bluuebunny/crossref_metadata_2025 huggingface dataset:
Inverse Exponentiated Lomax Power Series Distribution: Model, Estimation, and Application
Authors: Hassan Amal S. , Almetwally Ehab M. , Gamoura Samia C. , Metwally Ahmed S. M.
Published: June 2022

In this paper, we introduce the inverse exponentiated Lomax power series (IELoPS) class of distributions, obtained by compounding the inverse exponentiated Lomax and power series distributions. The IELoPS class contains some significant new flexible lifetime distributions that possess powerful physical explications applied in areas like industrial and biological studies. The IELoPS class comprises the inverse Lomax power series as a new subclass as well as several new flexible compounded lifetime distributions. For the proposed class, some characteristics and properties are derived such as hazard rate function, limiting behavior, quantile function, Lorenz and Bonferroni curves, mean residual life, mean inactivity time, and some measures of information. The methods of maximum likelihood and Bayesian estimations are used to estimate the model parameters of one optional model. The Bayesian estimators of parameters are discussed under squared error and linear exponential loss functions. The asymptotic confidence intervals, as well as Bayesian credible intervals, of parameters, are constructed. Simulations for a one‐selective model, say inverse exponentiated Lomax Poisson (IELoP) distribution, are designed to assess and compare different estimates. Results of the study emphasized the merit of produced estimates. In addition, they appeared the superiority of Bayesian estimate under regarded priors compared to the corresponding maximum likelihood estimate. Finally, we examine medical and reliability data to demonstrate the applicability, flexibility, and usefulness of IELoP distribution. For the suggested two real data sets, the IELoP distribution fits better than Kumaraswamy–Weibull, Poisson–Lomax, Poisson inverse Lomax, Weibull–Lomax, Gumbel–Lomax, odd Burr–Weibull–Poisson, and power Lomax–Poisson distributions.
Existence of Two Solutions for a Critical Elliptic Problem with Nonlocal Term in

ℝ

4
Authors: Sabri Khadidja , Matallah Atika , Benmansour Safia , El Mokhtar Mohammed El Mokhtar Ould
Published: June 2022

In this paper, we prove the existence of two positive solutions for a critical elliptic problem with nonlocal term and Sobolev exponent in dimension four.
A Novel Approach to Predict Brain Cancerous Tumor Using Transfer Learning
Authors: Khan Mohammad Monirujjaman , Omee Atiyea Sharmeen , Tazin Tahia , Almalki Faris A. , Aljohani Maha , Algethami Haneen
Published: June 2022

As the most prevalent and deadly malignancy, brain tumors have a dismal survival rate when they are at their most hazardous. Using mostly traditional medical image processing methods, segmenting and classifying brain malignant tumors is a challenging and time-consuming task. Indeed, medical research reveals that categorization performed manually with the help of a person might result in inaccurate prediction and diagnosis. This is mostly due to the fact that malignancies and normal tissues are so dissimilar and comparable. The brain, lung, liver, breast, and prostate are all studied using imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. This research makes significant use of CT and X-ray imaging to identify brain malignant tumors. The purpose of this article is to examine the use of convolutional neural networks (CNNs) in image-based diagnosis of brain cancers. It expedites and improves the treatment’s reliability. As a result of the abundance of research on this issue, the provided model focuses on increasing accuracy via the use of a transfer learning method. This experiment was conducted using Python and Google Colab. Deep features were extracted using VGG19 and MobileNetV2, two pretrained deep CNN models. The classification accuracy is used to evaluate this work’s performance. This research achieved a 97 percent accuracy rate by MobileNetV2 and a 91 percent accuracy rate by the VGG19 algorithm. This allows us to find malignancies before they have a negative effect on our bodies, like paralysis.
Role of Epstein-Barr Virus and Human Papilloma Virus in the Development of Oropharyngeal Cancer: A Literature Review
Authors: Migliaro Michela , Massuh Daniela , Infante María Fernanda , Brahm Ana María , San Martín María Trinidad , Ortuño Duniel
Published: June 2022

The aim of this review was to describe the association and related mechanisms between HPV, EBV, and the development of oral and oropharyngeal cancer. A search for scientific evidence was carried out in electronic databases (MEDLINE/PubMed, SciELO). It was found that, among the carcinogenic mechanisms of HPV, E6 and E7 proteins are responsible for the malignization process, inhibiting tumor suppressors p53 and pRb. As to EBV, it was noted that its “hit and run” phenomenon manipulates the host epigenetic mechanism, triggering the tumor process without the virus being currently present; a “cellular reprogramming” is essentially generated, causing heritable changes in gene expression without DNA mutation. In conclusion, there is an association between oropharyngeal carcinogenesis and HPV and also between the former and EBV. Further studies are required to clarify the causal mechanisms and impact of both viruses on cancer development and to obtain biomarkers of greater specificity in the case of EBV.
The Association between Physical Activity and Cognitive Function: Data from the China Health and Nutrition Survey
Authors: Huang Qiankun , Zhao Jing , Jiang Weiqing , Wang Wenfeng
Published: June 2022

