bluuebunny/crossref_metadata_2025 huggingface dataset:
Supervision Strategy Analysis on Price Discrimination of E-Commerce Company in the Context of Big Data Based on Four-Party Evolutionary Game
Authors: Xiao Meng
Published: March 2022

This paper focuses on the phenomenon of “big data killing” implied in e-commerce and discusses how to take the government as the lead to coordinately supervise the price discrimination behavior of e-commerce companies towards loyal customers. First, the four-party evolutionary game model of the government regulatory department, e-commerce platform, e-commerce company, and consumer is built. Second, the stability of the strategy choice of each game subject is analyzed. On this basis, the evolutionary stable strategy in the system based on First Law of Lyapunov is explored. Finally, the influences of key elements on system evolution are simulated and analyzed by MATLAB2021. Results demonstrate that (1) the government supervision mechanism can effectively supervise the price discrimination of e-commerce company based on big data to loyal customers; (2) when the government chooses the strict supervision strategy, reducing the information supervision cost of the e-commerce platform and the strict supervision cost of the government enable the government and the e-commerce platform to coordinate supervision and make the e-commerce company incline to choose the nondifferential pricing strategy; (3) when the government chooses the loose supervision strategy, reducing the information supervision cost of the e-commerce platform and increasing the probability of consumer discovering differential pricing and the penalties for differential pricing of e-commerce company enable the e-commerce platform and consumer to coordinate supervision, and make the e-commerce company incline to choose the nondifferential pricing strategy. The results of this study can provide theoretical guidance for the government and companies to make beneficial strategic decisions in the development of e-commerce.
The Efficacy of Glucocorticoids in the Treatment of Nonarteritic Anterior Ischemic Optic Neuropathy: A Systematic Review and Meta-Analysis
Authors: Zhou Pingping , Zhang Jian , Qi Yanxiu
Published: March 2022

Background. To evaluate the clinical effects and safety of glucocorticoids for patients with nonarteritic anterior ischemic optic neuropathy (NAION). Methods. The databases MEDLINE, Embase, PubMed, Cochrane Database, and Web of Science were used to search for the relevant studies, and full-text articles that reported on the evaluation of glucocorticoids vs. no-treatment or placebo for patients with NAION. Review Manager 5.4 was used to estimate the pooled effects of the results among selected studies. Forest plots, funnel plots, and Begg’s rank correlation were also performed on the included articles. Results. A total of 983 patients were contained in the 9 studies that satisfied the eligibility criteria. The meta-analysis showed that, compared with the control group, the glucocorticoid group had significantly improved the VA (MD: -0.25, 95% CI [-0.45, -0.05],

P
=
0.02

), VF (MD: -0.50, 95% CI [-0.94, -0.07],

P
=
0.02

), and RNFL (MD: -14.10, 95% CI [-26.41, -1.79],

P
=
0.02

) in NAION patients and had a high improvement rate of VA (RR 1.31, 95% CI [1.12, 1.52],

P
=
0.0005

). No significant publication bias was observed in our study. Discussion. Our research preliminarily confirmed the effectiveness of glucocorticoids for NAION treatment, but more high-quality RCTs focusing on the hormone adverse reactions should be performed to verify our conclusions.
The Effect of Health Education Combined with Personalized Psychological Nursing Intervention on Pregnancy Outcome of Pregnant Women with Gestational Diabetes Mellitus
Authors: He Rubi , Lei Qiong , Hu Haibin , Li Hui , Tian Dongmei , Lai Zhicun
Published: March 2022

Objective. To study the effect of health education combined with personalized psychological nursing intervention on pregnancy outcome of pregnant women with gestational diabetes mellitus (GDM). Methods. 170 patients with GDM admitted to Guangdong Women and Children Hospital from January 2018 to December 2018 were selected as study subjects and randomly divided into two groups. During the period from diagnosis of GDM to termination of pregnancy, both groups were given routine education and routine examination, and the intervention group adopted health education combined with personalized psychological nursing interventions during pregnancy. The pregnancy weight, blood glucose index, compliance, disease awareness, self‐adjustment management ability, satisfaction, and pregnancy outcome were measured before and after the intervention. Results. There were no statistically significant differences in pregnancy weight, fasting plasma glucose, and 2 h postprandial blood glucose between the two groups before intervention (P = 0.768, 0.605, and 0.762). After intervention, lower levels of the above indicators were obtained in the intervention group than in the control group (P < 0.001). The compliance and satisfaction with the intervention in the intervention group were significantly higher than those in the control group (P < 0.001). The intervention group had remarkably higher disease awareness rate and self‐psychological adjustment and management ability than the control group (P < 0.001). Better pregnancy outcomes were observed in the intervention group compared with the control group (P < 0.001). Conclusion. For patients with GDM, health education combined with personalized psychological nursing on the basis of the conventional nursing can effectively control patients’ condition and ensure a better pregnancy outcome, which merits widespread promotion.
Immune Infiltration Analysis with the CIBERSORT Method in Lung Cancer
Authors: Guan Meng , Jiao Yan , Zhou Lili
Published: March 2022

