bluuebunny/crossref_metadata_2025 huggingface dataset:
Facial Expression Recognition and Beauty Health Management Based on Image Feature Analysis
Authors: Fu Shanshan , Xu Binbin
Published: July 2022

Face recognition can convey a kind of intuitive and rich information to people. With the current Internet security problems becoming more and more prominent today, in order to better effectively guarantee the security of such digital images during Internet transmission, one of the most direct and effective solutions is to recognize the characteristics of such digital information on facial expressions. However, the customer service system of the beauty and health club itself is a customer service software system based on customer experience and marketing management as its core functions. This article discusses the main software design and system realization of the management platform of the beauty and health club customer information service system in the entire operation process from the application development trend of information software. Based on the theoretical research and application development of the current corporate customer business service management system of the beauty and health club, the basic concepts and operating methods of current corporate customers, business service information management, and business service customer management, as well as the corresponding corporate information customer management. The management technology and other issues are first briefly summarized in theory, and then based on the specific business development characteristics of the current beauty and health clubs, the main application functions and specific market requirements of the service company’s corporate customer service management system are analyzed in detail and introduced the related functions of using the software.
E-Sports Competition Analysis Based on Intelligent Analysis System
Authors: Lu Yao , Chen Hao , Yan Hongqiao
Published: July 2022

To improve the analysis effect of e-sports competitions, this paper studies the intelligent analysis methods of e-sports, compares the advantages and disadvantages of various multi-classification methods, and innovatively designs a two-layer SVM classifier structure for four types of motor imagery EEG signals. Moreover, this paper uses the competition data set to test the classification accuracy of the designed double-layer SVM classifier structure and compares it with the DAG-SVM multi-classification method. The research results show that the e-sports competition analysis system based on the intelligent analysis system proposed in this paper has a good e-sports competition analysis effect, and has a good effect in e-sports competition prediction.
Ultra-Compact Slitted Flower-Shaped Dual-Band Monopole Antenna for Modern Portable Devices
Authors: Abolade Jeremiah O , Konditi Dominic B. O.
Published: July 2022

A meanderingly slitted bio-inspired (MSB)-shaped antenna is presented in this work. The footprint of the proposed MSB antenna is 12 × 10 mm2

0.12
×
0.10

λ
g
2

  at  
2.1
G
H
z

. The MSB antenna proposed in this operates at 2.1 GHz and 5.2 GHz frequencies with a bandwidth of 70 MHz and 570 MHz and a radiation efficiency of 52.5% and 96%, respectively, which is suitable for UMTS, radio navigation, Long Term Evolution (LTE), and 5 GHz Wireless Local Area Network (WLAN) applications. The proposed MSB antenna demonstrates a peak gain of 2.9 dBi at 5.2 GHz and an omnidirectional radiation pattern at both E-plane and H-plane in both operating bands. The fabrication and measurement of the proposed antenna prototype are presented. The parametric study of the proposed structure is performed and presented. Therefore, the proposed antenna is a promising candidate for modern portable devices.
[Retracted] Evaluation Value of Serum miR‐4299 and miR‐16‐5p in Risk Stratification of Sepsis‐Induced Acute Kidney Injury
Authors: Pan Weiwei , Zhang Junfeng , Hu Luqi , Huang Zhiping
Published: July 2022

Objective. This study was designed to determine the evaluation value of serum miR‐4299 and miR‐16‐5p in risk stratification of sepsis‐induced acute kidney injury (SI‐AKI). Methods. A total of 115 sepsis patients were enrolled and assigned to the SI‐AKI group (n = 64) or the sepsis‐non‐AKI group (n = 51) based on the occurrence of AKI, and 72 healthy individuals were enrolled. Fasting venous blood was sampled from every patient before admission, before therapy, and after therapy, followed by quantification of miR‐4299 and miR‐16‐5p by fluorescence quantitative PCR. Receiver operating characteristic (ROC) curves were drawn to evaluate the value of serum miR‐16‐5p and miR‐4299 expression in predicting SI‐AKI, and Pearson’s correlation analysis was performed to explore the associations of the two with Scr, Cys‐C, and KIM‐1. Results. Cases with sepsis, especially SI‐AKI, presented significantly downregulated serum miR‐4299 and miR‐16‐5p. After therapy, the expression in them increased. The area under curve (AUC) of serum miR‐4299 and miR‐16‐5p in the prediction value for early diagnosis of SI‐AKI was 0.895 (95% CI: 0.839‐0.951, cutoff value: 0.780) and 0.838 (95% CI: 0.767‐0.909, cutoff value: 0.775), respectively, and the AUC of them in the prediction value for clinical efficacy on the disease were 0.733 (95% CI: 0.645‐0.820, cutoff value: 1.115) and 0.776 (95% CI: 0.698‐0.855, cutoff value: 1.125), respectively. Serum miR‐16‐5p and mIR‐4299 were negatively correlated with Scr, Cys‐C, and KIM‐1, separately. Conclusion. Both miR‐16‐5p and mIR‐4299 are promising factors for early diagnosis of SI‐AKI and dynamic evaluation of the efficacy on it.
Antimicrobial Alkaloids from Marine-Derived Fungi as Drug Leads versus COVID-19 Infection: A Computational Approach to Explore their Anti-COVID-19 Activity and ADMET Properties
Authors: Sweilam Sherouk Hussein , Alqarni Mohammed H. , Youssef Fadia S.
Published: July 2022