Background. Decreased cognitive function is a common problem in the old adults, which has high risk of progression to Alzheimer’s diseases (AD) and other dementias. This study was aimed at finding out the association between physical activity and cognitive function. Methods. In total, 1514 participants with the

age
≥
55

years old registered in the China Health and Nutrition Survey (CHNS) database were selected in this study. The association between physical activity and cognitive function was analyzed via the generalized additive model. The association between the variables and the cognitive function score was expressed as

β

coefficient with 95% confidence intervals (CIs). Results. After adjusting age, ethnicity, stratum, marital status, education, memory status, and memory changes, the cognitive function score was increased by 0.011 points for every 1-point increase in domestic score (

β
=
0.011

,

P
=
0.043

). Subgroup analysis indicated that in the female group, for every 1-point increase in the domestic score, the cognitive function score increased by 0.019 points (

β
=
0.019

,

P
=
0.017

). In people with good memory status, each 1-point increase in domestic score increased the cognitive function score of 0.020 points (

β
=
0.020

,

P
=
0.017

). Conclusions. The decreased cognitive function was correlated with decreased domestic physical activity. The increased domestic physical activity was associated with an increased cognitive function in females and people with good memory status. The findings might offer a reference for deep understanding of the association between physical activity and cognitive function.
Knowledge Graph-Enabled Text-Based Automatic Personality Prediction
Authors: Ramezani Majid , Feizi-Derakhshi Mohammad-Reza , Balafar Mohammad-Ali
Published: June 2022

How people think, feel, and behave primarily is a representation of their personality characteristics. By being conscious of the personality characteristics of individuals whom we are dealing with or deciding to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications takes place there. The most prominent tool in such communications is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text, a knowledge graph, which is a set of interlinked descriptions of concepts, was built by matching the input text’s concepts with DBpedia knowledge base entries. Then, due to achieving a more powerful representation, the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph, which is now a knowledgeable alternative for the input text, was embedded to yield an embedding matrix. Finally, to perform personality predictions, the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The results indicated considerable improvements in prediction accuracies in all of the suggested classifiers.
Stability Analysis of Geotechnical Landslide Based on GA-BP Neural Network Model
Authors: Xu Jin , Zhao Yanna
Published: June 2022

Rock and soil landslides, a regular geological disaster in engineering construction, endanger national property and, in severe circumstances, result in a huge number of casualties. A set of methods for landslide stability analysis and prediction has been established, with the academic idea of “geological process mechanism analysis-quantitative evaluation” at its core, combined with detailed field investigation of geological hazards, forming a relatively complete technical route for research on landslide stability analysis. The work of this paper can be summarized as follows: (1) Introduce the research status of geotechnical landslide stability at home and abroad and the current development trend of neural network. (2) Through the collected sample database, take the training function and the number of hidden layer neurons as variables to optimize the BP neural network, and combine the optimized BP neural network with the genetic algorithm to construct the GA-BP neural network. (3) The stability coefficients of the BP neural network, the genetic algorithm based back propagation neural network (GA-BPNN), and the limit equilibrium technique are analyzed and compared. The findings imply that landslide stability can be assessed using neural networks. GA-BPNN is a viable alternative to back propagation neural network (BPNN). The algorithm is more accurate, has a faster convergence rate, and is more stable.
Intrusion Detection Systems in Cloud Computing Paradigm: Analysis and Overview
Authors: Rana Pooja , Batra Isha , Malik Arun , Imoize Agbotiname Lucky , Kim Yongsung , Pani Subhendu Kumar , Goyal Nitin , Kumar Arun , Rho Seungmin
Published: June 2022

Cloud computing paradigm is growing rapidly, and it allows users to get services via the Internet as pay‐per‐use and it is convenient for developing, deploying, and accessing mobile applications. Currently, security is a requisite concern owning to the open and distributed nature of the cloud. Copious amounts of data are responsible for alluring hackers. Thus, developing efficacious IDS is an imperative task. This article analyzed four intrusion detection systems for the detection of attacks. Two standard benchmark datasets, namely, NSL‐KDD and UNSW‐NB15, were used for the simulations. Additionally, this study highlights the proliferating challenges for the security of sensitive user data and gives useful recommendations to address the identified issues. Finally, the projected results show that the hybridization method with support vector machine classifier outperforms the existing techniques in the case of the datasets investigated.
Multimedia Technology Based on Cloud Computing in Aerobics Teaching
Authors: Guo Xiuqing
Published: June 2022

