpoint events were followed up for 5 years. Then, the patients were divided into the occurrence group and the nonoccurrence group according to whether there was a stroke. The social demographic data and clinical examination data of all patients were collected to analyze the differences between the groups. Logistic regression analysis was performed on the factors with statistically significant differences to verify the factors affecting recurrent stroke in young and middle-aged stroke patients after interventional therapy. Results. Among the 300 patients, 69 (23.0%) had recurrent stroke and 231 (77.0%) had no recurrent stroke. The occurrence group (

n
=
69

) had 12 cases (17.4%) of massive cerebral infarction, 18 cases (26.1%) of cerebral watershed infarction, 5 cases (7.2%) of multiple cerebral infarction, 25 cases (36.2%) of lacunar infarction, and 9 cases (13.0%) of TIA. Notable differences were observed in age, drinking history, marital status, body weight, diastolic pressure, systolic pressure, fasting blood glucose, glycosylated hemoglobin, cholesterol, and fibrinogen between the occurrence group and the nonoccurrence group (

P
<
0.05

). The binary logistic regression analysis showed that age, drinking history, diastolic pressure, fasting blood glucose, glycosylated hemoglobin, cholesterol, and fibrinogen were the influencing factors of recurrent stroke in young and middle-aged stroke patients after interventional therapy. Conclusion. Blood glucose, blood lipid, blood pressure, age, and living habits have an impact on recurrent stroke in young and middle-aged patients after interventional therapy. Therefore, while strictly controlling blood glucose, blood lipid, and blood pressure, patients should improve their living habits and enhance the awareness of prevention after interventional therapy.
Effect of Ti-Based Thin Solid Films on Tribological and Mechanical Properties of AL7075-T7351
Authors: Kim Dohyeon , Kim Hyunji , Im Sunghoon , Jeong Hyomin , Noh Jungpil , Huh Sunchul
Published: April 2022

Aluminum alloy itself does not provide sufficient wear resistance for structural parts. Therefore, there is need to protect against the wear issue in practical applications. The objective of this study is to investigate different parameters wear characteristics of direct current (dc) magnetron sputtered titanium films on Al7075-T7351 using a wear test. Each parameter has three levels which include the direct current (DC) power, deposition time, and substrate surface roughness. The thickness of the coatings was measured using a focused ion beam scanning electron microscopy (FIB-SEM) from approximately 0.5

μ
m

to 4.4

μ
m

. The titanium thin film coatings were then evaluated using a PD102 wear tester under conditions of 60 rpm, 2N, 30 minutes. As a result, the coefficient of friction of the coating was reduced, and the wear resistance of the coating was improved as the applied power and deposition time increased. The hardness of titanium-coated specimens is increased significantly up to 272 HV, while the hardness of uncoated specimens was 160 HV.
Magnesium Hydride Ameliorates Endotoxin-Induced Acute Respiratory Distress Syndrome by Inhibiting Inflammation, Oxidative Stress, and Cell Apoptosis
Authors: Shi Xuan , Zhu Lina , Wang Sheng , Zhu Wanli , Li Quanfu , Wei Juan , Feng Di , Liu Meiyun , Chen Yuanli , Sun Xuejun , Lu Hongtao , Lv Xin
Published: April 2022

Acute respiratory distress syndrome (ARDS) causes uncontrolled pulmonary inflammation, resulting in high morbidity and mortality in severe cases. Given the antioxidative effect of molecular hydrogen, some recent studies suggest the potential use of molecular hydrogen as a biomedicine for the treatment of ARDS. In this study, we aimed to explore the protective effects of magnesium hydride (MgH2) on two types of ARDS models and its underlying mechanism in a lipopolysaccharide (LPS)-induced ARDS model of the A549 cell line. The results showed that LPS successfully induced oxidative stress, inflammatory reaction, apoptosis, and barrier breakdown in alveolar epithelial cells (AEC). MgH2 can exert an anti-inflammatory effect by down-regulating the expressions of inflammatory cytokines (IL-1β, IL-6, and TNF-α). In addition, MgH2 decreased oxidative stress by eliminating intracellular ROS, inhibited apoptosis by regulating the expressions of cytochrome c, Bax, and Bcl-2, and suppressed barrier breakdown by up-regulating the expression of ZO-1 and occludin. Mechanistically, the expressions of p-AKT, p-mTOR, p-P65, NLRP3, and cleaved-caspase-1 were decreased after MgH2 treatment, indicating that AKT/mTOR and NF-κB/NLRP3/IL-1β pathways participated in the protective effects of MgH2. Furthermore, the in vivo study also demonstrated that MgH2-treated mice had a better survival rate and weaker pathological damage. All these findings demonstrated that MgH2 could exert an ARDS-protective effect by regulating the AKT/mTOR and NF-κB/NLRP3/IL-1β pathways to suppress LPS-induced inflammatory reaction, oxidative stress injury, apoptosis, and barrier breakdown, which may provide a potential strategy for the prevention and treatment of ARDS.
Psychological Stress Identification and Evaluation Method Based on Mobile Human-Computer Interaction Equipment
Authors: Zhang Na
Published: April 2022