Therapeutic strategies based upon enzyme inhibition have recently gained higher attention in treating hazardous ailments. Herein, the potential use of seventy-two antimicrobial alkaloids isolated from marine-derived fungi to fight COVID-19 infection via inhibition of SARS-CoV-2 lethal virus was performed using in silico analyses. Molecular modelling was performed to assess their enzyme inhibitory potential on the main protease SARS-CoV-2 MPro, 3-chymotrypsin-like protease SARS-CoV-2 3CLpro, and papain-like protease SARS-CoV-2 PLpro using Discovery Studio 4.5. Validation of the docking experiments was done by determination of RMSD (root mean square deviation) after redocking the superimposition of the cocrystalized ligands. Results showed that gymnastatin Z (72) showed the best fitting score in SARS-CoV-2 MPro and SARS-CoV-2 3CLpr active sites with ∆G equal −34.15 and −34.28 Kcal/mol, respectively. Meanwhile, scalusamide C (62) displayed the highest fitting within SARS-CoV-2 PLpro active sites (∆G = −26.91 Kcal/mol) followed by eutypellazine M (57). ADMET/TOPKAT prediction displayed that eutypellazine M and scalusamide C showed better pharmacokinetic and pharmacodynamic properties. Gymnastatin Z is safer showing better toxicity criteria and higher rat oral LD50 and rat chronic LOAEL (lowest observed adverse effect level). Chemometric analysis using principle component analysis (PCA) based on the binding energies observed for the compounds with respect to the three tested enzymes revealed the clustering of the compounds into different clusters. Eutypellazine M, scalusamide C, and gymnastatin Z appear in one cluster due to their closeness in activity. Thus, these compounds could serve as promising SARS-CoV-2 enzymes inhibitors that could help in alleviation of COVID-19 infection. Further investigations are recommended to confirm the results of molecular modelling.
A New Spherical Fuzzy LBWA-MULTIMOOSRAL Framework: Application in Evaluation of Leanness of MSMEs in India
Authors: Biswas Sanjib , Pamučar Dragan , Božanić Darko , Halder Bikash
Published: July 2022

The present paper aims to propose a new hybrid multi-criteria decision-making (MCDM) framework with spherical fuzzy numbers (SFNs). We extend two recently developed algorithms such as level-based weight assessment (LBWA) and MULTIMOOSRAL in spherical fuzzy (SF) domain. We present a case study on six MSME units belonging to engineering cluster for examining their leanness. MSMEs form the backbone of the socioeconomic growth and therefore garner attention of the policy makers. Lean manufacturing (LM) has been a key enabler for the last three decades which help the organizations to achieve business growth. We consider the criteria like leadership, supplier focus, customer focus, process management, waste, culture, human resource focus, technology use and communication, and awareness to compare leanness of the MSMEs using expert opinions. We find that committed leadership, waste reduction, and customer value are given more weightage by the experts for achieving leanness in SMEs. Furthermore, the results show that medium and small units with focused product line score high in terms of leanness. We validate the results obtained by our proposed method by comparing with the same derived by using another widely used approach such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We carry out sensitivity analysis for examining the stability in the solution with the changes in the given condition such as variations in the criteria weights. Our results using SF-LBWA-MULTIMOOSRAL show reasonable accuracy and stability.
An 18-Year-Old Female Experiences Unilateral Vocal Cord Paralysis during Mild COVID-19 Infection
Authors: Coggins John M. , Wright Charisse , Underbrink Michael P.
Published: July 2022