Background. Immune infiltration of lung cancer (LC) is tightly related to clinical results. Nevertheless, past researches have not elucidated the diversities of functionally different cellular types making up the immunoresponse. Methods. In the present research, on the foundation of a deconvolution algorithm (CIBERSORT) and clinically annotated expression profiles, our team studied the tumor-infiltrating immune cells (TIICs) presenting in 502 LC samples and 49 normal samples in a comprehensive way. The fraction of 22 immunocyte subgroups was assessed to identify the relationship among every cellular type and survival and reaction to chemical therapies. Results. Consequently, profiles of immunity infiltration change remarkably between paired tumor and precancerous tissues, and the change can describe the diversity of individuals. Of the cellular subgroups studied, cancers without dendritic resting cells or with a decreased quantity of follicular helper T (Tfh) cells were related to the poor prognosis. Correlation analysis between different stages of LC and 22 immune cell subpopulations revealed that the amount of 14 immune cells in LC was remarkably related to tumor stage. The high expression of resting dendritic cells and follicular helper T cells predicted better prognostic value, and univariate analyses proved that two TIICs were significantly associated with patients’ prognosis. Conclusions. To sum up, the data herein reveal that there may be subtle differences in the cell constituents of the immune infiltrate in LC, and those diversities may be vital determinating factors of prognostic results and reactions to therapies.
Performance Evaluation of Reducing Consumption of Energy in the Yangtze River Delta under the Background of Low‐Carbon Economy
Authors: Sun Chenguang , Miao Bo
Published: March 2022

With global climate change and rapid environmental degradation, reducing energy consumption has attracted widespread attention worldwide. The Yangtze River Delta is a major province in China in terms of energy consumption and pollutant emissions. After adopting a series of energy‐saving and emission‐reduction measures, the Yangtze River Delta has achieved certain results. However, at present, the Yangtze River Delta region has a serious heavy industrial structure and many “two high and one capital” industries, which play a huge role in driving GDP growth. This situation will inevitably aggravate the contradiction between the three systems of energy, environment, and economy. In this context, a scientific and reasonable evaluation of the performance of reducing energy consumption and further promoting the implementation of reducing energy consumption in the Yangtze River Delta. It is conducive to achieving low carbon and energy conservation in the Yangtze River Basin provinces. In order to comprehensively examine and evaluate the consumption reduction performance of the Yangtze River Basin provinces, promote the scientific management of consumption reduction performance, and promote consumption reduction, this paper integrates the concept of low‐carbon economy into the evaluation of consumption reduction performance of the Yangtze River Basin provinces. Combining the connotation of energy consumption reduction and low‐carbon economy, this paper constructs a comprehensive and scientific evaluation index system for energy consumption reduction performance in the Yangtze River Delta region from three aspects: economy, energy, and environment. At the same time, the TOPSIS method and the full alignment polygon graphical index method are used to evaluate the energy consumption reduction performance of the Yangtze River Delta region from 2010 to 2020. Through the analysis, the following conclusions can be drawn: the overall performance level of energy consumption reduction in the Yangtze River Delta region is average; the coordination between economy, energy, and environment is poor. Through the gray correlation analysis of energy consumption reduction performance, it is found that the current energy consumption structure plays a decisive role in reducing energy consumption; therefore, energy consumption coefficient, industrial structure, and government incentives also play a key role in reducing energy consumption.
A Method for Detection of Rare Industrial Fake Film Based on Deep Learning
Authors: Gao Wuqi , Li Chao , Yang Ting , Li Liangliang , Li Xiaoyan
Published: March 2022

In order to solve the industrial fake film problem produced by repeated imaging in the field of nondestructive testing, this paper proposes an image similarity comparison algorithm of X-ray film based on the Siamese neural network. Firstly, in order to accurately locate and detect weld areas from high-resolution X-ray film, a robust location and detection algorithm based on the Gaussian function is proposed. Next, in order to solve the problem that the deep learning model system extracts features according to squares by default, which results in the disappearance of film features with high horizontal and vertical features, effective weld regions are used and processed in blocks. Then, a small sample data set for weld similarity discrimination is established by using each block region, and an industrial fake film discrimination model based on twin neural networks is constructed. Finally, the corresponding weights are set for different fake feature information areas, and a similarity comparison system for industrial fake film detection is proposed. The experiment shows that the method in this paper can not only accurately locate the weld area but also accurately detect the fake film in the industry, avoiding the influence of the geometric transformation of the image and the insufficient number of data sets in the process of similarity comparison, and has a good detection effect.
Decision Scheduling for Cloud Computing Tasks Relying on Solving Large Linear Systems of Equations
Authors: He Jing
Published: March 2022

