bluuebunny/crossref_metadata_2025 huggingface dataset:
Based on the Application of Deep Learning in College Education Evaluation
Authors: An Wen
Published: June 2022

The most significant material in teaching quality assessment is the teacher’s teaching evaluation; nevertheless, as education informatization accelerates, the current teaching management challenge is how to use network technology to evaluate instructors’ teaching quality in conventional teaching. The question of how to evaluate the network teaching environment is a crucial issue in the development of network teaching. The key to educational evaluation lies in the quality of teaching. Therefore, the effective processing and analysis of the huge original data collected in the teaching process of colleges and universities can provide decision support for the evaluation of teaching quality and the formulation of relevant improvement measures. Given the variety and large quantity of original teaching data, this paper proposes a deep learning-based teaching quality evaluation model that organically integrates various original data by constructing deep neural networks and can achieve more accurate teaching quality evaluation, which has practical value. Education evaluation is a complex system engineering, which needs to consume a lot of manpower, material, and financial resources. Through the use of the system to obtain a large number of statistical data, it will provide a basis for in-depth analysis and decision-making.
English Long and Short Sentence Translation and Recognition Method Based on Deep GLR Model
Authors: Wang Hongmei , Zhao Changhan
Published: June 2022

The translation recognition of English long and short sentence information is an important issue to obtain the focus and core of English articles. Based on the deep GLR model, this paper constructs a method framework for English long and short sentence translation and recognition, using different embedding layer parameter initialization methods and using multi-layer computing methods in the sentence decoder. The initial corpus text is segmented and tagged with part-of-speech, then, the part-of-speech tag is appropriately corrected to reduce ambiguity, and then it is manually syntactically tagged. In the simulation process, the English long and short sentence summary and translation components are designed and developed, which can accurately and efficiently obtain the key information of English long and short sentences. The experimental results show that the English long and short sentence translation and recognition method of the deep GLR model improves the accuracy of the model parameters. In terms of model structure, the deep GLR value can be improved by 70.77% by reproducing the multi-layer representation fusion of semantic translation; in terms of data enhancement, the deep GLR value can be increased by 70.35% by means of “back translation,” and the improved model is effective. It promotes the translation and recognition generalization ability of English long and short sentences.
Football Game Video Analysis Method with Deep Learning
Authors: Liu Nian , Liu Lu , Sun Zengjun
Published: June 2022

Football is a beloved sport, and its wide audience makes football video one of the most analytically valuable types of video. Researchers have achieved certain research results in football video content analysis. How to locate interesting event clips from a complete long video is an urgent issue to be addressed in football game video analysis. The granularity of sports event detection results with traditional machine learning is relatively coarse, and the types of events that can be detected are limited. In recent years, deep learning has made good progress in the research of video single-person events and action detection, but there are few achievements in the detection of sports video events. In response to this problem, this work uses a deep learning method to build an event detection model to detect events contained in football videos. The whole model is divided into two stages, in which the first stage is utilized to generate candidate event fragments. It divides the football video to be detected into a sequence of frames of a certain length and scans using a sliding window. Multiple frame sequences within a sliding window form a segment, and each segment is a prediction unit. The frame sequence features within the segment are obtained through a three-dimensional convolutional neural network, which is used as the input of each time point of the bidirectional recurrent neural network and further integrated to generate the event prediction of the segment. The second stage is to further process the above results to remove all segments predicted as nonevents. The thresholds are set according to the detection effect of various events to filter out event fragments with higher probability values, obtain the start and end positions of the events through merging, classify and mark them, and finally output complete event fragments. This work has carried out comprehensive and systematic experiments to verify correctness of the proposed method.
Isorhapontigenin Modulates SOX9/TOLLIP Expression to Attenuate Cell Apoptosis and Oxidative Stress in Paraquat-Induced Acute Kidney Injury
Authors: Zheng Qiang , Zhang Yuan , Wang Xiaofeng , Wang Fudong , Zhao Hongyu
Published: June 2022

