bluuebunny/crossref_metadata_2025 huggingface dataset:
System Design and Simulation for Square Dance Movement Monitoring Based on Machine Learning
Authors: Lei Ping
Published: May 2022

Since the reform and opening, China’s economy has grown rapidly, and people’s living standards have improved significantly. As one of the most effective ways to implement national fitness, square dance has gradually become the main lifestyle of urban communities, an important part of China’s sports construction, and an important indicator to reflect the fitness of the masses and the construction of a well-off society in an all-round way. On the other hand, with the rapid development of internet of things technology, many people can use intelligent bracelets based on machine learning technology to realize motion detection. This technology is also applicable in square dance, which is of great significance to exercise and protect health. This paper first reviews the research status of the internet of things communication protocol and cloud platform, then introduces and analyzes the MQTT communication protocol and Netty high-performance network framework, and studies the integration technology of the internet of things and machine learning. Then, according to the characteristics of the internet of things, a scheme to realize data preprocessing is proposed. The value to be completed is calculated based on the correlation of other attributes corresponding to the k-nearest neighbor model (KNN) and the regression model. Finally, the machine learning algorithm is used to train the results of the three models to obtain the final filling value. The whole scheme design allows the machine learning algorithm to obtain relatively high-quality data in the internal environment. This paper designs a sports monitoring data system for square dance by combining machine learning and internet of things technology, so as to promote national fitness.
AG600 Amphibious Aircraft Selection Model Based on Improved Fuzzy Evaluation Algorithm
Authors: Du Baoku , Xiong Wei , Du Hongwang
Published: May 2022

Aiming at the selection research gap of AG600 amphibious aircraft for salvage of life at sea, the selection index system of AG600 has been established, and an interval intuitionistic fuzzy selection decision model based on fuzzy entropy and score function is constructed. An evaluation index system with 8 indexes was determined based on the rules of the applicability, safety, and accessibility by questionnaire and statistical analysis. Different from the traditional method of determining the fuzzy number of each index solely by experts’ scoring, this paper establishes a 5-grade fuzzy evaluation grade and index membership function, and the interval intuitionistic fuzzy number of the index is determined by combining subjective and objective methods. Fuzzy entropy and score function are used to calculate the weights of each index and AG600 selection score for different accidents, so as to obtain the matching degree of AG600 selection in marine life accidents more scientifically and reasonably. Finally, the effectiveness of the proposed model is demonstrated by a practical case.
Secure IoT Healthcare Architecture with Deep Learning‐Based Access Control System
Authors: Thilagam K. , Beno A. , Lakshmi M. Vanitha , Wilfred C. Bazil , George Santhi M. , Karthikeyan M. , Peroumal Vijayakumar , Ramesh C. , Karunakaran Prabakaran
Published: May 2022

The existing healthcare system based on traditional management involves the storage and processing of large quantities of medical data. The incorporation of the Internet of Things (IoT) and its gradual maturation has led to the evolution of IoT‐enabled healthcare with extraordinary data processing capability and massive data storage. Due to the advancement in the Industrial Internet of Things (IIoT), the resulting system is aimed at building an intelligent healthcare system that can monitor the medical health of the patient by means of a wearable device that is monitored remotely. The data that is gathered by the wearable IoT module is stored in the cloud server which is subject to privacy leakage and attacks by unauthorized users and attackers. To address this security issue, an IoT‐based deep learning‐based privacy preservation and data analytics system is proposed in this work. Data is collected from the user, and the sensitive information is segregated and separated. Using a convolutional neural network (CNN), the health‐related information is analyzed in the cloud, devoid of users’ privacy information. Thus, a secure access control module is introduced that works based on the user attributes for the IoT‐Healthcare system. A relationship between the users’ trust and attributes is discovered using the proposed work. The precision, recall, and F1 score of the proposed CNN classifier are achieved at 95%. With the increase in the size of the training set, higher performance is attained. When data augmentation is added, the system performs better without data augmentation. Further, the accuracy of around 98% is achieved with an increased user count. Experimental analysis indicates the robustness and effectiveness of the proposed system with respect to low privacy leakage and high data integrity.
Quality Assessment of Physical Education Teaching in Colleges and Universities Based on Joint Neural Network
Authors: Tan Chengwei
Published: May 2022

In order to improve the accuracy of physical education teaching quality evaluation in colleges and universities, a new JNE teaching quality evaluation model of physical education is proposed in colleges and universities. Firstly, a set of multi-index evaluation system of college physical education teaching quality based on analytic hierarchy process is constructed from the four dimensions of teaching content, teaching method, teaching attitude, and teaching effect. The score data are used as the input vector of joint neural network (JEN), and the quality level of college physical education (excellent, good, average, and poor) is used as the output vector of JNE, and a teaching quality evaluation model of college physical education courses based on the JNE model is established. Compared with PSO-JEN and JEN, it is observed that JNE has higher classification accuracy, specificity, and sensitivity for teaching quality evaluation of physical education courses in colleges and universities.
On Fundamental Algebraic Characterizations ofμ-Fuzzy Normal Subgroups
Authors: Masmali Ibtisam , Shuaib Umer , Razaq Abdul , Fatima Areeba , Alhamzi Ghaliah
Published: May 2022