Since the 1980s, the research of artificial neural networks in the field of artificial intelligence has become more and more common. It accepts nonlinear parallel processing, has strong learning and flexibility, and can be used for influencing factor analysis. The ideal power values and triggers are obtained in the Hopfield network model using genetic algorithm, which best avoids the drawbacks of the Hopfield network model instillation learning method. Through the BP of mobile human-computer interaction equipment, hereditary, genetic algorithms, and Hi-PLS regression method in the artificial neural network, the psychological pressure of college students is identified, evaluated, and predicted from three dimensions such as learning, life, and personal events. This makes it possible to understand the current physical and mental conditions of the students in a timely manner, guide to relieve anxiety and fear, and reach a safe psychological level. The three test results are less than 1%, which has high research significance and value.
Intervention of Fluency and Anxiety in Mindfulness Training of Shooting
Authors: Liu Wei
Published: April 2022

Introduction. Mindfulness cognitive therapy is based on mindfulness decompression, integrating the elements of cognitive behavioral therapy and related psychological education components, a set of mindfulness group courses designed. Objective. In order to explore the influence of mindfulness training on fluency and anxiety in shooting sports training. Methods. There are 22 athletes in a provincial shooting team, 12 in the experimental group and 10 people in the control group, grouped according to the random principle. A single-participant experiment design with multiple baseline levels of ABA was adopted. Results. The fluency state of the athletes has increased from 28.75 to 30.63; the average value before the intervention increased by 6.5%,

PEM
=
88
%

, explaining that the previous intervention has a moderate-intensity effect. The average value of athletes’ sports competition anxiety state 205 before intervention was reduced to 171.25, reduced by 16.5%,

PEM
=
100
%

, showing that the intervention effect is very effective. Conclusions. After the shooting athletes received the intervention of the mindfulness cognitive intervention method MBCT, the state of fluency is improved, the level of competition anxiety is reduced, and the experimental intervention basically confirmed the research hypothesis. This study confirms the moderating role of emotion regulation self-efficacy between mindfulness and the fluency of shooters and provides further impetus for the refinement and development of a push-up spiral model that explains mindfulness mechanisms.
Estimations in a Constant‐Stress Partially Accelerated Life Test for Generalized Rayleigh Distribution under Type‐II Hybrid Censoring Scheme
Authors: Rabie Abdalla , Hussam Eslam , Muse Abdisalam Hassan , Aldallal Ramy Abdelhamid , Alharthi Amirah Saeed , Aljohani Hassan M.
Published: April 2022

The constant‐stress partially accelerated life test (CSPALT) model with Type‐II hybrid censoring scheme (Type‐II HCS) is the subject of our research. Units have a lifetime that follows the generalized Rayleigh distribution. Bayesian and E‐Bayesian estimates are derived by applying two of the loss functions, mainly the squared error loss (SEL) and LINEX loss functions. Bayesian and E‐Bayesian estimates are obtained using Markov chain Monte Carlo (MCMC) methods. To prove the applicability and the importance of the subject, a test for real data will be provided. To evaluate the distribution’s effectiveness, we utilized a variety of datasets and proposed several kinds of censoring. Finally, all results are compared in order to determine the effectiveness of the proposed methods. All major findings are concluded in the conclusion section.
Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning
Authors: Aqil M. , Azrai M. , Mejaya M. J. , Subekti N. A. , Tabri F. , Andayani N. N. , Wati Rahma , Panikkai S. , Suwardi S. , Bunyamin Z. , Roy E. , Muslimin M. , Yasin M. , Prakasa E.
Published: April 2022