The COVID-19 pandemic has shown with certainty that SARS-CoV-2 can cause a variety of clinical findings, with some of the most notable being lasting chemosensory changes. Severe infections with SARS-CoV-2 can also lead to a variety of complications. For example, vocal cord paralysis can be caused by trauma sustained during intubation, which is a necessary procedure for many severe cases. Rarely, SARS-CoV-2 related vocal cord paralysis has occurred outside the context of intubation. These cases contribute to an emerging assortment of evidence supporting the neuropathic capacity of SARS-CoV-2. This report documents a case of COVID-19 related vocal cord paralysis in an 18-year-old female. The patient had a significant history of muscle tension dysphonia, chronic laryngitis, and vocal cord nodules. The patient developed vocal cord paralysis concurrently with the onset of mild viral symptoms and was never intubated or hospitalized. Based on the onset of symptoms and other causes being excluded with CT, a diagnosis of COVID-19-related vocal cord paralysis was performed.
Experimental Study on Nanomechanical Properties of Yunnan Red-Bed Mudstone
Authors: Ye Xian , Liu Yongcai , Xue Haibin , Li Guo , Xing Jin
Published: July 2022

As a natural building material and building environment, red-bed mudstone has special engineering properties due to its special formation conditions. The engineering disaster caused by these special properties is becoming increasingly prominent. Therefore, it is of great theoretical significance and practical engineering value to study the macro- and micromechanical properties of red-bed mudstone. Nanoindentation mechanical tests are carried out on the red-bed mudstone from Chuxiong, Jingdong, and Nanhua in Yunnan. The microstructure, residual indentation morphology, and nanomechanical parameters of red-bed mudstone in different areas are compared and analyzed. The results show that the load-displacement curves can be divided into four stages, which are the compaction stage, linear elastic stage, holding stage, and unloading stage. Under the same loading and holding time conditions, sample 3 (Nanhua) has the deepest residual indentation, and SEM tests show that the damage to indentation is obvious. Sample 2 (Jingdong) has the shallowest residual indentation. The indentation morphology is complete and the size is small by SEM tests. The residual indentation depth of sample 1 (Chuxiong) is between sample 2 and sample 3. The average elastic modulus of sample 1 (Chuxiong) is

38.0
±
2.29

 GPa. The average hardness is

1.36
±
0.42

 GPa, and the fracture toughness is

0.966
±
0.048

 MPa·m0.5. The average elastic modulus of sample 2 (Jingdong) is

40.1
±
2.03

 GPa. The average hardness is

1.71
±
0.35

 GPa, and the fracture toughness is

1.142
±
0.053

 MPa·m0.5. However, the average elastic modulus of sample 3 (Nanhua) is

25.1
±
2.53

 GPa. The average hardness is

0.61
±
0.21

 GPa, and the fracture toughness is

0.507
±
0.043

 MPa·m0.5. Overall, the elastic modulus and the hardness of sample 2 (Jingdong) are the greatest because of its high quartz crystal content. It shows that the nanomechanical properties of red-bed mudstone in Jingdong are better than those in Chuxiong and Nanhua. The results can provide a basis for revealing the regional differences in macroscopic mechanical properties of Yunnan red-bed mudstone.
Visible and Infrared Image Fusion-Based Image Quality Enhancement with Applications to Space Debris On-Orbit Surveillance
Authors: Tao Jiang , Cao Yunfeng , Ding Meng , Zhang Zhouyu
Published: July 2022

The increasing amount of space debris in recent years has greatly threatened space operation. In order to ensure the safety level of spacecraft, space debris perception via on-orbit visual sensors has become a promising solution. However, the perception capability of visual sensors largely depends on illumination, which tends to be insufficient in dark environments. Since the images captured by visible and infrared sensors are highly complementary in dark environments, a convolutional sparse representation-based visible and infrared image fusion algorithm is proposed in this paper to expand the applicability of visual sensors. In particular, the local contrast measure is applied to obtain the refined weight map for fusing the base layers, which is more robust in a dark space environment. The algorithm can settle two significant problems in space debris surveillance, namely, improving the signal-noise ratio in a noise space environment and preserving more detailed information in a dark space environment. A space debris dataset containing registered visible and infrared images has been purposely created and used for algorithm evaluation. Experimental results demonstrate that the proposed method in this paper is effective for enhancing image qualities and can achieve favorable effects compared to other state-of-the-art algorithms.
GA-IRACE: Genetic Algorithm-Based Improved Resource Aware Cost-Efficient Scheduler for Cloud Fog Computing Environment
Authors: Arshed Jawad Usman , Ahmed Masroor , Muhammad Tufail , Afzal Mehtab , Arif Muhammad , Bazezew Banchigize
Published: July 2022