In this article, we present the study ofμ-fuzzy subgroups and prove numerous fundamental algebraic attributes of this newly defined notion. We also define the concept ofμ-fuzzy normal subgroup and investigate many vital algebraic characteristics of these phenomena. In addition, we characterize the quotient group induced by this particular fuzzy normal subgroup and establish a group isomorphism between the quotient groupsG/κμandG/κ∗μ. Furthermore, we initiate the study of level subgroup, open level subgroup, and tangible subgroup of aμ-fuzzy subgroup and emphasize the significance ofμ-fuzzy normal subgroups by establishing a relationship between these newly defined notions andμ-fuzzy normal subgroup.
SCU-Net++: A Nested U-Net Based on Sharpening Filter and Channel Attention Mechanism
Authors: Cui Hu , Pan Haiwei , Zhang Kejia
Published: May 2022

U-Net++ is one of the most prominent deep convolutional neural networks in the field of medical image segmentation after U-Net. However, the semantic gaps between the encoder and decoder subnets are still large, which will lead to fuzzy feature maps and even target regions of segmentation. To solve this problem, we propose an improved semantic segmentation model utilizing channel attention mechanism and Laplacian sharpening filter, SCU-Net++: dense skip connections are redesigned with sharpening filters to ease the semantic gaps, and channel attention modules are used to make the model pay more attention on the feature maps that are useful for our pixel-level classification task. Compared with U-Net++, the proposed model obtains a more competitive performance on the Pancreas Segmentation dataset and Liver Tumor Segmentation dataset, while increases a very small number of learnable parameters and thus almost does not make additional training and reasoning costs. The training of the proposed method is carried out in deep supervision mode, which alleviates the problem of gradient disappearance, and pruning mechanism can be activated to accelerate the reasoning speed.
Impact of Kangaroo Mother Care Intervention on Immunological and Pulmonary Functions of Preterm Infants during Breastfeeding
Authors: Yang Juan , Guo Yanan , Dai Yuying
Published: May 2022

Objective. Preterm infants (PTIs) are prone to respiratory failure or other diseases due to immature organ development and poor immunological function. Herein, the effects of Kangaroo Mother Care (KMC) on the immunological and pulmonary functions of PTIs during breastfeeding were investigated in this study. Methods. The study recruited 86 delivery women and their PTIs with preterm pregnancy outcomes, consisting of 46 cases receiving breastfeeding plus KMC intervention (KMC group) and 40 cases receiving breastfeeding plus routine care (control group). The time of first lactation, time of first breastfeeding, and duration of first breastfeeding were observed in both cohorts. The breastfeeding status was assessed using the LATCH system. Maternal psychological status was evaluated by the breastfeeding self-efficacy scale (BSES) and self-rating anxiety/depression scale (SAS/SDS). The growth and development of PTIs were recorded, and the levels of postalbumin (PA), transferrin (TRF), plasma albumin (ALB), immunoglobulin (Ig) A, IgG, IgM, and complement C3 and C4 were measured. The tidal volume (VT), tidal volume per kilogram (VT/kg), minute volume (MV), and minute volume per kilogram (MV/kg) were detected using a pulmonary function tester. Results. The KMC group presented shorter time of first lactation and first breastfeeding than the control group, with longer duration of first breastfeeding (

P
<
0.05

). After intervention, the BSES scores of delivery women were increased, while the SAS and SDS scores were decreased, with more notable improvements in the KMC group (

P
<
0.05

). The levels of PA, TRF, ALB, IgA, IgG, VT, and MV were elevated in PTIs in both groups, with more evident increase in the KMC group than in the control group (

P
<
0.05

). A better growth of PTIs was found in the KMC group than the control group (

P
<
0.05

). Conclusions. The study demonstrated that KMC intervention during breastfeeding could benefit PTIs specifically regarding their immunological and pulmonary functions.
Delay-Sensitive Task Assignment for Spatial Crowdsourcing
Authors: Li Yunhui , Chang Liang , Li Long , Liu Tieyuan , Gu Tianlong
Published: May 2022

In spatial crowdsourcing, mobile workers are recruited to perform special tasks related to location and time. The task assignment has been a focus of the research community due to its importance in spatial crowdsourcing. However, most of the existing efforts focus on optimizing the task requester’s goal and do not consider the requirements and constraints of workers. Unlike the existing methods, we consider task assignment comprising the goals of both the workers and the requesters, that is, the trade-off regarding the reward and travel cost of a worker and the delay of a task. We formulate the delay-sensitive task assignment (DSTA) problem that optimizes the combined goal of minimizing costs and maximizing rewards of a worker; meanwhile, reducing the delay to wait until the task is assigned. The DSTA algorithm based on greed idea (DSTA-G) is proposed to address the problem. To improve the efficiency, we introduce the Geohash algorithm and propose a new solution DSTA-GH, which is scalable to large-scale datasets. Moreover, the task assignment method that only considers the worker’s objective (TAW) is proposed as the baseline. Finally, extensive experiments offer evidence that DSTA and DSTA-GH outperform the baseline in terms of performance and DSTA-GH only takes about 0.03% of the CPU time as compared to DSTA-G.
Numerical Optimization of Drilling Parameters for Gas Predrainage and Excavating-Drainage Collaboration on Roadway Head
Authors: Luo Mingkun , Yang Lei , Wen Haiou , Zhao Dongsheng , Wang Kun
Published: May 2022