With the continuous reform and innovation of Internet technology and the continuous development and progress of social economy, Big Data cloud computing technology is more and more widely used in people’s work and life. Many parallel algorithms play a very important role in solving large linear equations in various applications. To this end, this article aims to propose and summarize a cloud computing task scheduling model that relies on the solution of large linear equations. The method of this paper is to study the technology of solving large-scale linear equations and propose an M-QoS-OCCSM scheduling model. The function of the experimental method is to solve the problem of efficiently executing N mutually dependent parallel tasks within limited resources, while fully satisfying users’ expectations of task completion time, bandwidth rate, reliability, and cost. In this paper, the application experiment of large-scale linear equations in task scheduling is used to study task scheduling algorithms. The results show that when the task load is 10 and 20, the convergence speed of the MPQGA algorithm is 32 seconds and 95 seconds faster than that of the BGA algorithm, respectively.
New Results in Vague Incidence Graphs with Application
Authors: Rao Yongsheng , Kosari Saeed , Gheisari Mehdi
Published: March 2022

Vague incidence graph (VIG), belonging to the FG family has good capabilities when facing with problems that cannot be expressed by FGs. When an element membership is not clear, neutrality is a good option that can be well-supported by a VIG. The previous definitions limitations in connectivity concept have led us to offer new definitions in VIGs. Hence, in this paper, the VIG and its matrix form are proposed. Vague incidence subgraph (VISG) is defined with several properties. Incidence pairs, paths, and connectivities between pairs in VIGs are introduced. Likewise, different types of strong and cut pair in VIGs are examined with their properties. Universities are one of the most important centers for human education and play an important role in the development of the country. But the point to be very careful is that the employees of a university must do their job in the best possible way. Therefore, we have tried to identify the most effective person in a university according to its performance by presenting an application.
Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model
Authors: Nagarajan Manikandan , Muthaiah Rajappa , Teekaraman Yuvaraja , Kuppusamy Ramya , Radhakrishnan Arun
Published: March 2022

Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep learning process aid the designer to abridge the circuitry and also to increase the computation speed at the cost of the accuracy of results. High computational complexity and low-power requirement of portable devices in the dark silicon era sought suitable alternate for Complementary Metal Oxide Semiconductor (CMOS) technology. Gate Diffusion Input (GDI) logic is one of the prompting alternatives to CMOS logic to reduce transistors and low-power design. In this work, a novel energy and area efficient 1-bit GDI-based full swing Energy and Area efficient Full Adder (EAFA) with minimum error distance is proposed. The proposed architecture was constructed to mitigate the cascaded effect problem in GDI-based circuits. It is proved by extending the proposed 1-bit GDI-based adder for different 16-bit Energy and Area Efficient High-Speed Error-Tolerant Adders (EAHSETA) segmented as accurate and inaccurate adder circuits. The proposed adder’s design metrics in terms of delay, area, and power dissipation are verified through simulation using the Cadence tool. The proposed logic is deployed to accelerate the convolution process in the Low-Weight Digit Detector neural network for real-time handwritten digit classification application as a case study in the Intel Cyclone IV Field Programmable Gate Array (FPGA). The results confirm that our proposed EAHSETA occupies fewer logic elements and improves operation speed with the speed-up factor of 1.29 than other similar techniques while producing 95% of classification accuracy.
Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models
Authors: Kumar Sarvesh , Wajeed Mohammed Abdul , Kunabeva Rajashekhar , Dwivedi Nripendra , Singhal Prateek , Jamal Sajjad Shaukat , Akwafo Reynah
Published: March 2022

It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient’s criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.
Cost-Effectiveness Analysis of Follow-Up Schedule for Hepatocellular Carcinoma after Radiofrequency Ablation
Authors: Hu Shuifang , Wu Xiaoxue , Wei Mengchao , Ling Yunyan , Zhu Meiyan , Wang Yan , Chen Yong , Jin Meng , Peng Zhenwei
Published: March 2022