Multimedia teaching has been widely introduced in college education and has become an important teaching aid. Cloud computing has relatively few applications in China’s education field, but it has developed rapidly in recent years. The main feature of cloud computing lies in the use of virtualization technology to integrate various software and hardware resources and provide them to users via the Internet. In recent years, the application of multimedia technology has expanded from professional courses and cultural courses to the courses of sound, body, and beauty, which greatly enriched the teaching content, activated the classroom atmosphere, and improved the teaching efficiency. Multimedia teaching can be based on clear learning objectives, rational selection, and use of modern digital media, targeted for students’ new aerobics education. Applying multimedia technology to college aerobics teaching is an important opportunity for the reform of aerobics education. Aerobics is a well-loved and widely popular sports event that integrates group gymnastics, dance, music, fitness, and entertainment. This paper introduces the difficulties of current multimedia technology and explains how to effectively carry out aerobics teaching, focusing on how to combine multimedia technology with aerobics teaching to promote the vigorous development of aerobics education. Taking the aerobics teaching in colleges and universities as an example, this paper mainly analyzes the shortcomings of current aerobics teaching in colleges and universities in China. Applying the strategy can bring certain enlightenment to the aerobics teaching in colleges.
Innovative Bipolar Fuzzy Sine Trigonometric Aggregation Operators and SIR Method for Medical Tourism Supply Chain
Authors: Riaz Muhammad , Pamucar Dragan , Habib Anam , Jamil Nimra
Published: June 2022

Bipolar fuzzy sets (BFSs) are effective tool for dealing with bipolarity and fuzziness. The sine trigonometric functions having two significant features, namely, periodicity and symmetry about the origin, are helping in decision analysis and information analysis. Taking the advantage of sine trigonometric functions and significance of BFSs, innovative sine trigonometric operational laws (STOLs) are proposed. New aggregation operators (AOs) are developed based on proposed operational laws to aggregate bipolar fuzzy information. Certain characteristics of these operators are also discussed, such as boundedness, monotonicity, and idempotency. Moreover, a modified superiority and inferiority ranking (SIR) method is proposed to cope with multicriteria group decision-making (MCGDM) with bipolar fuzzy (BF) information. To exhibit the relevance and feasibility of this methodology, a robust application of best medical tourism supply chain is presented. Finally, a comprehensive comparative and sensitivity analysis is evaluated to validate the efficiency of suggested methodology.
Automatic Traffic Anomaly Detection on the Road Network with Spatial-Temporal Graph Neural Network Representation Learning
Authors: Zhang Hengyuan , Zhao Suyao , Liu Ruiheng , Wang Wenlong , Hong Yixin , Hu Runjiu
Published: June 2022

Traffic anomaly detection is an essential part of an intelligent transportation system. Automatic traffic anomaly detection can provide sufficient decision-support information for road network operators, travelers, and other stakeholders. This research proposes a novel automatic traffic anomaly detection method based on spatial-temporal graph neural network representation learning. We divide traffic anomaly detection into two steps: first is learning the implicit graph feature representation of multivariate time series of traffic flows based on a graph attention model to predict the traffic states. Second, traffic anomalies are detected using graph deviation score calculation to compare the deviation of predicted traffic states with the observed traffic states. Experiments on real network datasets show that with an end-to-end workflow and spatial-temporal representation of traffic states, this method can detect traffic anomalies accurately and automatically and achieves better performance over baselines.
Effect of Admission Serum Calcium Levels and Length of Stay in Patients with Acute Pancreatitis: Data from the MIMIC-III Database
Authors: Wang Dongyan , Guo Xiaoyan , Xia Wenwen , Ru Zhijuan , Shi Yihai , Hu Zhengyu
Published: June 2022

Objective. We retrospectively investigated the effect of admission serum calcium levels on length of stay (LOS) in patients hospitalized with acute pancreatitis (AP). Methods. Clinical data for 3156 patients diagnosed with AP were obtained from the Multiparametric Intelligent Monitoring in Intensive Care III (MIMIC-III) database. Restricted cubic spline curve (RCS) functions of dose-response analysis curves and logistic regression analysis were used to analyze the relationship between admission serum calcium levels and the LOS. Results. All patients were divided into 2 groups (<8.5 mg/dl group and ≥8.5 mg/dl group) based on RCS analysis. RCS showed a significant nonlinear negative correlation between blood calcium levels and the LOS (

p
<
0.001

). In addition, compared with patients with blood calcium <8.5 mg/dl, multivariate logistic regression analysis showed that patients with blood calcium ≥8.5 mg/dl had a reduced risk of the LOS >2 days (aOR = 0.653; 95% CI 0.507–0.842;

p
=
0.001

), a reduced risk of the LOS >5 days (aOR = 0.589; 95% CI 0.503–0.689;

p
<
0.001

), and a reduced risk of the LOS >7 days (aOR = 0.515; 95% CI 0.437–0.609;

p
<
0.001

). And similar results were found in the subgroup analysis. Conclusion. Our findings suggest that low blood calcium increases the LOS in patients with AP. More attention is needed for patients with combined low blood calcium levels (<8.5 mg/dl) in hospitalized AP patients.
How Travel Vlogs on YouTube Influence Consumer Behavior: A Use and Gratification Perspective and Customer Engagement
Authors: Silaban Pantas H. , Chen Wen-Kuo , Nababan Tongam Sihol , Eunike Ixora Javanisa , Silalahi Andri Dayarana K.
Published: June 2022