Paraquat (PQ) is a widely used herbicide but can be lethal to humans. The kidney is vital for PQ elimination; therefore, explorations for therapeutic approaches for PQ-induced acute kidney injury (AKI) are of great significance. Here, the effects of a natural bioactive polyphenol isorhapontigenin (ISO) on PQ-AKI were investigated. In vitro experiments carried out in PQ-intoxicated rat renal tubular epithelial cells (NRK-52E) showed that ISO treatment inhibited PQ-induced cell apoptosis and oxidative stress, which was evidenced by the decreased proapoptotic proteins [cleaved caspase 3/9 and poly (ADP-ribose) polymerase (PARP)], the reduced oxidative stress indicators [reactive oxygen species (ROS), malondialdehyde (MDA), and lactate dehydrogenase (LDH) leakage], and the increased antioxidants [superoxide dismutase (SOD), nuclear factor E2-related factor 2 (NRF2), and oxygenase-1 (HO-1)]. Furthermore, 50 mg/kg ISO pretreatment before PQ administration significantly attenuated PQ-AKI in rats, as manifested by the improved renal tubule damage, the reduced serum and urine markers of kidney injury, and the inhibited cell apoptosis and oxidative stress in the renal cortex. Furthermore, expression of sex-determining region Y box 9 (SOX9) and Toll-interacting protein (TOLLIP) in NRK-52E cells and the renal cortex was significantly upregulated after ISO treatment. Overexpression of SOX9 increased TOLLIP transcription and attenuated PQ-induced apoptosis and oxidative stress, whereas knockdown of SOX9 impaired the protective effects of ISO on NRK-52E cells against PQ toxicity. In conclusion, the present study demonstrated that ISO modulated SOX9/TOLLIP expression to attenuate cell apoptosis and oxidative stress in PQ-AKI, suggesting the potential of ISO in treating PQ-poisoned patients.
Graph Algorithm for considering Second-Order Effects of Rc Ring-Section Columns
Authors: Yao Kaicheng , He Yingcheng , Wu Shilong , Zhou Donghua
Published: June 2022

The calculation of ring-section reinforcement considering the second-order effect has triple nonlinearity, that is, the nonlinearity of material, geometry and section-width variation, it’s very difficult to calculate. In order to provide a fast calculation tool for the design work, a simplified model of the ultimate curvature of the ring-section column is proposed through calculation and analysis, then calculated the second-order eccentricity by the quadratic parabola hypothesis of the second-order deflection shape of the member, and finally the simplified n-mu second-order equilibrium equation has been obtained. The three-coordinate nomogram for calculation was drawn to further facilitate the design in the paper. The hand-calculated reinforcement of the ring-section column could be done only by making 4 auxiliary lines in the diagram, avoiding iterative solution of the transcendental equation. Because of the dimensionless derivation, this nomogram is multipurpose, it could solve the calculation problems of second-order reinforcement in the case of arbitrary section size and concrete grade of C15 ~ C50.
Research on Mechanical and Carbonization Properties of Hybrid Fiber Iron Tailings Concrete Based on Deep Learning
Authors: Zheng Wenbo , Wang Sheliang , Qu Yang , Liu Kangning , Jia Liangwei
Published: June 2022

Iron tailings sand is a kind of mineral waste, and open-air storage is a common treatment method for iron tailings, which not only has a huge impact on the ecological environment but also occupies a lot of land resources. Therefore, the preparation of high-ductility fiber reinforced iron tailings concrete and its application in practical engineering structures have good application prospects. This paper is based on the deep learning research on the mechanical and carbonization properties of hybrid fiber iron tailings concrete. Therefore, tailings sands with different substitution rates, single-mixed steel fiber, and mixed steel-PVA fiber concrete were prepared in this paper. Its compressive strength, split tensile strength, axial compressive strength, elastic modulus, strain, and carbonization depth were tested. Through the existing concrete compressive stress-strain curve equations, the nonlinear calculation of each group of concrete compressive stress-strain curve equations in this paper is carried out, some parameters are determined, and the carbonation depth equation is established. The results show that, with the increase of tailings content, the properties of concrete increase first and then decrease and the addition of fibers can effectively improve the strength, elastic modulus, peak strain, and carbonization depth of concrete. However, with the increase of PVA fiber content, its performance enhancement effect decreased.
An Adaptive Learning System for English Vocabulary Using Machine Learning
Authors: Xu Yanqiu
Published: June 2022