The ever-growing number of Internet of Things (IoT) devices increases the amount of data produced on daily basis. To handle such a massive amount of data, cloud computing provides storage, processing, and analytical services. Besides this, real-time applications, i.e., online gaming, smart traffic management, and smart healthcare, cannot tolerate the high latency and bandwidth consumption. The fog computing paradigm brings the cloud services closer to the network edge to provide quality of service (QoS) to such applications. However, efficient task scheduling becomes critical for improving the performance due to the heterogeneous nature, resource-constrained, and distributed environment of fog resources. With an efficient task scheduling algorithm, the response time to application requests can be reduced along with bandwidth and cloud resource costs. This paper presents a genetic algorithm-based solution to find an efficient scheduling approach for mapping application modules in a cloud fog computing environment. Our proposed solution is based on the execution time as a fitness function to determine an efficient module scheduling on the available fog devices. The proposed approach has been evaluated and compared against baseline algorithms in terms of execution time, monetary cost, and bandwidth. Comprehensive simulation results show that the proposed approach offers a better scheduling strategy than the existing scheduler.
Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data
Authors: Yan Xiaoqian , Li Ximin , Lu Ying , Ma Dongfang , Mou Shenghong , Cheng Zhiyuan , Ding Yuan , Yan Bin , Zhang Xianzhen , Hu Gang
Published: July 2022

Objective. To establish a prediction model for the risk evaluation of chronic kidney disease (CKD) to guide the management and prevention of CKD. Methods. A total of 1263 patients with CKD and 1948 patients without CKD admitted to the Tongde Hospital of the Zhejiang Province from January 1, 2008, to December 31, 2018, were retrospectively analyzed. Spearman’s correlation was used to analyze the relationship between CKD and laboratory parameters. XGBoost, random forest, Naive Bayes, support vector machine, and multivariate logistic regression algorithms were employed to establish prediction models for the risk evaluation of CKD. The accuracy, precision, recall, F1 score, and area under the receiver operating curve (AUC) of each model were compared. The new bidirectional encoder representations from transformers with light gradient boosting machine (MD-BERT-LGBM) model was used to process the unstructured data and transform it into researchable unstructured vectors, and the AUC was compared before and after processing. Results. Differences in laboratory parameters between CKD and non-CKD patients were observed. The neutrophil ratio and white blood cell count were significantly associated with the occurrence of CKD. The XGBoost model demonstrated the best prediction effect (accuracy = 0.9088, precision = 0.9175, recall = 0.8244, F1 score = 0.8868, AUC = 0.8244), followed by the random forest model (accuracy = 0.9020, precision = 0.9318, recall = 0.7905, F1 score = 0.581, AUC = 0.9519). Comparatively, the predictions of the Naive Bayes and support vector machine models were inferior to those of the logistic regression model. The AUC of all models was improved to some extent after processing using the new MD-BERT-LGBM model. Conclusion. The new MD-BERT-LGBM model with the inclusion of unstructured data has contributed to the higher accuracy, sensitivity, and specificity of the prediction models. Clinical features such as age, gender, urinary white blood cells, urinary red blood cells, thrombin time, serum creatinine, and total cholesterol were associated with CKD incidence.
Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function
Authors: Wen Hui
Published: July 2022

To reduce the complexity and multiple constraints of logistics distribution routing problems, the author proposes an improved genetic algorithm, adaptive immune genetic algorithm (AIGA). The algorithm utilizes a new immune vaccine selection strategy and immune operation method, the optimization process is adaptively changed with the evolutionary algebra, combined with the parallel selection method to optimize the multiobjective logistics distribution path, and the specific steps to solve the multiobjective logistics distribution path problem are given. The experimental results show that the optimal path is calculated as 0—2—8—5—3—1—0; 0—4—7—6—0. AIGA not only efficiently converges to the optimal solution but also stabilizes to a fitness value of 66.5, reflecting better precision than IGA. The computational efficiency and convergence of the algorithm are significantly improved, which verifies the practicability and effectiveness of the algorithm. The adaptive immune genetic algorithm realizes the path optimization of complex logistics distribution, which can better change the global search performance of the original immune genetic algorithm, greatly improve the algorithm convergence speed, and achieve good results in practical applications.
Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization
Authors: Subba Reddy Tatireddy , Harikiran Jonnadula , Enduri Murali Krishna , Hajarathaiah Koduru , Almakdi Sultan , Alshehri Mohammed , Naveed Quadri Noorulhasan , Rahman Md Habibur
Published: July 2022

The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy.
Modeling and Estimation of CO2 Emissions in China Based on Artificial Intelligence
Authors: Wang Pan , Zhong Yangyang , Yao Zhenan
Published: July 2022

Since China’s reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO2) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO2 emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO2 emissions, this study establishes a hybrid intelligent algorithm model suitable for CO2 emissions prediction based on China’s CO2 emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5% in the advance prediction of CO2 emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues.
Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review
Authors: Ferreira-Vanegas Carlos M. , Vélez Jorge I. , García-Llinás Guisselle A.
Published: July 2022