Background and Purpose. Follow-up intervals after radiofrequency ablation (RFA) varied in different international guidelines. This study aimed to compare the cost-effectiveness of different follow-up intervals for hepatocellular carcinoma (HCC) following RFA. Methods. A Markov model was established to evaluate the cost-effectiveness of every 2 months or 2-3 months (2- to 3-month group) versus every 3 months or 3-4 months (3- to 4-month group) posttreatment surveillance in the first two years for HCC after RFA. Transition probabilities and utility values were derived from the literature review. Costs of follow-up were estimated from our institution. The incremental cost-effectiveness ratio (ICER), which was less than $10888 per quality-adjusted life-year (QALY), was considered cost-effective. Sensitivity analyses were performed to determine the uncertainty of the model. Results. The 2- to 3-month group gained 1.196 QALYs at a cost of $2212.66, while the effectiveness and cost of the 3- to 4-month group were 1.029 QALYs and $1268.92, respectively. The ICER of the 2- to 3-month group versus the 3- to 4-month group was $5651.14 per QALY gained, which was less than the willingness-to-pay threshold of 1-time gross domestic product per capita of China ($10888/QALY). One-way sensitivity analysis showed that the model was most sensitive to the utility of progression-free survival. The probabilistic sensitivity analysis demonstrated that the 2- to 3-month group had a higher probability of being more cost-effective than the 3- to 4-month group when willingness to pay was over $1088.8. Conclusions. Every 2 months or 2-3 months of follow-up intervals were more cost-effective than 3 months or 3-4 months of follow-up intervals. Thus, the intensive follow-up interval in the first two years was recommended for Child-Pugh class A or B HCC patients within the Milan criteria following RFA.
The Activation of AMPK/NRF2 Pathway in Lung Epithelial Cells Is Involved in the Protective Effects of Kinsenoside on Lipopolysaccharide-Induced Acute Lung Injury
Authors: Yang Yue , Zhong Zhen-tong , Xiao Yong-guang , Chen Hong-bin
Published: March 2022

The disorder of mitochondrial dynamic equilibrium of lung epithelial cell is one of the critical causes of acute lung injury (ALI). Kinsenoside (Kin) serves as an active small-molecule component derived from traditional medicinal herb displaying multiple pharmacological actions in cancers, hyperglycemia, and liver disease. The objective of this study was to investigate the effects of Kin on lipopolysaccharide- (LPS-) induced ALI and further explore possible molecular mechanisms. Kin was administered orally (100 mg/kg/day) for 7 consecutive days before LPS instillation (5 mg/kg). After 12 hours, pathological injury, inflammatory response, and oxidative stress were detected. The results demonstrated that Kin significantly alleviated lung pathological injury and decreased the infiltration of inflammatory cells and the release of inflammatory mediators in bronchoalveolar lavage fluid (BALF), apart from inhibiting the production of reactive oxygen species (ROS) and lipid peroxidation. Meanwhile, Kin also promoted mitochondrial fusion and restrained mitochondrial fission in mice with ALI. In terms of mechanism, Kin pretreatment increased the phosphorylation of AMP-activated protein kinase (AMPK) and the protein level of nuclear factor erythroid 2-related factor 2 (NRF2). In Ampk-α knockout mice challenged with LPS, Kin lost its pulmonary protective effects, accompanied by lower NRF2 level. In vitro experiments further unveiled that either AMPK inhibition by Compound C or NRF2 knockdown by siRNA abolished the protective roles of Kin in LPS-treated A549 lung epithelial cells. And NRF2 activator TAT-14 could reverse the effects of Ampk-α deficiency. In conclusion, Kin possesses the ability to prevent LPS-induced ALI by modulating mitochondrial dynamic equilibrium in lung epithelial cell in an AMPK/NRF2-dependent manner.
Optimization Algorithm of Preschool Education Resource Allocation Based on Data Envelopment Analysis Model
Authors: Wang Lili , Liu Taotang
Published: March 2022

This paper uses the data envelopment analysis method to construct an emerging resource allocation optimization algorithm to deeply analyze and study preschool education resource allocation. This paper combines information related to distributed systems and database application practices, and HBase, a distributed data storage system, is selected as the research object. The relationship between hardware configuration parameters and the throughput and response latency of HBase is modeled using the random forest algorithm, an improved particle swarm algorithm is designed and implemented to optimize the mathematical model of the relationship between resource allocation and capital cost, and the optimization results are verified in a real environment to realize the optimization of HBase resource allocation scheme. In this paper, duplicate solution sets are removed based on the objective function in the resource allocation model. Finally, NSGA-II is compared with SPEA2, MOEA/D, and IBEA in this model for simulation experiments to verify the efficient solution of NSGA-II to the constructed model through subjectivity presentation as well as objective performance analysis and to provide a decision solution with a theoretical support for resource allocation optimization. This study provides countermeasure suggestions for each kindergarten to improve the efficiency of resource management, optimize the scale of operation, and increase technological progress, which is based on the findings of the empirical analysis and field interviews, and thus can effectively help kindergartens to improve the efficiency of resource allocation.
Development of the Music Teaching System Based on Speech Recognition and Artificial Intelligence
Authors: Zhichao Zhang
Published: March 2022