The vocabulary of a language is the collection of words used in that language. The vocabulary learning of English language plays an important role in learning English language. The expansion of learners vocabulary is linked to both their own efforts and the instruments used to guide learning. Better English vocabulary learning software can increase learner’s passion for studying as well as their learning efficiency. Therefore, this paper aims to explore an adaptive learning system for English vocabulary based on machine learning. At the beginning, it explains the primary modules of the English vocabulary adaptive learning system, as well as the system’s general design and database analysis. Afterwards, the paper looks up the AdaBoost algorithm, where the key parameter is conditional probability, which is primarily used to measure learners’ cognitive adaptation to English learning content. The fitness will be updated once learners finish the selection of English vocabulary learning content, which is known as training. The fitness will be changed gradually through training to push the relevant English vocabulary learning material and complete the adaptive learning system for English vocabulary based on machine learning. The experiments result show that the proposed learning system outperforms previous approaches in terms of learning efficiency, scientifically, and reliability.
The Effect of Chia Seed Extracts against Complete Freund’s Adjuvant-Induced Rheumatoid Arthritis in Rats
Authors: Aljumayi Huda , Aljumayi Adel , Algarni Eman , Algheshairy Reham M. , Alharbi Hend F. , Azhar Wedad , Bushnaq Taqwa , Qadhi Alaa
Published: June 2022

Background and Objectives. It is known that the oxidation of chia seeds is minimal or absent, due to the presence of bioactive compounds, having a great potential in the foods and pharmacological industry. This investigation was done to estimate the nutrition values and anti-oxidant activity of chia seed extracts. Materials and Methods. The protective effects of chia seed extract against complete Freund’s adjuvant (CFA) rheumatoid arthritis in rats were investigated with 100, 200, 300, and 400 ppm/kg BW rat/day chia seeds aqueous extract for 4 weeks. Results. The results obtained revealed that chia seed extract contained high proportions of protein and total dietary fiber values of 21.35% and 27.24%, respectively. Moreover, the results reported that the mineral contents were the greatest in phosphorous, potassium, magnesium, calcium, and sodium (450.09, 410.46, 245.22, 218.69, and 120.34 mg/100 g, respectively). Meanwhile, iron, zinc, and copper were the lowest in chia seeds (9.26, 4.67, and 3.66 mg/100 g, respectively). Besides, vitamins C and E reported 1.65 and 0.82 mg/100 g, respectively. Chia seed extracts were effective in vitro for the bioactive components such as phenolics, flavonoids, and anti-oxidant activities. The biomarkers of complete blood picture, lipid profile, anti-oxidant enzymes, TNF-α, and IL-10 had been improved. Histopathological examination of the rat knee confirmed health amelioration, revealing that chia seed extract consumption can lower pathological changes in injured rheumatoid arthritis rats. Conclusion. It could be seen that the chia seed extracts alleviated the harmful effect of rheumatoid arthritis CFA-induced rats.
Solitary Solutions to the Fractional Generalized Hirota–Satsuma-Coupled KdV Equations
Authors: Liu Jing , Li Zhao
Published: June 2022

In this paper, the complete discrimination system method is used to the fractional generalized Hirota–Satsuma-coupled KdV equations, which depicts the dispersive long wave widely applied in fluid mechanics. First, by the wave transformation, the fractional generalized Hirota–Satsuma-coupled KdV equation can be transformed into ordinary differential equations. Second, a series of new solutions are constructed by the discrimination system method of the fourth order polynomial, these solutions include kink solitary wave solutions, Jacobian elliptic function double periodic solutions, trigonometric function solution, solitary wave solutions, two-period solutions of elliptic function, and implicit solutions formula solutions. Finally, the comparison between our results and others’ works is also given.
Application of Data Mining Technology in Software Intrusion Detection and Information Processing
Authors: Zhao Xuemin
Published: June 2022

In order to improve the efficiency of the software intrusion detection system, the author proposes an application based on data mining technology in software intrusion detection and information processing. Apply data mining technology to software intrusion detection; first, analyze and research software intrusion detection technology and data mining technology, including the basic concepts of software intrusion detection, the realization technology of software intrusion detection, the classification of software intrusion detection systems, and the typical software intrusion detection system situation. By detecting and analyzing known intrusion data and using association rules, constructing the inspection system rule base enables the system to learn independently and improve itself and has good scalability, while improving the degree of automation and complete intrusion detection. Experimental results show that under the same test sample, the accuracy of the detection system model designed in this paper is 95.67%, higher than the other three detection systems, and the false alarm rate is lower than other systems, which has certain advantages. It is proved that the system in this paper can help improve the accuracy of software intrusion detection, significantly reduce the false alarm rate and false alarm rate of software intrusion detection, and provide reference for the optimization and improvement of software intrusion detection system and information processing. The system has a certain degree of self-adaptation, which can effectively detect external intrusions.
Genetic Variants Associated with Chronic Obstructive Pulmonary Disease Risk: Cumulative Epidemiological Evidence from Meta-Analyses and Genome-Wide Association Studies
Authors: Liu Caiyang , Ran Ran , Li Xiaoliang , Liu Gaohua , Xie Xiaoyang , Li Ji
Published: June 2022