In this systematic literature review (SLR), we use a series of quantitative bibliometric analyses to (1) identify the main papers, journals, and authors of the publications that make use of statistical analysis (SA) and machine learning (ML) tools as well as technological elements of smart cities (TESC) and Geographic Information Systems to predict road traffic accidents (RTAs); (2) determine the extent to which the identified methods are used for the analysis of RTAs and current trends regarding their use; (3) establish the relationship between the set of variables analyzed and the frequency and severity of RTAs; and (4) identify gaps in method use to highlight potential areas for future research. A total of 3888 papers published between January 2000 and June 2021, distributed in four clusters—RTA + HA + SA (SA, n = 399); RTA + HA + ML (ML, n = 858); RTA + HA + SC (TESC, n = 2327); and RTA + HA + GIS (GIS, n = 304)—were analyzed. We identified Accident Analysis and Prevention as the most important journal, Fred Mannering as the main author, and The Statistical Analysis of Crash-Frequency Data: A Review and Assessment of Methodological Alternatives as the most cited publication. Although the negative binomial regression method was used for several years, we noticed that other regression models as well as methods based on deep learning, convolutional neural networks, transfer learning, 5G technology, Internet of Things, and intelligent transport systems have recently emerged as suitable alternatives for RTA analysis. By introducing a new approach based on computational algorithms and data visualization, this SLR fills a gap in the area of RTA analysis and provides a clear picture of the current scientific production in the field. This information is crucial for projecting further research on RTA analysis and developing computational and data visualization tools oriented to the automation of RTA predictions based on intelligent systems.
Application of Bayesian Algorithm in Risk Quantification for Network Security
Authors: Wei Lei
Published: July 2022

Network security risk quantification involves both technical and management aspects. Risk quantification has great uncertainty and cannot be fully quantified. Therefore, the fully objective realization of network information security risk quantification is not yet mature. This paper analyzes and quantifies the network security risks caused by various threat sources through a network security risk quantification model based on the Bayesian algorithm. By combining expert knowledge, the conditional probability matrix under the inference rule of the Bayesian algorithm is clarified, and the subjective judgment information of experts on the damage degree of the target information system is synthesized into the prior information system of network security threat. The Bayesian algorithm is used to realize the observation node of objective assessment information and combining subjective security threat levels to achieve continuity and accumulation of security assessments. The error is about 3%, which has a very good effect on the quantification of network security risk.
Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data
Authors: Dinar Ahmed M. , Raheem Enas A. , Abdulkareem Karrar Hameed , Mohammed Mazin Abed , Oleiwie Marwan Ghazi , Zayr Fawzi Hasan , Al-Boridi Omar , Al-Mhiqani Mohammed Nasser , Al-Andoli Mohammed Nasser
Published: July 2022

The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an effort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more difficult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19 patients were recruited from the Azizia primary health care sector/Wasit Health Directorate/Ministry of Health to form different combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of 82%. The established prediction system allows for the early identification of three severity cases, which reduces deaths.
He’s Yangchao Recipe Ameliorates Ovarian Oxidative Stress of Aging Mice under Consecutive Superovulation Involving JNK- And P53-Related Mechanism
Authors: Zhao Ying , Chen Yun , Miao Chenyun , Wang Ruye , Yang Liuqing , Liu Jingjing , Chen Zhongfang , Zhang Qin , Ma Jing
Published: July 2022

Objective. To evaluate the effects of He’s Yangchao Recipe (HSYC) on ameliorating ovarian oxidative stress of aging mice under consecutive superovulation. Methods. An 8-month-old C57BL/6 female mouse was chosen to establish an aging model under ovarian hyperstimulation. Mice were randomly separated into four groups: R1 as the control group, R4 as the model group, NR4 with N-acetyl-L-cysteine (NAC) administration, and TR4 with HSYC administration. Oocyte collection, in vitro fertilization, and embryo culture were performed. The serum hormone levels were measured by enzyme-linked immunosorbent assays (ELISA); the reactive oxygen species (ROS) level of oocytes, the number of growing follicles, corpus luteum, ovulated oocytes, and developing embryos at each stage, along with the proportions of fragmented oocytes and abnormal mitochondria in granulosa cells (GCs) and the apoptosis rate of GCs were calculated; the mRNA and protein levels of JNK, P53, BAX were detected by real-time PCR and the Simple Western System. Results. HSYC enhanced estradiol, progesterone, and inhibin-B levels and increased growing follicle and corpus luteum and ovulated egg counts compared to the R4 group (

P
<
0.05

), whereas it decreased the proportions of fragmented oocytes (

P
<
0.01

); Meanwhile, embryos from mice subjected to four superovulation cycles with HSYC treated had a higher hatching potential. The ROS level of oocytes is downregulated by HSYC (

P
<
0.01

) and the percentage of abnormal mitochondrial in ovaries of the TR4 group was also significantly declined compared to the R4 group (