Intelligent music teaching is the direction of subsequent reforms in music teaching methods. In order to improve the intelligence of music teaching, this paper conducts research on speech recognition technology. The speech conversion system based on Multiscale Star GAN extracts different levels of multiscale features of the global features of music utterance through the multiscale structure, which enhances the details of the converted speech and uses residual connections to alleviate the problem of gradient disappearance and enable the network to spread more deeply. In addition, after improving the speech recognition algorithm, this paper combines the needs of music teaching to construct a music teaching system based on speech recognition and artificial intelligence and designs system functional modules. Finally, this paper evaluates the performance of the system constructed in this paper by means of teaching experiments. From the experimental analysis, it can be seen that the system constructed in this paper has a good teaching effect.
A Cyber Deception Defense Method Based on Signal Game to Deal with Network Intrusion
Authors: Gao Chungang , Wang Yongjie , Xiong Xinli
Published: March 2022

Increasingly, cyber security personnel are using cyber deception defense techniques to deal with network intrusions. However, traditional cyber deception techniques (such as honeypots and honeynets) are easily detected by attackers, thus leading to failure. Therefore, we propose a cyber deception defense method based on the signal game to improve the effectiveness of the defense. More specifically, first, we propose a moving target defense (MTD) enhanced cyber deception defense mechanism. Then, on the basis of the in-depth analysis of network attack and defense scenarios, a signal game model is constructed to describe the network attack and defense process, and a multistage attack and defense game equilibrium solution method is designed to guide the selection of the optimal deception defense strategy. Meanwhile, considering the actual network attack and defense, we quantify the game revenue based on a probabilistic model. The experimental results show that the defense method proposed in this paper could guide the defender to implement the optimal defense strategy and achieve a better defense effect.
Scenic Tourist Route Planning Algorithm Based on Mobile Computing and Grey Entropy Decision Model
Authors: Xiao Hongyi
Published: March 2022

Since the concept of “Smart Earth” was put forward, countries have attached great importance to the research and introduction of smart tourism. With the increasing updating of information technology, the tourism industry is also constantly trying to transform intelligently, and the personalized recommendation of its travel routes has become the most important development in smart tourism, one of the hot topics. Tourism agencies create traditional travel itineraries based on the preferences of the majority of people and the characteristics of tourist attractions. These lines are primarily used for team travel. People’s expectations for travel route planning are increasing as their living standards rise, and the traditional route planning algorithm based on a static road network data model has struggled to describe the complex and changing scenic environment. The improved random walk algorithm suggests suitable travel routes to users, allowing for more accurate route recommendations and effectively addressing the problem of new route recommendation difficulty. This paper proposes a scenic tourist route planning algorithm using the grey entropy decision-making model and mobile computing. The content of the recommended results is more closely related to the target recommendation user’s tendency after personalized adjustment, which has a better effect on the personalization of the recommended results.
Design and Implementation of Electrical Parameter Monitoring System Based on ZigBee Wireless Communication
Authors: Meng Xiangbin
Published: March 2022

In order to solve the problems of many lines, troublesome movement and redundant management of traditional electrical parameter monitoring system, a design and implementation of electrical parameter monitoring system based on ZigBee wireless communication is proposed. Taking the monitor as an example, this paper briefly introduces the application status of the monitor monitoring system and electrical safety detection. According to the comparison of wireless communication technology and the analysis of actual demand, this paper expounds the advantages of ZigBee wireless technology. Based on the combination of received signal strength indication (RSSI) ranging algorithm and trilateral positioning algorithm, the wireless positioning of mobile targets is realized. Then, the sparse correlation matrix of signal attenuation coefficient between base stations is established in the upper computer to optimize the positioning by using the look-up table method. The relevant test results show that through the serial port test software 2000 times of data transmission and reception, 1866 times of communication are successful, and the success rate is 93.3%, which basically meets the requirements of data transmission stability; After data transmission time detection, it takes about 1.4 seconds to send data from the monitor to the client for reception. The client sends the module query command, and the time when the command reaches the monitor is 1.6 seconds. The data transmission speed can basically meet the requirements of real-time monitoring. The design and implementation of the monitoring system can not only help to reduce the workload of medical staff in hospital nurse stations and improve their work efficiency, but also provide the possibility of practical significance for developing into an application tool for patient family members or personal health management in the future.
A Novel Radiomics-Based Machine Learning Framework for Prediction of Acute Kidney Injury-Related Delirium in Patients Who Underwent Cardiovascular Surgery
Authors: Xue Xin , Chen Wen , Chen Xin
Published: March 2022

Acute kidney injury (AKI) can be caused by multiple etiologies and is characterized by a sudden and severe decrease in kidney function. Understanding the independent risk factors associated with the development of AKI and its early detection can refine the risk management and clinical decision-making of high-risk patients after cardiovascular surgery. A retrospective analysis was performed in a single teaching hospital between December 1, 2019, and December 31, 2020. The diagnostic performance of novel biomarkers was assessed using random forest, support vector machine, and multivariate logistic regression. The nomogram from multivariate analysis of risk factors associated with AKI indicated that only LVEF, red blood cell input, and ICUmvat contribute to AKI differentiation and that the difference is statistically significant (