In order to improve efficiency of gas drainage, the reasonable layout parameters for borehole on roadway head are investigated. Assuming that the coal mass has a dual pore structure with fractures and pores, a multiphysical field coupling mathematical model is proposed for gas drainage. The governing equations of gas adsorption, seepage, and diffusion are considered. The process of gas preextraction and excavating-extraction collaboration on roadway heading face in 2606 haulage roadway of Zhangcun mine is modeled by finite element (FE) method. The effects of gas drainage under different drilling arrangements are analyzed. The results show that the fastest decreasing rate of gas content occurs at the beginning of drainage, and decreasing rate of gas content tends to be stable in the later stage. After removing the predraining on roadway head, the gas content at the center of coal wall on the roadway heading face will rebound. The included angle between the borehole and the roadway middle line determines the spatial area of gas drainage. Too small included angle will reduce the area of gas content reduction, while too large included angle will result in a blind area on both sides of the roadway. The change in borehole spacing affects the decreasing rate of gas content in the rock and coal around the roadway. Large spacing may cause an inadequate decreasing rate in the middle of the roadway, while small spacing may cause an inadequate decreasing rate in both sides of the roadway. In the field application, the pure gas flow and gas concentration of drainage show a downward trend in whole. The gas concentration decreased from 13.2% to 10.8%, and the pure gas flow decreased from 10.6 m3/min to 8.15 m3/min. Research achievements can provide a basis for gas drainage in underground roadway.
Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things
Authors: Li Xiufeng , Wen Zheng , Hua Qiaozhi
Published: May 2022

Intelligent transportation applications based on urban Internet of Things can improve the efficiency of government services and promote urban modernization. As smart cameras are more and more widely used in cities, artificial intelligence technology is an important force to achieve license plate recognition. An efficient license plate recognition algorithm not only improves the efficiency of traffic management but also saves management costs. This paper proposes a network based on the shufflenetv2 dilated convolution (SDC) model, which includes two parts: license plate location and license plate recognition. SDC model adopts shufflenetv2 as the backbone network, which combines dilated convolution and global context blocks. Therefore, the receptive field and feature expression ability of the model are enhanced. For license plate location, CIOU loss considers not only the coverage area of the bounding box but also the center distance and aspect ratio. For license plate recognition, CTC loss trains the network based on the sequence and solves the sample alignment problem, which improves the accuracy of license plate recognition. The experiments show that the precision of the SDC model in license plate location is 98.7%, which is 5.2%, 5.5%, and 4.1% higher than the precision of Faster-RCNN, YOLOv3, and SSD, respectively. The precision of the SDC model in license plate recognition is 98.2%, which is 5.3%, 3.7%, and 2.9% higher than the precision of LPRNet, AlexNet, and RPNet, respectively.
Heterogeneous Deformation and Microstructure of TWIP Steel under Damage and Fracture
Authors: Dong Fuyuan , Zhang Mingxu , Hou Junfeng
Published: May 2022

Materials have always been a research hotspot in the chemical industry. With the continuous development of technology, the iteration of materials research has become faster and faster. Steel has strong toughness and tensile strength, and it is widely used in construction and other fields. However, one problem that cannot be avoided is the fracture of steel. The purpose of this paper is to analyze the damage and fracture of TWIP steel. In this paper, a microstructure characterization calculation method based on TWIP is proposed, which can effectively analyze the fracture surface, and for the performance of TWIP steel, this paper uses two different TWIP steels for comparative analysis. The results of this paper show that the HDI hardening rate of 22Mn0.6C TWIP steel is higher than that of 22Mn0.6C3Al TWIP steel.
Research on Strategies of Promoting Mental Health of Higher Vocational College Students Based on Data Mining
Authors: Yang Lingfei
Published: May 2022

Since entering the 21st century, my country’s education is facing the transition from traditional exam-oriented education to quality education and personality education, and the problem of mental health has gradually attracted the attention of all sectors of society. Suspension, drop-out, suicide, and crime occur from time to time, and the number of students with psychological disorders is also increasing year by year. Therefore, in-depth research on the mental health of vocational college students and exploration of students’ psychological intervention models have become the focus of colleges and other education departments. For the focus of research works, the application of DM technology in the field of teaching and management in an institution of higher learning has also achieved initial results. Through consulting a lot relevant literature, this paper deeply studies the DM technology and tries to analyze the application of DM technology in the data of psychological problems of higher vocational students. Through experimental comparison, a single algorithm model is used to classify and predict the test data set, and the accuracy rate is 78% after comparing the classification results with known categories. The accuracy of classification results obtained by the fusion algorithm in this paper is 85.1%. The results show that this technology provides help for students’ mental health counseling, mental health education and the prevention of students’ psychological problems in higher vocational colleges. It provides new ideas and methods to solve the mental health problems of higher vocational students and makes the school mental health education more purposeful, targeted, and effective.
On Extremal Spectral Radii of Uniform Supertrees with Given Independence Number
Authors: Zhang Lei , Ren Haizhen
Published: May 2022