YouTube is one of the emerging technologies that impact consumer behavior. In the tourism sector, travel video blogs (travel vlogs) have become increasingly popular since they are highly influential in influencing travel behavior. Similarly, YouTube travel vlogs are highly engaging and provide consumers with destination experiences. This study determines how consumer behavior expressed through emerging technologies, such as travel vlogs on YouTube, can influence travel intention and electronic-Word of Mouth (e-WOM) behavior. By incorporating the use and gratification (U&G) perspective, this study examines the role of information seeking and entertainment as motivational factors for watching travel vlogs as a source of satisfaction. It will, in turn, lead to a greater degree of engagement. The current study investigated the emotional and presence aspects of travel videos to determine engagement. To evaluate the research hypothesis, we collected data from 300 respondents who had traveled to a destination after watching a travel vlog. We tested the hypothesis by utilizing structural equation modeling, a feature of the Smart-PLS 3.0 software. The results indicate that consumer motivations for watching travel vlogs, including the desire for information and entertainment, significantly impact customer engagement, both emotion and presence. Customers who are emotionally engaged and perceive travel vlogs as having a high presence will significantly affect their travel intention behavior and influence them to share e-WOM. In addition, the current study provides insight into tourism managers and academics on how travel vlogs as emerging technologies influence consumer behavior.
Effects of Anticholinergic Drugs on Visual Acuity of Patients with Tracheal Intubation under General Anesthesia
Authors: Song Jinmei , Shen Wenchao , Cheng Yi , Zheng Yunhui
Published: June 2022

Background. General anesthesia (GA) is the core means of surgical intervention, mainly used for analgesia and anxiety relief. Therefore, it is necessary to understand the laboratory and clinical research results during induction of GA. Penehyclidine hydrochloride (PHCD) combined with atropine sulfate (Atr) has the potential to induce GA. However, the role of PHCD combined with Atr during tracheal intubation under GA remains unclear. Objective. The research is aimed at exploring the effects of preoperative PHCD or Atr on adverse reactions (ARs) in patients during tracheal intubation under general anesthesia (GA). Methods. This study retrospectively enrolled 473 patients who underwent surgery under GA induction and divided them into a research group (

n
=
234

) and a control group (

n
=
239

) according to preoperative use of PHCD (with or without). Both groups of patients were given Atr postoperatively and nursing intervention. Anesthesia-related indexes, ARs, and hemodynamics were observed and compared between the two groups. Results. There were no significant differences in anesthesia-related indexes and hemodynamics between the research group and the control group. The incidence of blurred vision and diplopia in the research group was higher than that in the control group. Conclusion. Preoperative PHCD combined with postoperative Atr should be avoided in clinical practice, or Atr rather than PHCD should be used preoperatively, so as to reduce the occurrence of blurred vision, diplopia, and other ARs.
Ecological Efficiency Analysis of Ice and Snow Environmental Sports Industry Based on Input-Output Evaluation System
Authors: Lian Hongye , Peng Di
Published: June 2022

There are still some problems in China's ice and snow environmental sports industry, such as an unbalanced industrial structure and insufficient utilization of ecological resources. In order to achieve leap-forward development, the ice and snow sports industry needs to further optimize the allocation of input-output resources, enhance the overall technical capability, and improve the efficiency level of the industry. This study takes five cities of Heilongjiang, Jilin, Liaoning, Hebei, and Beijing as the research objects, constructs the input-output evaluation system of the ice-snow sports industry, measures the input-output-efficiency level of ice-snow environmental sports industry in China, and studies and analyzes the relationship between efficiency level and industrial input-output. Furthermore, this paper compares and analyzes the traditional total factor productivity and green total factor productivity of the ice and snow environmental sports industry and discusses the influencing mechanism of ecosystem efficiency of the ice-snow sports industry. The results show that the input/output ratio of different regions in 2018 was Heilongjiang (1.12) > Liaoning (1.09) > Beijing (1.08) > Hebei (1.06) > Jilin (0.81). The change in input and output level is the direct influencing factor leading to the change of efficiency, while efficiency is the core to promote the development of the ice and snow environmental sports industry. Comparing traditional and green total factor productivity, it can be found that the GameMaker Language (GML) index of green total factor productivity in Heilongjiang, Jilin, Liaoning, Hebei, and Beijing has increased by 10.50%, 9.59%, 12.22%, 11.34%, and 20.29%, respectively, in 2013. In most cases, the green total factor productivity is higher than the traditional total factor productivity, showing an environment-friendly development. The development of the ice and snow environmental sports industry, talents in the ice and snow tourism industry, urbanization level and promotion of sports cultural products in the ice and snow environment are the main external influencing factors, while the internal influencing factors are mainly reflected in the industrial structure, ownership structure, industrial relevance, industrial agglomeration degree, industrial layout, industrial scale, and differentiation of industrial product.
Music Emotion Classification Method Based on Deep Learning and Improved Attention Mechanism
Authors: Jia Xiaoguang
Published: June 2022