P
<
0.05

); the most TUNEL-positive cells proportion was detected in the R4 group; nevertheless, HSYC effectively attenuated this detrimental effect (

P
<
0.05

). The mRNA and protein expressions of JNK and P53 in ovary tissues were reduced in the TR4 group while these genes were upregulated by repeated superovulation (

P
<
0.05

). Conclusions. HSYC exerted promising effects on promoting the diminished ovarian reserve and decreased oocyte quality induced by both aging and consecutive ovarian superovulation, potentially via the ROS/JNK/p53 pathway.
Multithreshold Image Segmentation and Computer Simulation Based on Interactive Processing System
Authors: Dong Liang , Gangyi Ding , Dapeng Yan , Kexiang Huang
Published: July 2022

With the increasing application of interactive processing systems at all levels, in order to better and efficiently spread the application range of interactive processing systems, this study has researched and developed an application system for interactive online processing of DVB-SMODIS data. In addition, correct and improved algorithms are proposed for most of the problems in DVB-SMODIS data. In order to better improve the overall search ability of the BBO algorithm in multithreshold image segmentation, this study discusses a new BBO algorithm. Image segmentation is an important processing part of digital image processing systems and computer vision systems. The use of computer simulation programming technology can simulate and replace the process of objects to produce simulation maps. According to the original object calculated by the computer system, it can be reconstructed and reproduced by referring to the mathematical formula of light waves and using traditional simulation technology experiments. Therefore, this article uses interactive processing technology to study the multithreshold graph segmentation and computer simulation technology.
Application of Decision Tree in PE Teaching Analysis and Management under the Background of Big Data
Authors: Che Yong , Che Kaixuan , Li Qinlong
Published: July 2022

With the continuous development of physical education reform, the defects and deficiencies of physical education teaching in colleges and universities are increasingly exposed. The reform of the original physical education teaching thought, education system, teaching mode, and method has achieved little. At present, the research on the prediction of physical education teaching achievements is mainly aimed at the prediction of athletes and physical education teaching achievements or the prediction of the past data of college students. This paper studies the physical education under the decision tree under the background of big data and constructs the physical education management system. When the number of tests reaches 40, the qualified rate of long-distance running is 65.2%, that of basketball is 68.1%, and that of volleyball is 68.2%. The quality of physical education teaching determines the lifeline of the development of school physical education teaching. In the process of collecting and selecting teaching materials, this paper enriches teaching materials and teaching reflection, cultivates one’s own understanding, and improves the artistic appeal and creativity of teaching.
Design and Application of Marketing Intelligent Platform Based on Big Data Technology
Authors: Chen Xianfeng
Published: July 2022

The progress of big data technology has promoted the development of intelligent marketing for education companies. The online course selection system on the official website of the education company can provide users with online course selection and purchase services. The early marketing conversion rate remained below 0.09% throughout the year. After the analysis of the marketing department, it is found that there are some problems on the official website, such as a low marketing conversion rate. To improve the marketing conversion rate of the official website, the marketing department puts forward business needs. The experiential marketing strategy has been formulated, and it is planned to implement experiential course purchase and secondary marketing on the official website. The IT R & D department has established a big data marketing intelligent recommendation system project to meet the business needs put forward by the marketing department. Firstly, use the big data real-time computing technology to collect the user behavior information of users screening courses on the official website. Real time analysis of user behavior information can predict the courses given to users, which is conducive to the purchase of courses. Then, use the big data machine learning technology to integrate the marketing activity opportunity data in each marketing activity opportunity management system and establish a user portrait. For users who have participated in online or offline marketing activities but have not purchased courses, offline predict the courses and classes that can be recommended to users to carry out secondary marketing for them. Finally, the information of recommendable courses and classes predicted in real time and offline is displayed in the display area of the official website, which makes the marketing conversion rate of the official website reach 0.20%, which is 125% higher than that of the original official website.
Cognitive Heterogeneous Wireless Network and Artificial Intelligence-Based Supply Chain Efficiency Optimization Application
Authors: Yuan Yuan
Published: July 2022

Cognitive radio can specifically perceive the surrounding environment, can understand and learn the changes in the surrounding environment, and can adjust its own adaptation method, channel calculation, and other transmission parameters at any time. This cognitive radio method is more flexible and is an intelligent wireless communication system, a relatively advanced system. Cognitive communication devices can detect changes in the surrounding wireless environment and relatively change their own parameters to cope with the changes in the environment and ensure the stability of communication quality. As a different part of the supply chain, enterprises use the sharing economy and cooperation to improve the overall competitiveness of the enterprises in the supply chain, ultimately achieve a win-win effect for all enterprises in the supply chain, and complete the improvement of the company’s competitiveness. The supply chain model can help companies to be familiar with the structure, operation process, and orientation of the supply chain in which they are located, so that they can clearly understand the work process through simulation scenarios and better understand the problems of the supply chain work process. Supply chain supply with cognitive heterogeneous wireless networks and artificial intelligence is a necessary way to deeply improve the operation and working process of supply chain control. In short, the cognitive radio system proposed in this study can receive network-related messages by observing the surrounding wireless network environment, and can receive internal communication tasks and emergency communication tasks based on historical data and prediction of future relationships. Through the access control process, we make an appropriate strategy and finally complete the communication function of the system.
CAD Design for Recycling and Utilization of Waste Packaging from the Perspective of Ecological Criticism
Authors: Wen Jia
Published: July 2022