A supertree is a connected and acyclic hypergraph. Denote by Tm,n,α the set of m‐uniform supertrees of order n with independent number α. Focusing on the spectral radius in Tm,n,α, this present completely determines the hypergraphs with maximum spectral radius among all the supertrees with n vertices and independence number α for [m − 1/mn] ≤ α ≤ n − 1, which extend the results of Lu et al. from tree to uniform supertree. Our techniques are based on the structure properties of supertrees with given independence number and general edge‐moving operation. As a byproduct, we also determine the hypergraphs with minimum signless Laplacian spectral radius in Tm,n,α.
Research on Embedded Security Control for Smart Home Based on Improved IBE Algorithm
Authors: Zhang Siyuan
Published: May 2022

A smart home control system is a comprehensive control system that cross-fuses electronic technology, etc. In the process of data communication in smart home systems, a communication security system based on the upgraded IBE algorithm is created to address network security issues such as hostile assaults, privacy leaks, and identity theft. The system adopts the IBE algorithm for the encryption design, which can effectively resist security threats such as static password loss and pseudo-server attacks, improve system confidentiality, and make data transmission in the smart home system more secure. The key management, it can be argued, is the foundation of cryptographic security and plays an essential role in encryption behavior.
Study on the Relationship between Different Body Mass Indexes and Puncture Pain and Image Quality in Patients Undergoing Coronary Angiography with Intravenous Indwelling Needle
Authors: Ding Jiani , Chen Xiaoyu
Published: May 2022

Objective. To investigate the effect of different body mass indexes (BMIs) on patients’ puncture pain, puncture success rate, and image quality in coronary angiography (CAG) performed with an intravenous indwelling needle, and to provide a basis for selecting the appropriate intravenous indwelling needle for CAG in patients with different BMIs in an outpatient clinic. Method. In this study, 300 patients undergoing CTA at the department of radiology in the First Affiliated Hospital of Wenzhou Medical University from January to May 2021 were divided into group 1 (BMI 1 ≤ 18.5), group 2 (18.5 < BMI ≤ 24), and group 3 (BMI > 24) according to their BMI, with 100 cases in each group, and a 20 G intravenous indwelling needle was used in each group. The age, sex, height, and weight of each patient were recorded, and the primary puncture success rate, contrast leakage rate, injection success rate, pain perception, and subjective ratings of image quality and objective indicators were compared in patients with different BMI values. Results. There was no statistically significant difference between the age, gender, and heart rate of the patients in the three groups (

P
>
0.05

). There was no statistically significant difference between the primary puncture success rate, injection success rate, and contrast leakage rate of the three groups of patients (

P
>
0.05

). The pain scores of group 3 during contrast injection were significantly higher than those of the remaining two groups (

P
<
0.05

), while the differences between the pain scores of group 2 and group 1 during contrast injection were not statistically significant (

P
>
0.05

); the comparison of the pain scores of the three groups during puncture and during retention was not statistically significant (

P
>
0.05

). The differences between the subjective ratings of image quality and the objective indicators of the three groups were not statistically significant (

P
>
0.05

). Conclusion. The 20 G indwelling needle can basically meet the coronary angiography examination of patients with different body mass indexes, but patients with a BMI greater than 24 are recommended to use a larger diameter indwelling needle to reduce contrast leakage as well as to reduce patient pain and improve patient comfort.
Nonstationary Generalised Autoregressive Conditional Heteroskedasticity Modelling for Fitting Higher Order Moments of Financial Series within Moving Time Windows
Authors: Clerk Luke De , Savel’ev Sergey
Published: May 2022

Here, we present a method for a simple GARCH (1,1) model to fit higher order moments for different companies’ stock prices. When we assume a Gaussian conditional distribution, we fail to capture any empirical data when fitting the first three even moments of financial time series. We show instead that a mixture of normal distributions is needed to better capture the higher order moments of the data. To demonstrate this point, we construct regions (parameter diagrams), in the fourth- and sixth-order standardised moment space, where a GARCH (1,1) model can be used to fit moment values and compare them with the corresponding moments from empirical data for different sectors of the economy. We found that the ability of the GARCH model with a double normal conditional distribution to fit higher order moments is dictated by the time window our data spans. We can only fit data collected within specific time window lengths and only with certain parameters of the conditional double Gaussian distribution. In order to incorporate the nonstationarity of financial series, we assume that the parameters of the GARCH model can have time dependence. Furthermore, using the method developed here, we investigate the effect of the COVID-19 pandemic has upon stock’s stability and how this compares with the 2008 financial crash.
Association between the Use of Biomass as Fuel for Cooking and Acute Respiratory Infections in Children under 5 Years of Age in Peru: An Analysis of a Population‐Based Survey, 2019
Authors: Chávez-Zacarías Renato , Lindo-Cavero Félix , Caira-Chuquineyra Brenda , Fernandez-Guzman Daniel , Delgado-Flores Carolina J. , Toro-Huamanchumo Carlos J. , Urrunaga-Pastor Diego , Bendezu-Quispe Guido
Published: May 2022