P
<
0.05

). Seven radiomics biomarkers were found among 65 patients to be highly correlated with AKI-associated delirium. The importance of the variables was determined using the multilayer perceptron model; fivefold cross-validation was applied to determine the most important delirium risk factors in radiomics of the hippocampus. Finally, we established a radiomics-based machine learning framework to predict AKI-induced delirium in patients who underwent cardiovascular surgery.
Effects of Sodium-Glucose Cotransporter-2 Inhibitors on Weight in Type 2 Diabetes Mellitus and Therapeutic Regimen Recommendation
Authors: Wang Dong-Dong , Mao Yi-Zhen , Yang Yang , Wang Tian-Yun , Zhu Ping , He Su-Mei , Chen Xiao
Published: March 2022

Aims. The present study is aimed at exploring the effects of sodium-glucose cotransporter-2 (SGLT-2) inhibitors on weight in type 2 diabetes mellitus (T2DM) and therapeutic regimen recommendations. Methods. 20,019 patients with T2DM were enrolled. The maximal effect (

E

max

) models, whose evaluation index was change rate of body weight from baseline value, were used to analyze data using nonlinear mixed effect modeling (NONMEM). Results. For SGLT-2 inhibitors, canagliflozin, empagliflozin, ertugliflozin, ipragliflozin, luseogliflozin and tofogliflozin, the

E

max

, and treatment duration to reach half of the maximal effects (ET50) were -3.72% and 3.35 weeks, -5.59% and 16.8 weeks, -2.84% and 3.42 weeks, -3.43% and 3.09 weeks, -3.04% and 4.38 weeks, and -2.45% and 3.16 weeks, respectively. In addition, for T2DM patients, 100 mg/day canagliflozin needs to be taken 13.4 weeks for the plateau of effect on weight; 10 mg/day empagliflozin needs to be taken 67.2 weeks for the plateau of effect on weight; 5 mg/day ertugliflozin needs to be taken 13.68 weeks for the plateau of effect on weight; 50 mg/day ipragliflozin needs to be taken 12.36 weeks for the plateau of effect on weight; 2.5 mg/day luseogliflozin needs to be taken 17.52 weeks for the plateau of effect on weight; 20 mg/day tofogliflozin needs to be taken 12.64 weeks for the plateau of effect on weight. Conclusions. This was the first study to explore effects of SGLT-2 inhibitors on weight in T2DM; meanwhile, the optimum dosages and treatment durations on weight from canagliflozin, empagliflozin, ertugliflozin, ipragliflozin, luseogliflozin, and tofogliflozin were recommended, respectively.
The Application of Adaptive Analytic Hierarchy Process Driven by Multisource Big Data in the Training of School-Enterprise Joint Engineering Ability
Authors: Zhang Liqing
Published: March 2022

The Chinese government has pointed out in relevant literature that by 2020, China is in short supply of high-level innovative technology talents, and China’s engineering education will face unprecedented challenges. Based on the needs of China’s future economic development, the Ministry of Education urgently launched the “Excellent Engineer Education and Training Program” in 2010, which aims to cultivate a large number of solid theoretical foundations, strong practical skills, and high innovation capabilities for the industry, the world, and the future. An outstanding engineer is the one who can quickly adapt to the needs of economic and social development. The program is divided into three levels of undergraduate, master, and doctoral training. The undergraduate level focuses on training in a series of high-quality applications capable of not only being competent in the management and marketing of the production site but also in the design, development, and operation of engineering projects. Talent: to achieve this training goal, the in-depth cooperation between schools and enterprises has become the key. In order to achieve the undergraduate-level training goals of the “Excellence Program,” various local colleges and universities have adopted the method of school-enterprise cooperation, combined with their own school positioning, trying to explore a training path suitable for their own development. This research takes China’s local application-oriented universities as the research object and uses the literature method to sort out and summarize the status quo of the school-enterprise cooperation in training outstanding engineers in China’s local application-oriented universities. Select two different application-oriented universities located in areas with large economic development gaps, analyze their common problems, and try to put forward countermeasures and suggestions.
Incidence, Characteristics, and Management of Patients with Recurrent Myocardial Infarctions: Insights from the EYESHOT POST-MI
Authors: De Luca Leonardo , Colivicchi Furio , Gabrielli Domenico , Lucci Donata , Grippo Gabriele , Piemonte Francesco , Villari Bruno , Di Lenarda Andrea , Oliva Fabrizio , Gulizia Michele Massimo
Published: March 2022