In order to realize the value of packaging waste through recycling, detection, classification, maintenance, and reprocessing, the model construction and simulation study of logistics packaging recycling and utilization were put forward. This paper takes logistics companies as the research object, aiming at the business model of logistics companies, uses the Internet of things recycling machine for packaging recycling, and establishes a dual channel packaging recycling model. This model is intended to compare and analyze the difference between traditional packaging waste recycling channel and intelligent recycling machine of the internet of things in terms of recycling cost, profit, recovery rate, waste incineration rate, and other aspects. It is assumed that the recycling proportion of the two channels accounts for half of the packaging waste. The results of simulation analysis on recycling and utilization of dual-channel packaging waste show that in the simulated time period, the recycling rate of the intelligent recycling machine using the internet of things is higher than that of the traditional dispersed recycling vendors. Assume that when the recycling frequency = 2, when the recycling frequency is increased to 2 times a day, the recycling time of the internet of things recycling machine is further shortened, and the recycling efficiency is improved. At the same time, due to the improvement of the recycling rate of the internet of things recycling machine, the amount of waste packaging decreases, so the recycling rate of the dispersed recycling vendors decreases accordingly. The recycling revenue and net profit generated by channel 2 (internet of things recycling machine) are higher than those generated by channel 1 (decentralized recycling vendor responsible for recycling). The average profit of channel 2 is about 28,163.69 yuan, while the average profit of channel 1 is only 9,395.89 yuan. The waste recovered through channel 2 is lower than that in channel 1; the average for channel 1 is 15.58 tons. However, the average value of channel 2 is 14.58 tons. Comprehensive analysis shows that channel 2 has obvious advantages in reducing waste discharge. However, it is necessary to strengthen the research and development of recycling technology, improve the reuse efficiency, and reduce the incineration of nonreusable waste as much as possible to the minimum, which proves the robustness of this paper to the logistics packaging recycling model construction. It is proved that the robustness of this paper for logistics packaging recycling model construction provides strong support for the analysis of waste pallets and packaging recycling-related issues.
MRI Semi-Quantitative Evaluation of Clinical Features of Cartilage Injury in Patients with Osteoarthritis
Authors: Zhou Xianchao , Shen Xiang
Published: July 2022

This study aimed to investigate the correlation between magnetic resonance imaging (MRI) findings of articular cartilage and clinical symptoms in patients with osteoarthritis (OA). Eighty patients with OA were selected as the study subjects (OA group) and 80 healthy subjects during the same period were also selected as the control group. All subjects underwent knee sagittal PDW-SPAIR, sagittal T1WI-aTSE, sagittal T2WI-TSE, coronal PDW-SPAIR, sagittal 3D-WATSc, and sagittal T2 mapping scans. Thereafter, all subjects underwent clinical assessment. The whole-organ MRI score (WORMS) was adopted for MRI examination and semiquantitative analysis, and the T2 value was calculated. The correlation among T2 value, WORMS, and Western Ontario and Mc Master University OA Index (WOMAC) was then compared and analyzed. The correlation coefficients between T2 values and WORMS in each sub-region of patients with OA were 0.8, 0.55, −0.038, 0.811, and 0.743; the correlation coefficients between WORMS and WOAMC were 0.66, 0.71, 0.46, and 0.88; and the correlation coefficients between T2 values and WOAMC were 0.483, 0.33, 0.282, and 0.636, respectively. There was a significant positive correlation between the results of MRI semiquantitative analysis and clinical symptoms as well as disease severity in patients with OA.
Traditional Chinese Medicine Recognition Based on Target Detection
Authors: Lv Bijun , Wu Liyao , Huangfu Tianran , He Jiaru , Chen Wenying , Tan Lu
Published: July 2022