Background. Acute respiratory infections (ARIs) are the most frequent respiratory diseases associated with the use of biomass as fuel within the home. ARIs are the main cause of mortality in children under 5 years of age. We aimed to evaluate the association between the use of biomass as cooking fuel and ARI in children under 5 years of age in Peru in 2019. Methods. A secondary data analysis of the 2019 Peru Demographic and Family Health Survey (ENDES) has been performed. The outcome variable was a history of ARI. The exposure variable was the use of biomass as fuel for cooking food. To evaluate the association of interest, generalized linear models from the Poisson family with logarithmic link function considering complex sampling to estimate crude prevalence ratio (cPR) and adjusted prevalence ratio (aPR) with their respective 95% confidence intervals have been performed. P values <0.05 were considered statistically significant. Results. A total of 16,043 children were included in the analysis. Of the total, biomass was used as fuel to cook food in the homes of 3,479 (20.0%) children. Likewise, 2,185 (14.3%) of the children had a history of ARI. In the adjusted model, it was found that children living in homes in which biomass was used as cooking fuel had a greater probability of presenting ARI (aPR = 1.13; 95% CI: 1.01–1.28). Conclusions. It has been found that biomass was used to cook food in two of every 10 households. Likewise, almost one‐seventh of children under 5 years old presented an ARI. The use of biomass as a source of energy for cooking in the home was associated with a higher probability of presenting ARIs.
KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT
Authors: Cong Kai , Li Tao , Li Beibei , Gao Zhan , Xu Yanbin , Gao Fei
Published: May 2022

The Bidirectional Encoder Representations from Transformers (BERT) technique has been widely used in detecting Chinese sensitive information. However, existing BERT-based frameworks usually fail to emphasize key entities in the texts that contribute significantly to knowledge inference. To meet this gap, we propose a BERT and knowledge graph-based novel framework to detect Chinese sensitive information (named KGDetector). Specifically, we first train a pretrained knowledge graph-based Chinese entity embedding model to characterize entities in the Chinese textual inputs. Finally, we propose an effective framework KGDetector to detect Chinese sensitive information, which employs the knowledge graph-based embedding model and the CNN classification model. Extensive experiments on our crafted Chinese sensitive information dataset demonstrate that KGDetector can effectively detect Chinese sensitive information, outperforming existing baseline frameworks.
Avian Diversity and Abundance of Taunsa Barrage Ramsar Site in Punjab, Pakistan
Authors: Haider Muhammad Zeshan , Ahmed Shakeel , Sial Nuzhat , Afzal Gulnaz , Riaz Ayesha , Asif Akhtar Rasool , Mehmood Tahir , Rasheed Abdul , Muhammad Sayyed Aun , Ahmad Hafiz Ishfaq
Published: May 2022

The study of avian species diversity and abundance at the Taunsa Barrage Ramsar site was carried out from September 2019 to March 2020. Taunsa Barrage is an important wetland for international and local migratory birds, including waterfowls, waders, and long-distance migrant avifauna. Data were collected by direct counts made on four-point transects representing a disturbance gradient due to humans and different habitats. Species diversity indices, analysis of variance (ANOVA), and other statistical methods were used to analyze data. A total of 150 avian species of 19 orders and 53 families were recorded. These included 66 migrant species and 84 resident species. The spotting of ten globally threatened species also highlights the importance of wetland for avian species. On average,

1511
±
373

(

mean
±
SD

) species of birds were recorded every month. Shannon’s diversity index indicated that Transect Point 2 had higher species diversity (

H

′

=
3.779

), followed by Transect Points 3, 4, and 1 (

H

′

=
3.769

,

H

′

=
3.491

, and

H

′

=
3.457

, respectively). Evenness index showed that birds evenly distributed in September (

E
=
0.5584

) than November (

E
=
0.310

9). ANOVA showed a significant difference (

F
=
4.800

,

df
=
3

,

P
=
0.002

) of avian diversity and abundance among the transect points. But there was no statistically significant interaction between months (

F
=
1.23

,

df
=
6

,

P
=
0.233

). The results showed that the Taunsa Barrage Ramsar site harbors plenty of resident migratory and threatened bird species. Hence, it is important to protect its habitat and need to protect avian diversity by overcoming major threats.
Greedy Strategies with Multiobjective Optimization for Investment Portfolio Problem Modeling
Authors: Zhang Xinchen , Zhang Linghao , Zhou Qincheng , Jin Xu
Published: May 2022