Background. Last two decades, many association studies on genetic variants and chronic obstructive pulmonary disease (COPD) risk have been published. But results from different studies are inconsistent. Therefore, we performed this article to systematically evaluate results from previous meta-analyses and genome-wide association studies (GWASs). Material and Methods. Firstly, we retrieved meta-analyses in PubMed, Embase, and China National Knowledge Infrastructure and GWASs in PubMed and GWAS catalog on or before April 7th, 2022. Then, data were extracted and screened. Finally, two main methods—Venice criteria and false-positive report probability test—were used to evaluate significant associations. Results. As a result, eighty-eight meta-analyses and 5 GWASs were deemed eligible for inclusion. Fifty variants in 26 genes obtained from meta-analyses were significantly associated with COPD risk. Cumulative epidemiological evidence of an association was graded as strong for 10 variants in 8 genes (GSTM1, CHRNA, ADAM33, SP-D, TNF-α, VDBP, HMOX1, and HHIP), moderate for 6 variants in 5 genes (PI, GSTM1, ADAM33, TNF-α, and VDBP), and weak for 40 variants in 23 genes. Five variants in 4 genes showed convincing evidence of no association with COPD risk in meta-analyses. Additionally, 29 SNPs identified in GWASs were proved to be noteworthy based on the FPRP test. Conclusion. In summary, more than half (52.38%) of genetic variants reported in previous meta-analyses showed no association with COPD risk. However, 13 variants in 9 genes had moderate to strong evidence for an association. This article can serve as a useful reference for further studies.
Analysis of Trends in Awareness Regarding Hepatitis Using Bayesian Multiple Logistic Regression Model
Authors: Al-Alwan Ali , Feroze Navid , Nazakat Aneela , Essa Almuhayfith Fatimah , Alshenawy R.
Published: June 2022

Diseases like hepatitis remained a major health concern, especially in developing countries. The awareness and knowledge about such diseases are of prime importance. The analysis of socioeconomic factors associated with the tendency of awareness and knowledge about the said diseases is fundamental. However, in developing countries like Pakistan, very few studies have considered such investigations using nationally representative data. In addition, a careful review of the literature suggests that no studies have analyzed the trends in awareness and knowledge about said disease with respect to time using nationally representative datasets. Furthermore, the existing literature regarding these studies has utilized the classical methods for the analysis. We have considered a detailed study for analyzing the trends in awareness and knowledge about the said disease in the general population of Pakistan from 2012 to 2018, using nationally representative data collected through Pakistan Demographic and Health Surveys. In addition, we have considered the Bayesian methods for the analysis and performance of the proposed Bayes methods that have been compared with the frequently used classical methods. The results indicated that the proposed Bayesian multiple logistic regression models performed better as compared to classical multiple logistic regression models (CMLRMs). This is due to fact that widths of 95% CIs were smaller for Bayesian multiple logistic regression models (BMLRM), as compared to classical multiple logistic regression models. The findings of the study suggest that there are severe disparities (with respect to different socioeconomic groups) in the knowledge and awareness of respondents for hepatitis. The levels of knowledge and awareness about the said disease are drastically low for respondents living in rural areas, having lower levels of education and wealth. These disparities seem to persist, as the corresponding odds have not changed much during the period 2012 to 2018. The policy-maker should plan and implement the strategies to reduce the observed disparities for different sectors of society.
Research on Dynamic Response of Shallow Buried Tunnel Lining Constructed by Drilling and Blasting Method
Authors: Wu Bo , Cui Yaozhong , Meng Guowang , Xu Shixiang , Xia Chengming , Li Yicai , Zhang Yao
Published: June 2022