Background. It is unknown whether patients who survived two or multiple episodes of myocardial infarction (MI) present different clinical characteristics and management than patients at their first MI. Methods. The EYESHOT post-MI was a prospective, observational, nationwide study aimed to evaluate the management of patients presenting to cardiologists 1 to 3 years from the last MI event. In 3 months of enrolment, 165 Italian cardiology centers included 1633 consecutive post-MI patients. In the present analysis, we stratified the study cohort according to the number of prior MI episodes (i.e., 1, 2 or ≥3). Results. Among the 1618 patients enrolled with complete data on MI history, 1335 (82.5%) were at their first MI episode, 209 (12.9%) had a history of 2 MIs, and the remaining 74 (4.6%) had ≥ 3 prior MIs. Patients with a history of multiple MIs were increasingly older and presented a significantly higher rate of risk factors compared to those at their first MI. During the year prior to enrolment, patients with 2 or ≥3 MI episodes more frequently underwent coronary angiography compared to the other group (

p
<
0.0001

). In addition, several lifesaving and antianginal drugs were more frequently prescribed in patients presenting with a history of multiple MIs compared to those at their first MI. Conclusions. Our data suggest that patients with multiple MIs managed by cardiologists in routine clinical practice present an incremental clinical risk, more frequently undergo coronary angiography, and are more intensively managed with pharmacological therapies compared to patients at their first MI episode.
Computational Intelligence Approaches in Developing Cyberattack Detection System
Authors: Alzahrani Mohammed Saeed , Alsaade Fawaz Waselallah
Published: March 2022

The Internet plays a fundamental part in relentless correspondence, so its applicability can decrease the impact of intrusions. Intrusions are defined as movements that unfavorably influence the focus of a computer. Intrusions may sacrifice the reputability, integrity, privacy, and accessibility of the assets attacked. A computer security system will be traded off when an intrusion happens. The novelty of the proposed intelligent cybersecurity system is its ability to protect Internet of Things (IoT) devices and any networks from incoming attacks. In this research, various machine learning and deep learning algorithms, namely, the quantum support vector machine (QSVM), k-nearest neighbor (KNN), linear discriminant and quadratic discriminant long short-term memory (LSTM), and autoencoder algorithms, were applied to detect attacks from signature databases. The correlation method was used to select important network features by finding the features with a high-percentage relationship between the dataset features and classes. As a result, nine features were selected. A one-hot encoding method was applied to convert the categorical features into numerical features. The validation of the system was verified by employing the benchmark KDD Cup database. Statistical analysis methods were applied to evaluate the results of the proposed study. Binary and multiple classifications were conducted to classify the normal and attack packets. Experimental results demonstrated that KNN and LSTM algorithms achieved better classification performance for developing intrusion detection systems; the accuracy of KNN and LSTM algorithms for binary classification was 98.55% and 97.28%, whereas the KNN and LSTM attained a high accuracy for multiple classification (98.28% and 970.7%). Finally, the KNN and LSTM algorithms are fitting-based intrusion detection systems.
An Application of Pascal Distribution Series on Certain Analytic Functions Associated with Stirling Numbers and Sălăgean Operator
Authors: Frasin Basem Aref
Published: March 2022

In the present paper, we will observe that the Sălăgean differential operator can be written in terms of Stirling numbers. Furthermore, we find a necessary and sufficient condition and inclusion relation for Pascal distribution series to be in the class

ℙ

k

λ
,
α

of analytic functions with negative coefficients defined by the Sălăgean differential operator. Also, we consider an integral operator related to Pascal distribution series. Several corollaries and consequences of the main results are also considered.
Research on System Economic Operation and Management Based on Deep Learning
Authors: Wangtao  , Zheng Zhenzhu , Wang Peiyuan , Liu Xiaobin
Published: March 2022

It is of great significance to accurately predict the operation of the system economy, analyze the gains and losses of macrocontrol policies, evaluate the operation quality of the economic system, and correctly formulate the future development plan and strategy. This paper introduces the deep belief network, which has attracted much attention in the field of deep learning in recent years, into the research of system economic operation and management. This method solves the problems of slow training and learning speed, easy to fall into local minima and insufficient generalization of BP artificial neural network in the research of system economic operation and management. Taking the consumer price index and total import and export volume of F Province as the research object, the experiment proves that DBN has better application in system economic operation and management than BP neural network and vector autoregressive analysis. This paper analyzes and compares the modeling performance of DBN, BP neural network, and VaR method from many aspects, such as prediction accuracy, training convergence speed, and pretraining with or without samples. Relevant empirical results show that DBN has better economic prediction performance than BP neural network and ver. On the other hand, DBN can effectively use nonstandard samples to pretrain network weight parameters. Therefore, DBN is a better operation and management modeling means of economic system, with excellent practicability and application, and is expected to be popularized and applied in the field of economic forecasting.
DBNTFPO: ANN-Based Approach for Logistics Distribution Optimization
Authors: Li Nanxi , Mao Zhiyong
Published: March 2022