The ultimate purpose of portfolio investment is to reduce investment risk and improve total return on the premise of ensuring reasonable allocation of capital. In this paper, we build a quantitative model to advise on trading based on the price movement of Bitcoin and gold between 2016 and 2021; our goal is to maximize profit while minimizing risk. We mainly use greedy strategies with multiobjective optimization models. For the purpose of obtaining the correct price trend, some popular trend indicator strategies are referred to predict the future price trend in the medium and long term. In addition, we also consider people with different trading preferences and divided them into aggressive, advanced, balanced, and cautious and provided trading strategies for each of these four groups. This gives our model scalability. Finally, we analyze the sensitivity of the model and discuss the impact of trading commission costs on the model results. The model can be applicable to various investment situations.
Chaos in Cancer Tumor Growth Model with Commensurate and Incommensurate Fractional-Order Derivatives
Authors: Debbouche Nadjette , Ouannas Adel , Grassi Giuseppe , Al-Hussein Abdul-Basset A. , Tahir Fadhil Rahma , Saad Khaled M. , Jahanshahi Hadi , Aly Ayman A.
Published: May 2022

Analyzing the dynamics of tumor-immune systems can play an important role in the fight against cancer, since it can foster the development of more effective medical treatments. This paper was aimed at making a contribution to the study of tumor-immune dynamics by presenting a new model of cancer growth based on fractional-order differential equations. By investigating the system dynamics, the manuscript highlights the chaotic behaviors of the proposed cancer model for both the commensurate and the incommensurate cases. Bifurcation diagrams, the Lyapunov exponents, and phase plots confirm the effectiveness of the conceived approach. Finally, some considerations regarding the biological meaning of the obtained results are reported through the manuscript.
Site Selection Optimization of Reverse Logistics Network for Waste Tires
Authors: Wang Qiang , Li Rong , Jiang Li , Chen Liufen , Wang Yunlong , Wang Guotian
Published: May 2022

In order to solve the problem of reverse recycling of waste tires in Heilongjiang Province, this paper chooses the third party as the core to construct a reverse logistics network system of waste tires based on the current situation of reverse logistics of waste tires. The reverse recycling area of waste tires in Heilongjiang Province is divided into five areas (the first area is Harbin; the second area contains Qiqihar, Daqing, and Suihua; the third area contains Yichun, Hegang, Jiamusi, and Shuangyashan; Qitaihe, Jixi, and Mudanjiang form the fourth area; and the fifth area contains Daxinganling and Heihe). The analytic hierarchy process (AHP) is employed to analyze the influence factors of site selection of reverse logistics network and obtain the total weight of each evaluation factor. The weights of economic conditions, the number of waste tires, traffic conditions, development planning and policy, and geographical conditions are obtained as 0.3547, 0.2256, 0.1690, 0.1644, and 0.0863, respectively. The economic conditions and the number of waste tires with high weights are vital factors for selecting a reverse logistics network for waste tires. The linear mixed-integer programming method (LINGO) is utilized for site selection. Harbin, Qiqihar, Daqing, Mudanjiang, Jiamusi, and Suihua are chosen for the recovery center’s site selection, while Daqing, Mudanjiang, and Suihua are chosen as the site selection of remanufacturing enterprise.
Research on Recommendation of Big Data for Higher Education Based on Deep Learning
Authors: Zhao Ang , Ma Yanhua
Published: May 2022

To improve the recommendation accuracy of educational resources, an intelligent recommendation method based on autoencoder has been proposed by combining intelligent recommendation algorithm and autoencoder. The method uses the dimension reduction advantage of autoencoder to obtain the required feature vector. Then, the prediction score is utilized to recommend educational resources. Finally, it is verified from the algorithmic and system perspective. The results show that this recommendation method is the most efficient method. The efficiency on the dataset is 0.90, respectively. Furthermore, it can score different recommended articles, and the recommendation of different educational resources is realized.
Asymptotic Behavior of Weak Solutions of Nonisothermal Flow of Herschel–Bulkley Fluid to Free Boundary
Authors: Yazid Fares , Saadallah Abdelkader , Ouchenane Djamel , Chougui Nadhir , Abdalla Mohamed , Himadan Ahmed
Published: May 2022

In this manuscript, the behavior of a Herschel–Bulkley fluid has been discussed in a thin layer in ℝ3 associated with a nonlinear stationary, nonisothermal, and incompressible model. Furthermore, the limit problem has been considered, and the studied problem in Ωε is transformed into another problem defined in Ωε without the parameter Ωε (ε is the parameter representing the thickness of the layer tend to zero is studied). We also investigated the convergence of the unknowns which are the velocity, pressure, and the temperature of the fluid. In addition, we established the limit problem and the specific Reynolds equation.
Effect of Yifei-Huoxue Decoction Combined with Tiotropium on Inflammatory Cytokine Levels, Pulmonary Function, and Quality of Life in Patients with Chronic Obstructive Pulmonary Disease
Authors: Sheng Yunjiao , Li Yang , Feng Ting
Published: May 2022