Hybrid maize seed production is a relatively complex task due to the coexistence of three distinct types of maize plants in the field: female, male, and contaminant/off-type plants. Female and contaminant/off-type plants’ tassels should be removed immediately following flowering initiation, while male tassels should be retained to allow cross-pollination between male and female plants. Therefore, development of an intelligent tassel classification system is deemed critical for hybrid purity decision-making. The research’s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. Tenfold cross-validation was used to evaluate the model performance. Cloud computing was also investigated using EfficientNet to compare the predictive performance of the models. The models’ performance was assessed using four metrics: accuracy, AUC, precision, and recall. The results depicted an appropriate developed model that properly distinguished male, female, and contaminant plants. The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. Among all the evaluated CNN architecture and stacking models, Inception V3-embedded images with logistic regression metaclassifier outperformed other models with accuracy of about 98%. SqueezeNet and EfficientNet provided comparable results for consistent tassel classification with slightly lower performance measures. The model was also subjected to a multidimensional scaling (MDS) analysis to investigate and comprehend misclassification. Male and female plants are clearly distinguished by MDS, but female and off-type/contamination plants are ambiguous. This indicates that the prediction errors were caused by highly similar data features among female and off-type images. The developed modern plant phenotyping model can be used to assist breeders/technicians in maintaining the quality of large-scale hybrid maize seed production activities in Indonesia.
Alternative External Resource Allocation Method to Information Security in Smart Cities
Authors: Li Jun , Zou Kai , Xing Lining
Published: April 2022

There are alternative external resources among smart cities; that is, no matter which cities are invaded illegally, other cities can avoid losses. Keeping this in mind, urban resource allocation is studied. To be specific, the external resource substitution rate model among smart cities is introduced when describing and modeling the problem, and then, the influences of the independent strategy and joint strategy are analyzed. Based on a comparison of the Nash equilibrium solutions for the above two strategies, a cooperative mechanism is proposed to deal with the problem of overallocation of resources in the process of independent strategy. Finally, the conclusions are verified by numerical experiments.
The Effect of Different Tillage Methods and Nitrogen Chemical Fertilizer on Quantitative and Qualitative Characteristics of Corn
Authors: Imani Reza , Samdeliri Morteza , Mirkalaei Amirabbas Mousavi
Published: April 2022

Selection of suitable tillage and application of optimal nitrogen fertilizer are essential to achieve optimal efficiency in crop management. In order to investigate the effect of tillage and nitrogen fertilizer on photosynthetic pigments and quantitative and qualitative traits of corn grain, an experiment was conducted in the research farm of Islamic Azad University, Chalous Branch, Mazandaran Province, Iran, in 2016 and 2017. Experimental factors were tillage at three levels, including no-tillage (NT), conservation tillage (MT), and conventional tillage (CT) in the main plot, and nitrogen fertilizer at four levels (no nitrogen fertilizer application and 33, 66, and 100% of the recommended amount of nitrogen fertilizer) in a subplot. The results showed that, in tillage treatments, increasing nitrogen fertilizer application increased photosynthetic pigments. Carotenoids and chlorophyll b were not significantly different in CT and MT treatments. Nitrogen and grain protein, yield components, and biological yield increased with the increasing use of recommended nitrogen fertilizer. The highest grain and biological yields in MT in the second year were observed at 11633.15 and 16644.16 kg·ha1, respectively. Grain and biological yield in the second year than the first year were further increased in NT and MT treatments compared to CT. Yield in MT and NT treatments improves over time. Due to the time limit in land preparation in the study area, the use of MT with 100 and 66% of the recommended nitrogen fertilizer can replace CT in the area.
Biomaterials Used for Periodontal Disease Treatment: Focusing on Immunomodulatory Properties
Authors: Garzón H. , Suárez L. J. , Muñoz S. , Cardona J. , Fontalvo M. , Alfonso-Rodríguez C. A.
Published: April 2022

The growing use of biomaterials with different therapeutic purposes increases the need for their physiological understanding as well as to seek its integration with the human body. Chronic inflammatory local pathologies, generally associated with infectious or autoimmunity processes, have been a current therapeutic target due to the difficulty in their treatment. The recent development of biomaterials with immunomodulatory capacity would then become one of the possible strategies for their management in local pathologies, by intervening in situ, without generating alterations in the systemic immune response. The treatment of periodontal disease as an inflammatory entity has involved the use of different approaches and biomaterials. There is no conclusive, high evidence about the use of these biomaterials in the regeneration of periodontitis sequelae, so the profession keeps looking for other different strategies. The use of biomaterials with immunomodulatory properties could be one, with a promising future. This review of the literature summarizes the scientific evidence about biomaterials used in the treatment of periodontal disease.