Since the existing music emotion classification researches focus on the single-modal analysis of audio or lyrics, the correlation among models are neglected, which lead to partial information loss. Therefore, a music emotion classification method based on deep learning and improved attention mechanism is proposed. First, the music lyrics features are extracted by Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec method, and the term frequency weight vector and word vector are obtained. Then, by using the feature extraction ability of Convolutional Neural Network (CNN) and the ability of Long Short-Term Memory (LSTM) network to process the serialized data, and integrating the matching attention mechanism, an emotion analysis model based on CNN-LSTM is constructed. Finally, the output results of the deep neural network and CNN-LSTM model are fused, and the emotion types are obtained by Softmax classifier. The experimental analysis based on the selected data sets shows that the average classification accuracy of the proposed method is 0.848, which is better than the other comparison methods, and the classification efficiency has been greatly improved.
Exploring the Spatial Impact of Multisource Data on Urban Vitality: A Causal Machine Learning Method
Authors: Xiao Zhixuan , Li Chengyi , Pan Shihua , Wei Gaoda , Tian Mengmeng , Hu Runjiu
Published: June 2022

Identifying urban vitality is the key to optimizing the urban structure. Previous studies on urban multisource data and urban vitality often assume that they follow a predefined (linear or nonlinear in terms of parameters) relationship, and few studies have explored the causality of urban multisource data on urban vitality. The existing machine learning methods often pay attention to the correlation in the data and ignore the causality. With the continuous emergence of new needs, its disadvantages gradually begin to appear and face a series of urgent problems in interpretability, robustness, and fairness. In this paper, we use a combination of causal inference and machine learning to deeply explore and analyze the causal effects of multisource data on the 16 administrative districts of Shanghai, taking Shanghai as an example. The analysis results show that each data indicator has different degrees of influence on the urban vitality of the 16 administrative districts of Shanghai, resulting in different heterogeneous effects, and through the analysis result, each administrative district can better optimize urban resources and improve urban vitality according to its situation. This discovery guides urban planning and has enlightenment significance for cities seeking construction facility investment and facility construction-oriented development.
Modeling and Mathematical Analysis of Liquidity Risk Contagion in the Banking System
Authors: Mourad Hamza , Fahim Said , Burlea-Schiopoiu Adriana , Lahby Mohamed , Attioui Abdelbaki
Published: June 2022

In recent times, all world banks have been threatened by the liquidity risk problem. This phenomenon represents a devastating financial threat to banks and may lead to irrecoverable consequences in case of negligence or underestimation. In this article, we study a mathematical model that describes the contagion of liquidity risk in the banking system based on the SIR epidemic model simulation. The model consists of three ordinary differential equations illustrating the interaction between banks susceptible or affected by liquidity risk and tending towards bankruptcy. We have demonstrated the bornness and positivity of the solutions, and we have mathematically analyzed this system to demonstrate how to control the banking system’s stability. Numerical simulations have been illustrated by using real data to support the analytical results and prove the effects of different system parameters studied on the contagion of liquidity risk.
Optimal Relay Angle-Based Clustering Routing Protocol for Wireless Sensor Networks
Authors: Hu Huangshui , Guo Yuxin , Wang Chuhang , Gao Dong , Liu Qinxue
Published: June 2022

In this paper, a novel optimal relay angle-based clustering routing protocol called RACR is proposed to balance nodes’ energy consumption and prolong the network lifespan of wireless sensor networks (WSNs). In RACR, each node considers the parameters residual energy, number of neighbors, and average distance with neighbors as a criteria to determine whether to become a cluster head (CH) which cooperates with its neighbors to form a cluster. Afterward, the optimal relay angle is calculated for each CH to find its best relay node according to a target function considering the least energy consumption, which is applied to reduce the search range for finding routing paths. Consequently, all the CHs can find their best next-hop nodes within the determined field according to their residual energy, distance to the next-hop CH, and loads. Iteratively, the CHs obtain their best routing paths to the BS in the end. Simulation results demonstrate its effectiveness of RACR in terms of energy consumption, standard deviation of residual energy, data communication delay, network throughput, and lifespan.
Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means
Authors: Tao Yao , Yong Xingkai , Yang Jiangong , Jia Xuefeng , Chen Wenjun , Zhou Jianli , Wu Yunna
Published: June 2022