Objective. The main objective is to investigate the effect of Yifei-Huoxue decoction combined with tiotropium on the inflammatory cytokine levels, pulmonary function, and quality of life in patients with chronic obstructive pulmonary disease (COPD). Methods. Ninety-eight COPD patients who were treated in our hospital from January 2021 to January 2022 were selected as the research objects, and they were divided into reference group (routine treatment) and study group (Yifei-Huoxue decoction combined with tiotropium), with 49 cases in each group. The patients’ clinical indexes were compared between the two groups, and the clinical efficacy in the two groups was analyzed. Results. Compared with the reference group, the study group had remarkably lower inflammatory cytokine levels (including tumor necrosis factor-

α

(TNF-

α

), C-reactive protein (CRP), and interleukin-6 (IL-6)) after treatment

P
>
0.05

. In terms of the patients’ pulmonary function indexes after treatment, the study group achieved lower forced vital capacity (FVC), forced expiratory volume in 1s (FEV1), and FEV1/FVC levels compared with the reference group

P
<
0.05

. Both groups had a lower erythrocyte sedimentation rate (ESR) after treatment compared with that before treatment. However, the intergroup difference in the ESRs after treatment was remarkable, and the study group had a much lower ESR compared with the reference group (P < 0.05). Compared with the reference group, the study group achieved much higher 36-item short form health survey (SF-36) scores in dimensions of general health, physical functioning, social functioning, and mental health after treatment (P < 0.05). Conclusion. Yifei-Huoxue decoction combined with tiotropium not only enhances the COPD patients’ pulmonary function but also decreases their inflammatory levels. Therefore, this treatment is conducive to promoting the COPD patients’ recovery and exerts positive effect on improving their quality of life.
Evaluation of College Students’ Ideological and Political Education Management Based on Wireless Network and Artificial Intelligence with Big Data Technology
Authors: Qin Junnan , Wang Yihan , Zhao Qing , Tan Liheng , Luo Yaqi
Published: May 2022

The construction of a correct worldview, outlook on life, and values for students is linked to the development and breakthrough in the management of ideological as well as political education of students. At the same time, college students must be encouraged to follow well-rounded education and struggle to be well-prepared for the challenges of the new era. In order to raise students’ understanding of the critical role that political and ideological education plays in their academic success, it is authoritative that efforts to integrate these two spheres of learning be extended and new encounters made. That’s what prompted this study, which is focused on assessing college students’ level of ideological and political education administration, and it uses a mixture of big data technologies as well as artificial intelligence (AI) to do it. The accuracy of the traditional ideological as well as political education management quality assessment algorithm is not high, feature information extracted by the single-scale neural network (NN) is not rich enough, and the multiscale convolutional network (CN) fusion cannot consider the different values and importance for each scale. In this paper, the convolution kernel of the two-dimensional CN is changed to a one-dimensional convolution kernel, and the multiscale feature fusion CN model MCNN is first designed. The model is optimized and improved, the attention mechanism is integrated, and the MACNN model for the management evaluation of ideological as well as political education is proposed. Besides, this work organizes the network model in a wireless network environment that users can contact and operate at any time.
Micro Nanoengraving Technology and Aesthetic Practice of Architectural Sculpture Art
Authors: Zhu Weili , Wei Dong
Published: May 2022

Micro nanotechnology refers to the emerging technology of material production using a single atomic or molecular structure. The structure and size of materials range from one to 100 microns or nanometers. In this paper, micro nanotechnology is used to study the aesthetic practice of sculpture technology and architectural sculpture art. This paper aims to combine traditional art ideas with science and technology to promote common development. This paper first introduces the micro nanoengraving technology and architectural sculpture art, it includes the definition of works, classification of works, and research on artistic commonalities and differences, and then summarizes the characteristics of micro nanoengraving materials. Finally, this paper will carry out aesthetic practice of sculpture works based on micro nanocreation. Finally, this paper compares with the works created by traditional technology. The results show that the recognition and creativity evaluation of sculpture works based on micro nanocreation have reached more than 8 points. It verifies the effectiveness of the technology.
Research on the Innovation of Ideological and Political Education in Universities Based on Artificial Intelligence Machine Learning in a Wireless Network Environment
Authors: Liu Qian
Published: May 2022

In order to facilitate students’ study and life on campus, the on-campus wireless network has become an essential infrastructure for universities, which has led to an exponential growth of data in the on-campus network. How to use the on-campus network to promote students’ enthusiasm for ideological and political education courses has become a key goal for many universities. As a result, this paper first introduces the innovations developed by universities for better ideological teaching using such a large amount of data, followed by an analysis of the issues that exist. Then, to address the problem that the existing on-campus wireless network data are not fully utilized, we combine machine learning techniques to classify the data on the intranet and analyze the use of on-campus resources based on the classification. Finally, according to students’ use of the intranet, the allocation ratio of on-campus resources is adjusted at different times to increase the attractiveness of ideological and political education courses to students and promote their learning.
Simulation Model of PV System Function in Stand-Alone Mode for Grid Blackout Area
Authors: Ganthia Bibhu Prasad , Dharmaprakash R. , Choudhary Tushar , Muni T. Vijay , Al-Ammar Essam A. , Seikh A. H. , Siddique M. H. , Diriba Abdi
Published: May 2022