To study the dynamic response of shallow buried tunnel lining by drilling and blasting method, ABAQUS simulation software was used to establish a tunnel blasting finite element model based on the consideration of in situ stress. Dynamic static coupling numerical simulation was conducted to analyze the vibration response and stress response of tunnel lining. The simulation results were compared and analyzed with field monitoring data to obtain the dynamic response law of tunnel lining. The results show that in the same tunnel lining section, the vibration velocity response in the Y direction is the largest and the vibration velocity response in the Z direction is the smallest. The location of the peak particle velocity of the tunnel lining in three directions appears differently, and the location of the maximum MISES stress appears differently for different excavation sections. The arch shoulder is most affected by horizontal vibration in the X direction, and the vault is most affected by horizontal vibration in the Y direction. The dynamic response at the foot or arch shoulder position away from the lining section of the working face will show the “whip tip effect.” A sudden change in MISES stress occurs at the location of the footing in the 315° direction of the liner section, and the liner is not uniformly stressed in this range.
Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network
Authors: Liu Yang , Gao Mengfei , Zong Humin , Wang Xinping , Li Jinshuang
Published: June 2022

Matching the detection speed and accuracy in practical applications is considered to improve the speed of video object detection in front of trains. Lightweight convolutional neural network MobileNet and clustering ideas are combined to improve the object detection algorithm, and the MYOLO-lite model object detection algorithm is designed. The self-made object dataset of a forward-moving train is combined with the K-means clustering idea. The transcendental frame is redesigned to enhance scale adaptability. The nonmaximum suppression and loss function improvement methods of the MYOLO-lite network model are proposed for occluded tracks in front of the train, obstruction of trains, low detection accuracy of large object coincidence, and uneven distribution of positive and negative samples. The mean average precision value of the experimentally designed MYOLO-lite model object detection algorithm reaches 95.74%, with 42.04 frames per second. Detecting a picture only takes 0.024 s.
Research on Structural Optimization of Prefabricated Components Based on Improved Immune Genetic Algorithm
Authors: Ma Tingting
Published: June 2022

On the premise of satisfying the design conditions and practical engineering needs, it is of practical significance to optimize the design of assembly components. Therefore, in this paper, based on the improved immune genetic algorithm, the structural optimization of prefabricated components is deeply studied, where it is converted into binary gene code, and the structural design requirements are introduced into the algorithm through structural layout vaccine and concrete strength vaccine. In addition, the section size of components is designed according to economic indicators, which has proved that the research is effective for the structural optimization method of prefabricated components.
Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning
Authors: Attique Haleema , Shah Sajid , Jabeen Saima , Khan Fiaz Gul , Khan Ahmad , ELAffendi Mohammed
Published: June 2022

DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.
Experimental Characterization of the Seismic Responses of Silt-Interlayered Stratified Sandy Tailings Slopes Using Centrifuge Shaking Table Tests
Authors: Zhang Zitao , Hu Jing , Zhang Xuedong
Published: June 2022

This study aims to characterize the seismic responses of silt-interlayered stratified sandy tailings slopes. In order to achieve this, a series of model tests were carried out in an advanced centrifuge shaking table. The experimental results demonstrate that sandy tailings slopes interlayered with silts are more prone to flow failure compared with homogeneous slopes. The failure process of a steep silt-interlayered sandy slope is further elaborated in detail, and two modes of tailings release have been identified. The initial loading cycles may only lead to localized deformation near the relatively shallow silty layer; however, there is continuous build-up of excess pore water pressure in deeper sandy tailings. With increasing the number of loading cycles, more sandy tailings start to spread laterally, and ejection of tailings may occur. After ejection, the excess pore pressure turns to dissipate, while the tailings near slope crest become liquefied and then flow toward the downstream direction layer by layer, leading to tailings release. This is the first mode of tailings release. Due to the continuous lateral spreading, the displaced tailings eventually arrive at the crest of the starter dam and then flow along its slope at the downstream side, leading to tailings release. This is the second mode of tailings release. Those unique and invaluable observations can be used to improve the seismic design of tailings impoundments and to validate associated numerical methods.
Economic Development Analysis of China’s Coastal Industries Based on Deep Learning and SDA Model Weighting
Authors: Li Zhuo
Published: June 2022

China’s coast is the main spatial carrier and important geographical unit for future industrial development in China. We propose a weighted average decomposition method based on three basic problems in the theory of SDA model decomposition method, investigate the relevant properties of this method, and prove that two decomposition methods widely used in the literature—bipolar decomposition method and midpoint power decomposition method—are the approximate solutions of this decomposition method. Based on the idea of the Wasserstein distance algorithm for machine learning, the Wasserstein distance algorithm and its solution are improved by using the matrix expansion Sinkhorn algorithm and the entropy regularization constraint method, and the industry copolymerization index is constructed through hypothesis testing and Monte Carlo simulation to measure the level of industry copolymerization in coastal China. The results show that the level of industry copolymerisation within the same quantile industry is greater than that between industries across quantile industries in China’s coasts.
Higher Education Teaching Quality Evaluation Model Based on Improved RBF Neural Network
Authors: Qi Xiaowei , Tan Aidi , Gao Yan
Published: June 2022