Government-invested construction project (GICP) has a great significance to social and economic development but suffered many risks due to its large scale, huge investment, and long construction period. The risks in GICP are complex so as to lead the project to failure; it is extremely urgent to take the risk management of GICP. This study establishes a risk early-warning framework to help the managers to understand the risk threat in advance, which supports them to make proper management strategies for the risk control. The whole framework can be concluded as three parts: information collection, data processing, and result prediction. Firstly, the 16 risk factors of GICP are identified. To express the hesitance of human decision and reduce the information loss in quantification, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are used to collect the experts’ linguistic term and transform them into numerical value. And then, these inputs are simplified into five factors based on principal component analysis (PCA), decreasing the impact of redundancy to risk early-warning. Meanwhile, the warning level is divided based on K-means, which avoids the subjectivity of experience decision. Further, the backpropagation neural network optimized by the genetic algorithm (GA-BP) is used to complete the simulation of risk value. The 75 groups of questionnaire data are used to train the network and the 10 groups are used as test set. The validation of the proposed framework has been verified with an average relative error in 7.2% and the average absolute error in 3.91. Finally, corresponding suggestions to prevent and control the different risks in GICP are put forward.
Fractional-Stochastic Solutions for the Generalized (
2
+
1
)-Dimensional Nonlinear Conformable Fractional Schrödinger System Forced by Multiplicative Brownian Motion
Authors: Albosaily Sahar , Mohammed Wael W. , Ali Ekram E. , Sidaoui R. , Aly E. S. , El-Morshedy M.
Published: June 2022

In this paper, the (

2
+
1

)-dimensional nonlinear conformable fractional stochastic Schrödinger system (NCFSSS) generated by the multiplicative Brownian motion is treated. To get new rational, trigonometric, hyperbolic, and elliptic stochastic solutions, we use two different methods: the sine-cosine and the Jacobi elliptic function methods. Moreover, we use the MATLAB tools to plot our figures to introduce a variety of 2D and 3D graphs to highlight the effect of the multiplicative noise on the exact solutions of the NCFSSS. Finally, we illustrate that the multiplicative Brownian motion stabilizes the solutions of NCFSSS a round zero.
An Experimental Study of ZrO2‐CeO2 Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations
Authors: Vidhya R. , Balakrishnan T. , Kumar B. Suresh , Palanisamy R. , Panchal Hitesh , Angulo-Cabanillas Luis , Shaik Saboor , Saleh B. , Alarifi Ibrahim M.
Published: June 2022

This article experimentally and statistically reports the convective heat transfer performance of a cylindrical mesh‐type heat pipe apparatus filled with ZrO2‐CeO2/water‐ethylene glycol nanofluids. In this regard, ZrO2‐CeO2 nanoparticles were synthesized and characterized through the Scanning Electron Microscope and Powder X‐ray diffraction methods followed by the preparation of hybrid ZrO2‐CeO2 nanofluids of various concentrations ranging from 0.025 to 0.1%. The heat transfer features of a tubular heat pipe with a mixture of the ZrO2‐CeO2 nanofluid were evaluated. A 5.33% decrease in thermal resistance value and a 41.16% increase in heat transfer ability with increased power input were observed. The potent regression models were proposed to estimate heat transfer features of the heat pipe. The ANOVA statistical method has been employed to determine the P value and the F value of the models towards enhancing the reliability and accuracy of the developed models. The outcome revealed that the proposed models are reliable and have the best fit with the experimental data for 30–60 W power. The correlations’ results were validated against the experimental data and showed high accuracy. Moreover, the accuracy of the developed models was ensured through R‐squared and adjusted R‐squared values.
The Construction and Exploration of University English Translation Teaching Mode Based on the Integration of Multimedia Network Technology
Authors: Yuan Liying
Published: June 2022

The teaching mode of English translation in universities has been changed greatly, so the design of teaching mode is needed to be optimized. Besides, the network teaching mode should be studied. Specifically, in this article, the advantages of English translation teaching in network environment are analyzed. Finally, we focus on the effective countermeasures to solve the problems of translation teaching mode and provide the reference values for teaching staff. This paper summarizes the integration of information technology and interpretation teaching according to the evolutionary route of technology application from three aspects: (1) general field, (2) interpretation professional field, and (3) education field. Furthermore, we propose a correlation analysis method of English translation teaching based on data fusion technology. Considering the time-related characteristics of English translation teaching, a perturbation time period division method based on moving time window is proposed based on the actual data and the development results of cutting-edge technologies. Finally, interpretation teaching is foreseen in four aspects: intelligence, simulation, precision, and collaboration.
A Survey on Information-Centric Networking with Cloud Internet of Things and Artificial Intelligence
Authors: Karim Farhan Ahmed , Mohd Aman Azana Hafizah , Hassan Rosilah , Nisar Kashif
Published: June 2022