This research aims to improve the optimization ability of traditional algorithms under complex urban road conditions. Henceforth, in this paper, the logistics distribution path optimization problem model is established. Each part of the model is introduced in detail, the weight update of the ant algorithm is introduced, the problem of unreasonable road parameters set by the ant algorithm is solved, and the Deep Belief Network Traffic Forecast Path Optimization (DBNTFPO) algorithm is proposed. The related applications of deep learning technology are analyzed, and the relationship between deep learning technology and real-time distribution vehicle routing problem is discussed. Finally, the challenges brought by the real-time logistics distribution path optimization problem to deep learning are introduced. Finally, the effectiveness and feasibility of the algorithm in the actual logistics distribution are demonstrated through an example analysis.
The Growth Model of Industrial Internet Platform in Industrial 4.0
Authors: Wang Lei , Ye Zi , Zhang Rui , Lin Jing , Chen Fei , Tang Fangqing
Published: March 2022

The development of industrial Internet platform is an important carrier to realize the innovation of Industry 4.0. This paper analyzes the development mechanism of Chinese industrial Internet platforms from three aspects of motivation, incentives, and constraints, summarizes the influencing factors of platform development, and establishes a growth model of industrial Internet platform, to select representative factual data as observation variables to verify the effect of each factor. In the end, this paper puts forward relevant suggestions for the government, platform companies, and other related parties to drive the realization of Industry 4.0.
Curriculum Design of College Students’ English Critical Ability in the Internet Age
Authors: Shen Jie , Chen Yukun , Lin Jiaxin
Published: March 2022

“Speculation” is considered to be one of the important qualities that modern students should possess, and it is also the goal of modern education. With the advent of the Internet age, teaching models have been continuously combined with technology. From mixed teaching to flipped classroom, teaching with different methods of information transmission can achieve the best results. This paper is aimed at studying the curriculum design of college students’ English critical thinking ability in the Internet era. On the basis of analyzing the significance of cultivating college students’ critical thinking ability and the curriculum design of critical thinking ability, the flipped classroom mode is used for teaching practice through the experimental method and questionnaire survey method. The personality tendency and the cultivation of students’ critical thinking ability in the flipped classroom were analyzed statistically. Finally, it was concluded that the students’ critical thinking skills have been improved to a certain extent through the various links set in the flipped classroom teaching.
Artificial Intelligence Algorithm‐Based Magnetic Resonance Imaging to Evaluate the Effect of Radiation Synovectomy for Hemophilic Arthropathy
Authors: Zhang Heng , Duan Shukai , Xiao Wei , Yang Xinyue , Li Shenglin
Published: March 2022

This study aimed to discuss magnetic resonance imaging (MRI) based on artificial intelligence (AI) algorithm to evaluate the effect of radiation synovectomy for hemophilic arthropathy (HA). MRI based on the Canny algorithm was applied and compared with conventional MRI to evaluate its application effects according to the PSNR and SSIM. Sixty patients diagnosed with HA were selected as the research subjects. According to the detection method, the patients were divided into group A (pathological detection after radiation synovectomy), group B (conventional MRI detection), and group C (MRI detection based on the Canny algorithm). The application value of MRI based on the Canny algorithm was judged by comparing the differences between the two detection methods and pathological results. The results displayed that the reconstruction effect of the Canny algorithm was remarkably better than that of the traditional algorithm regarding the peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM), which showed a clearer synovial contour. The results of the IPSG score of joint effusion and hemorrhage showed that there was a difference in the detection rate of joints between conventional MRI and pathological results on the score of 1 and 2 (P < 0.05); and there was no significant difference between the MRI and pathological results based on the Canny algorithm (P > 0.05). The results of the IPSG score of synovial hyperplasia showed that the detection rate of conventional MRI was different from pathological results on the score of 1 and 2 (P < 0.05); and there was no significant difference between the MRI and pathological results based on the Canny algorithm (P > 0.05). The results of the IPSG score of hemosiderin deposition showed that the detection rate of conventional MRI was different from the pathological results on the score of 1 and 2 (P < 0.05); and there was no significant difference between the MRI and pathological results based on the Canny algorithm (P > 0.05). The synovial volume of patients after surgery was reduced compared with that before surgery. One‐factor variance was used to analyze the clinical hemorrhage frequency before and after surgery, and the results showed that the differences were statistically significant (P < 0.05). Therefore, MRI on account of AI algorithm made it easier to detect synovial contour, which was helpful to evaluate the efficacy of polygenic risk scores (PRS) surgery in HA patients. MRI based on the Canny algorithm had less differences between the score of hemophilic arthropathy and pathological results, which could replace conventional MRI examination and have clinical application value.