Traditional Chinese medicine (TCM) is widely used in China, but the large variety can easily lead to difficulties in visual identification. This study aims to evaluate the availability of target detection models to identify TCMs. We have collected images of 100 common TCMs in pharmacies, and use three current mainstream target detection models: Faster RCNN, SSD, and YOLO v5 to train the TCM dataset. By comparing the metrics of the three models, the results show that the YOLO v5 model has obvious advantages in the recognition of a variety of TCM, the mean average accuracy of the YOLO v5 is 94.33% and the FPS has reached 75, this model has a smaller number of parameters and solves the problem of detection and occlusion for small targets. Our experiments prove that the target detection technology has broad application prospects in the detection of TCM.
Nicotine Has a Therapeutic Window of Effectiveness in a Drosophila melanogaster Model of Parkinson’s Disease
Authors: Mannett Brady T. , Capt Braden C. , Pearman Krista , Buhlman Lori M. , VandenBrooks John M. , Call Gerald B.
Published: July 2022

Strong epidemiological evidence and studies in models of Parkinson’s disease (PD) suggest that nicotine may be therapeutically beneficial in PD patients. However, a number of clinical trials utilizing nicotine in PD patients have had mixed results, indicating that either nicotine is not beneficial in PD patients, or an important aspect of nicotine therapy was absent. We hypothesized that nicotine must be administered early in the adult fly life in order to have beneficial effects. We show that continuous early nicotine administration improves both climbing and flight deficiencies present in homozygous park25 mutant PD model Drosophila melanogaster. Using a new climbing assay, we identify several climbing deficiencies in this PD model that are improved or rescued by continuous nicotine treatment. Amongst these benefits, it appears that nicotine improves the ability of the park25 flies to descend the climbing vial by being able to climb down more. In support of our hypothesis, we show that in order for nicotine benefits on climbing and flight to happen, nicotine administration must occur in a discrete time frame following adult fly eclosure: within one day for climbing or five days for flight. This therapeutic window of nicotine administration in this PD model fly may help to explain the lack of efficacy of nicotine in human clinical trials.
[Retracted] Value of Transabdominal Combined Transvaginal Color Doppler Ultrasonography in the Distinguish between Uterine Adenomyoma and Uterine Fibroids
Authors: Qi Hongmei , Zhou Changjiang , Huang Zhenyi , Yang Na , Wu Qun
Published: July 2022

Objective. To investigate the value of transabdominal combined transvaginal color Doppler ultrasonography in the diagnosis of uterine adenomyoma. Methods. A total of 80 patients with suspected uterine adenomyoma in our hospital from January 2019 to December 2021 were selected as the study subjects. All of them were examined by transabdominal color Doppler ultrasound (TA‐CDUS) and transvaginal color Doppler ultrasound (TV‐CDUS), and the postoperative pathological examination results were taken as the gold standard to analyze the diagnostic efficacy of different examination methods for uterine adenomyoma. Results. By postoperative pathological biopsy, 46 cases (57.50%) were diagnosed as positive and 34 cases (42.50%) were diagnosed as negative, including 29 cases of uterine adenomyoma and 5 other cases. The sensitivity, accuracy, and negative predictive value of TA‐CDUS combined with TV‐CDUS in the diagnosis of adenomyoma were higher than those of TA‐CDUS (P < 0.05), and the Kappa value between TA‐CDUS and pathological diagnosis was 0.923, which was higher than the 0.615 between TV‐CDUS and pathological diagnosis. TA‐CDUS combined with TV‐CDUS showed that there were significant differences in the distribution of Adier blood flow grades between patients with uterine adenomyoma and uterine fibroids (P < 0.05), and the Adier blood flow grades of patients with uterine adenomyoma were mainly grade 0 and grade I; and the resistance index (RI), peak systolic velocity (Vs), and pulsatile index (PI) in patients with uterine adenomyoma were higher than those in patients with uterine fibroids (P < 0.05). Conclusion. Compared with TA‐CDUS, TA‐CDUS combined with TV‐CDUS is more sensitive and accurate in the diagnosis of uterine adenomyoma and has a good consistency with pathological diagnosis results. Attention should be paid to the blood flow parameter values in the differential diagnosis of uterine fibroids.
Stability Analysis of Shield Inclined Tunnel Faces under the Change Effect of Soil Heterogeneity and Pore Water with Buried Depth
Authors: Sun Hong , Zhang Dao-bing , Yin Hua-dong , Hu A-ping
Published: July 2022

The purpose of this work is to look at how soil heterogeneity and pore water pressure impact shield inclined tunnel excavation face stability as burial depth varies. The calculation model of excavation face stability was established, and the supporting pressure of maintain the excavation face stability was solved via the upper bound method. The results showed that the soil heterogeneity coefficient and pore water pressure coefficient had substantial effects on the supporting pressure. The location of failure face and failure range of shield inclined tunnel under varied heterogeneity coefficient and pore water pressure coefficient were presented. In addition, the boundary depth of shallow tunnel under the influence of soil heterogeneity and pore water was obtained, which supplemented the relevant tunnel code.