The Internet is evolving, and data is a critical component of today’s Internet. People are more interested in data than data location. An information-centric network (ICN) uses this idea and makes data, instead of host addresses, an integral component. Another essential topic in the contemporary period is cloud or edge computing, as well as the Internet of Things (IoT) and Artificial Intelligence (AI), which becomes even more critical when combined with ICN. We initially rate the configuration of ICN with cloud or edge IoT and AI (ICN-CIoT-AI) in this study so that readers may learn about the latest trends and merging of ICN-CIoT-AI. As data rates rise and the Internet becomes a requirement for any technology, we require IoT settings in which data can be cached locally, which is possible when ICN collaborates with cloud or edge computing. To make this arrangement more intelligent, we require AI, and machine learning algorithms can help to overcome many obstacles. In this paper, we first discuss ICN, its deployment, and its unique features that distinguish it from its archrival TCP/IP. We then present the most recent research on ICN-CIoT-AI and provide a comprehensive analysis of this domain in terms of technology, AI/ML domain, IoT, and cloud technology. The study framework, simulation software, and results achieved by the researchers are also listed. Finally, we explore three broad categories of open issues and challenges raised by the researchers: security, performance, and in-network caching. We also exhibit the technologies that were employed in the study.
Lithium Battery Allocation Decision-Making Scheme Based on K-Means Algorithm
Authors: Ma Dandan , Qin Xiangge
Published: June 2022

Lithium-ion batteries, the core components of electric vehicles, have received unprecedented attention and undergone development in the era of huge energy demand. The traditional clustering algorithm cannot meet the requirement of the consistency of lithium battery distribution. In this study, we provide an improved K-means algorithm to meet the battery distribution needs of enterprises and combine it with reality. This model includes an early data processing model and a battery comparison method based on the new K-means algorithm. In the battery data processing model, the preprocessing process approach and actual production standards preclude problematic batteries. In the battery comparison algorithm, the number of batteries in each cluster becomes equal after the battery comparison. The algorithm can ensure the internal characteristics of lithium-ion power batteries, and, at the same time, after the matching is completed, the number of lithium batteries in each cluster is equal.
Prediction of Ischemic Stroke Recurrence Based on COX Proportional Risk Regression Model and Evaluation of the Effectiveness of Patient Intensive Care Interventions
Authors: Wang Yun , Lu Ting
Published: June 2022

With the continuous improvement of medical technology and the aging of the population, the death rate of stroke is gradually decreasing, but the recurrence rate is still high, and the number of recurrences is increasing, resulting in disability and other symptoms, which brings great burden and distress to patients and their families. As the number of strokes increases, neurological impairment becomes more and more severe, affecting patients’ ability to live, socialize, and work, and seriously reducing their quality of life. Clustered care is a combination of evidence-based linked interventions and a multidisciplinary team providing the best possible care through evidence-based research and highly operational practice, and it can improve outcomes for ischemic stroke patients more than implementation alone. This paper presents a Cox proportional risk regression-based model, using it to build the most used semi-parametric model for multifactorial survival analysis, due to its advantages of both parametric and nonparametric models, and to analyze the factors influencing survival time in study subjects with incomplete data. The proposed strategy has been found to be useful in predicting ischemic stroke recurrence and cluster care interventions for patients.
Construction of Algorithm Model in Artificial Intelligence Interactive Art Teaching Evaluation System from the Perspective of Core Literacy
Authors: Bu Xiangwei
Published: June 2022

Art teaching is not only an important part of basic education but also an important subject of core literacy education, and the cultivation of core literacy is also a long and lasting process. In order to change the original teaching mode, reform the teaching concept, and cultivate high-quality all-round talents, this paper integrates artificial intelligence interactive teaching method into the art teaching process from the perspective of core literacy, which can not only increase the classroom interaction, improve students’ enthusiasm, and conduct multidimensional evaluation on students’ art work performance, moral sentiment, appreciation ability, creativity, and imagination. While paying attention to cultivating and improving students’ comprehensive quality and ability, the all-round development of students’ moral, intellectual, physical, artistic, and labor ability is improved.
circACTG1 Promotes Hepatocellular Carcinoma Progression by Regulating miR-940/RIF1 Axis and Activating AKT/mTOR Pathway
Authors: Wu Chunchen , Tian She , Guo Yuting , Yu Chao , Lei Linhan , Zheng Dijie , Xie Huahua , Zheng Jian , Sun Chengyi
Published: June 2022