In order to improve the reliability of higher education quality evaluation, this paper improves the RBF neural network algorithm based on the characteristics of higher education teaching data. Using RBF neural network technology, the predictive model established after learning from samples of actual teaching data can be used to evaluate teaching quality. Moreover, this paper combines the improved RBF neural network to construct a higher education teaching quality evaluation model. The model built in this article needs the support of a support vector machine (SVM) to build a sentiment analysis module and a teaching quality evaluation module. After the model is constructed, the performance of the model built in this article is evaluated, and the performance of the model is displayed by combing statistics. The teaching quality assessment of the higher education model is between 80% and 85%.The test results show that the higher education teaching quality appraise model depended on the improved RBF neural network put forward in this paper has certain effects.
Clinical Efficacy of Thoracoscopic Surgery with the da Vinci Surgical System versus Video-Assisted Thoracoscopic Surgery for Lung Cancer
Authors: Zhou Jin-Cai , Wang Wu-Ping , Wu Shu-Qiang , Wang Jia-Lin , Li Wen-Hai
Published: June 2022

Objective. To assess the clinical efficacy of thoracoscopic surgery with the da Vinci surgical system versus video-assisted thoracoscopic surgery (VATS) for lung cancer. Methods. From August 2019 to December 2020, 193 patients with lung cancer assessed for eligibility scheduled for surgery in our hospital were recruited and assigned at a ratio of 1 : 1 to receive VATS (control group) or thoracoscopic surgery with the da Vinci surgical system (research group). The primary measurement is the clinical efficacy of the two surgical modalities. Results. The baseline features of the research group were comparable with those of the control group (

P
>
0.05

). Besides, the two groups showed similar tumor types, tumor locations, and clinicopathological staging (

P
>
0.05

). Da Vinci surgical system-assisted thoracoscopic surgery had short operative time, less intraoperative blood loss, better lymph node dissection, and lower intraoperative conversion rates compared to VATS. Compared with the control group, the research group had significantly higher postoperative forced expiratory volume in one second (FEV1), forced vital capacity (FVC), maximal voluntary ventilation (MVV), the functional assessment of cancer therapy-general module (FACT-G) of the FACT-lung (FACT-L) Chinese version V4.0, lung cancer-specific module scores, and total scores (

P
<
0.05

). The research group showed better postoperative drainage volume, shorter intubation duration, and length of hospital stay and a lower incidence of complications versus the control group (

P
<
0.05

). The da Vinci surgical system reduced the probability of intraoperative mistakes and better ensured a safe and satisfactory surgery. Conclusion. The thoracoscopic surgery with the da Vinci surgical system better reduces intraoperative and postoperative bleeding, shortens drainage and intubation duration, enhances the lung function and survival quality of patients, and lowers the risk of surgical mistakes to ensure surgical safety versus VATS.
Body Weight Is a Valid Predictor of the Long-Term Prognosis of Cervical Cancer
Authors: Lu Li , Ji Shuqi , Jiang Jing , Yan Yu
Published: June 2022

Objective. To identify and validate effective clinical predictors for the long-term prognosis of patients with cervical cancer. Methods. Cervical cancer patients were retrieved from the TCGA database, and patients’ clinical data were collected and analyzed for the predictive value of long-term prognosis. In the other branch of the study, patients with cervical cancer and admitted to our hospital between January 1, 2016, and December 31, 2016, were retrieved and followed up for prognosis analysis. Results. In the database patient cohort of our study, 607 cases with cervical cancer were analyzed. Aneuploidy score (

p
=
0.012

), Buffa hypoxia score (

p
=
0.013

), histologic grade (

p
=
0.01

), fraction genome altered >0.4 (

p
<
0.001

), weight > 60 kg (

p
<
0.001

), height > 160 cm (

p
=
0.047

), BMI <18.5 (

p
=
0.023

), Winter hypoxia score (

p
=
0.002

), and adjuvant postoperative radiotherapy were good predictors for disease-free survival (DFS), while aneuploidy score (