PV systems are frequently used in a stand-alone configuration. In a solar PV-based energy-producing system, power fluctuation is a natural occurrence. Alternative sources of energy, including such hybrid grid-tied or energy storage systems, could be discovered when solar PV systems run off-grid to satisfy regional power demands for reliable power supply. This research uses an unusual PV system that can function in both grid-connected and stand-alone states to propose an efficient approach for the power generation challenge in the residential segment. A block of storage battery with sufficient dimensions is included in the system to make sure the constant power supply of such a residential building with an average electricity demand of 10 kWh. An atypical 3.2 kWp PV system and a 19.2 kWh storage battery brick was determined to be capable of meeting the house’s whole daily energy requirements, as well as the defined electrical shutdown times, to simulate the system, which took into account the day load profile, network cutoff times, and monthly radiation from the sun. The collected simulation results showed that during 9 months of each year, the generated PV energy surpasses the load needs, resulting in a maximum battery state-of-charge (SOC) in the range of 74-85%. The generated PV energy is an approximately proportional requirement as during 3 months of minimum solar irradiance (Dec-Feb), whereas the sequence’s SOC differs between

40

and

49
%
,

demonstrating the validity of the proposed photovoltaic system. In January and July, the PV service’s daily energy produced ranges between 2.6 and 5.4 kWh/kWp, corresponding to a conversion efficiency of 90% and 66.25%, correspondingly.
[Retracted] Mutation Detection and Functional Analysis of MSX1, PAX9, AXIN2, and BMP in Nonsyndromic Congenital Missing Teeth Based on Intelligent Image Detection
Authors: Yao Xueqin , Zhang Cheng , Gao Peipei , Meng Zixuan , Hao Yonghong , Yan Jingjing , Yao Wenbo
Published: May 2022

Due to the complexity of clinical manifestations and the lack of standardized diagnostic criteria, it is still difficult to distinguish the etiological types of congenital edentulousness corresponding to genetic defects. This paper studies the application of deep learning image processing and digital image processing in medical images in detail and analyzes the functions of congenital edentulous hotspot genes. The cases in the control group and the study group were collected, and the gene mutations of direct sequence MSX1, PAX9, AXIN2, and BMP were analyzed, and new pathogens were found. The experimental results suggest that PAX9 and MSX1 genes may have a synergistic effect in nonsyndromic congenital edentulous patients. In severely missing teeth, the role of PAX9 may be greater than that of MSX1. The experimental results will help us lay the foundation for further understanding of the disease in the future.
Morphological and Chemical Analysis of Different Types of Calcium Silicate-Based Cements
Authors: Mahmoud Okba , Al-Afifi Nashwan Abdullah , Salihu Farook Mohideen , Ibrahim Maysara Adnan , Al Shehadat Saaid , Alsaegh Mohammed Amjed
Published: May 2022

Objectives. Particle size and shape can influence the properties of materials. However, to improve the physicochemical and biological properties of mineral trioxide aggregate (MTA), silicate-based hydraulic cements were introduced. This study aimed to evaluate and compare the major constituents and crystalline structures along with the surface morphology of different types of calcium silicate-based cement (CSC). Materials and Methods. Six different types of CSC (white Portland cement, white ProRoot MTA, white MTA Angelus, Biodentine, and Endosequence, both putty and paste) were used in this study. Five samples of each material were analyzed in both uncured and cured cement using scanning electron microscopy/energy-dispersive X-ray (SEM/EDX), X-ray diffraction (XRD), and Fourier transform-infrared spectroscopy (FTIR). Results. SEM analysis showed that the surfaces of all materials consisted of particle sizes ranging from 0.194 μm to approximately 51.82 μm. The basic elements found in both uncured and cured cement of all tested materials using EDX were carbon, calcium, silicon, and oxygen. A difference was observed in the presence or absence of magnesium, aluminum, bismuth, zirconium, and tantalum. XRD showed that all tested materials were composed mainly of tricalcium silicate and dicalcium silicate, which are the main components of Portland cement. FTIR analysis showed aromatic rings, phosphine PH, alkyl halides, and alcohol O-H groups in all tested materials but at different wavenumbers. Conclusions. The different types of CSCs tested in this study were primarily modified types of Portland cement with the addition of radiopacifiers. ProRoot MTA and MTA Angelus contained bismuth oxide, Biodentine contains zirconium oxide, whereas Endosequence root repair materials (both putty and paste) contained zirconium oxide and tantalum oxide. Endosequence root repair materials showed smaller particle sizes than the other groups. White PC had the most irregular and large particle sizes. CSC had a smaller particle size, except for MTA Angelus. Clinical Relevance. The composition of CSC has a direct influence on the properties of these cements, which may affect the clinical outcome of the treatment.