Background. Hepatocellular carcinoma (HCC) is recognized as the fourth in incidence and the third in mortality worldwide. The onset of HCC is insidious and often asymptomatic at the early stage. HCC is more prone to metastasis, recurrence, and drug resistance than other solid tumors owing to its feature of high heterogeneity. Therefore, what particularly important is to search for effective molecular markers in the occurrence and progression of HCC. Aim. To probe into the therapeutic potential of circACTG1 (hsa_circ_0046144) in HCC cell migration and invasion, providing a new insight and molecular target to diagnose and cure HCC patients. Methods. The circACTG1 expression in collected HCC cells was determined by quantitative polymerase chain reaction (qPCR). Assessment for circACTG1 diagnosing capability was analyzed by receiver operating characteristic (ROC) curves. Transwell assay, wound healing assay, and cell counting kit-8 assay were used for monitoring the effect of circACTG1 in HCC cell invasion, migration, and proliferation, respectively; qPCR, luciferase reporter assay, databases, and Western blot analysis were used for identifying the modulation mechanisms among circACTG1, miRNA-940, and RIF1. What is more, our study verified AKT-mTOR signaling after miR-940 mimic treatment or circACTG1 knockdown. Results. circACTG1 was overexpressed in HCC cells and tissues. Knockdown of circACTG1 restrained 97H and Huh7 cell migration and invasion. Significantly, circACTG1 was discovered to serve as a miR-940 sponge. miR-940 activation rebated the circACTG1 level, and conversely, miR-940 inhibition boosted the circACTG1 level. However, this effect or relationship was not seen after circACTG1 mutation. Furtherly, miR-940-downregulated expression was also found in HCC patients, and importantly, miR-940 inhibition reversed circACTG1 expression in 97H cells with circACTG1 knockdown. Moreover, the expression of RIF1 was significantly reduced after inhibiting circACTG1 or overexpressing miR-940 but rescued when both circACTG1 and miR-940 were inhibited. Finally, circACTG1 and miR-940 played significant roles of regulating AKT-mTOR signaling. Conclusion. circACTG1 expression remarkably ascended in HCC, which is of certain diagnostic value. Moreover, circACTG1 potentially regulates HCC cell proliferation, invasion, and migration via miR-940/RIF1/AKT/mTOR pathway.
Modeling the Interaction Networks about the Climate Change on Twitter: A Characterization of its Network Structure
Authors: Mouronte-López Mary Luz , Subirán Marta
Published: June 2022

This work studies the interaction networks (replying, retweeting, and quoting) that arise on Twitter in relation to such a relevant topic as climate change. We detected that the largest connected component of these networks presents low values of average degree and betweenness, as well as a small diameter compared to the total number of nodes in the network. The largest connected component of retweeting and quoting networks also exhibits very low negative assortativity. The quoting and retweeting networks have a more hierarchical structure than the replying network. We also find that the process of emergence of new links in the interaction networks can be properly modeled (with high accuracy) through a Support Vector Machine model using the embeddings provided by the Node2Vec algorithm. A Random Forest model using certain similarity measures as explanatory variables between nodes also provides high accuracy. In addition, we analyze the communities existing in each interaction network by means of the Louvain method. The cumulative probability distributions of hashtags per community are also examined.
The Effect and Evaluation of the Third Military Medical University Fluid Resuscitation Formula
Authors: Luo Jia , Zhang Peng , Gan Ying-Hong , Li Ning , Yuan Li-Li , Luo Gao-Xing , Xiang Fei
Published: June 2022

Background. The clinical efficacy of the third Military Medical University formula (TMMU formula) for fluid resuscitation stage was evaluated to improve the treatment level of adult patients with extensive burns during the shock stage. Methods. Retrospective analysis of the data of 55 patients undergoing fluid resuscitation according to the TMMU formula within six hours after burn injury. The following indicators were collected: (1) demographic and injury information; (2) fluid resuscitation information; (3) efficiency information, including cardiovascular function, liver function, renal function, coagulation function evaluation indicators, blood concentration, and average urine output index. Results. (1) In the first and second 24 hours after injury, the median fluid rehydration coefficient was 1.68 ml/kg·(%) TBSA and 1.15 ml/kg·(%) TBSA, the median ratio of crystal to colloid was 2.24 and 1.67, and the median urine output index was 0.75 ml/kg·h and 1.05 ml/kg·h, respectively. (2) The actual fluid volume during patient resuscitation is higher than the formula calculated volume, and this difference is more obvious in patients with burn area ≥80%. (3) In the second 24 hours, the value of the actual total fluid volume minus the formula total volume in the group with crystal to colloid ratio ≤2 was significantly lower than that in the ratio >2 group. (4) At 24 and 48 hours after injury, the cardiovascular function, liver function, renal function, and coagulation function were better than those before fluid resuscitation. Conclusions. Early application of the TMMU formula for fluid resuscitation in adult patients with extensive burns is safe and effective, but the actual input volume often exceeds the volume calculated by the formula, especially in the second 24 hours after burn injury and in patients with larger burn areas. Increasing the colloid input volume can help reduce the total amount of fluid used for resuscitation.
The Application of Art Therapy Based on Particle Swarm Optimization Method for Preschool Children’s Mental Health
Authors: Wu Chuyi , Sang-Yeol Lee
Published: June 2022

Art provides a starting point for children to understand works of art. In the initial stage of life, the development of vision and movement is particularly important, and painting helps coordinate the work of these systems. Art dispels the mystery of art and helps children connect with art classics, thus understanding the language and structure of art. In particle swarm optimization, the inertia weight is constant, but with the increase of iteration times, the inertia weight may become larger or smaller, so it tends to search globally or locally. This paper first analyzes what art therapy is from the basic concepts of art and art education and the stage characteristics of children’s painting and then studies the application of art therapy based on particle swarm optimization in preschool children’s art teaching. Finding the global optimal fitness function in the research process not only simplifies the computational complexity but also approximates the maximum coverage effect provided by the current network. The results show that applying particle swarm optimization algorithm to the research of mental health treatment of preschool children can establish an effective evaluation program in the practical research of children’s art treatment.