p
=
0.001

), MSI sensor score > 0.5 (

p
=
0.035

), person neoplasm status (

p
<
0.001

), race (

p
=
0.006

), Ragnum hypoxia score (

p
=
0.012

), weight (

p
<
0.001

), height (

p
<
0.001

), and BMI < 18.5 (

p
=
0.04

) were good predictors for overall survival (OS). In the admitted patient cohort, age over 60 years old at the time of diagnosis was the only clinical factor influencing the long-term DFS (

p
=
0.004

). TNM stage above III (

p
=
0.004

), body weight > 70 kg (

p
<
0.001

), and complicated with other cancer (

p
<
0.001

) were clinical factor influencing the long-term OS. Conclusions. Clinical factors, especially common to both cohorts, could be used to show the long-term prognosis of cervical cancer.
Effect of Improved Association Algorithm on Mining and Recognition of Audit Data
Authors: Qiu Putianyi
Published: June 2022

Audit evidence is the proof material on which the auditors issue audit opinions and make relevant audit conclusions, and audit evidence in the era of big data presents new characteristics in terms of adequacy, relevance, and reliability. This paper combines the improved association algorithm to construct the audit data mining system to improve the data processing effect of the audit process. Moreover, this paper proposes a new dynamic threshold method, gives the calculation method of some important parameters in the algorithm, and presents the improved cell-based association algorithm flow. In addition, this paper discusses how the outlier algorithm is applied to the acquisition of audit evidence and the application scenarios in the audit system. The experimental research results show that the audit data mining system based on the improved association algorithm proposed in this paper has a good effect in the audit of accounting and financial data.
The Determinants of Adolescent Glycolipid Metabolism Disorder: A Cohort Study
Authors: Liang Xiao-Hua , Xiao Lun , Chen Jing-Yu , Qu Ping , Tang Xian , Wang Yuwei
Published: June 2022

Background. The increased prevalence of glycolipid metabolism disorders (GLMD) in childhood and adolescents has a well-established association with adult type 2 diabetes and cardiovascular diseases; therefore, determinants of GLMD need to be evaluated during this period. Objectives. To explore the prevalence of and risk factors for GLMD from the prenatal period through childhood and adolescence. Methods. A bidirectional cohort study which was established in 2014 and followed between March 1 and July 20, 2019, was used to illustrate the impact factors for GLMD. Stratified cluster sampling in urban-rural areas was used to include subjects from four communities in Chongqing. 2808 healthy children aged between 6 and 9 years in 2014 entered the cohort in 2014 and followed in 2019 with a follow-up rate of 70%. 2,136 samples (aged 11.68 ± 0.60 years) were included. Results. The prevalence rates of insulin resistance (IR), prediabetes/diabetes, and dyslipidemia were 21.02%, 7.19%, and 21.61%, respectively. Subjects with an urban residence, no pubertal development, dyslipidemia in 2014, higher family income, and higher parental education had significantly elevated fasting insulin (FI) or homeostasis model assessment of insulin resistance (HOMA-IR) levels; subjects with female sex, no pubertal development, dyslipidemia in 2014, obesity, gestational hypertension, maternal weight gain above Institute of Medicine guidelines, and single parents had increased triglyceride or triglyceride/high-density lipoprotein (HDL). Adolescents with rural residence had higher HbA1c level. Conclusion. We observed that the prevalence of GLMD was high in childhood and adolescents, and rural-urban areas, sex, pubertal development, dyslipidemia in a younger age, maternal obesity, and hypertension were associated with increased GLMD risk, suggesting that implementing the community-family intervention to improve the GLMD of children is essential.
Robust Transmit Beamforming Algorithm for Low-Altitude Slow-Speed Small Target Detection
Authors: Jin Songpo , Li Hongtao , Wang Pengyi , Shen Yi , Zhuang Shanna
Published: June 2022

Strong ground clutter echoes make it difficult to detect low-altitude slow-speed small (LSS) targets. To suppress ground clutter effectively in LSS target detection, a robust transmit beamforming algorithm has been proposed in this paper. Sidelobes in the ground side can be cut down, with the excess energy concentrated on the air side, which would be cleaner and simpler. The objective function is a second-order cone programming problem and can be solved by the convex optimization algorithm. With the consideration of taking full advantage of transmit power, the weight vector is further processed under the unimodular constraint. Numerical experiments are carried out to demonstrate the validity and superiority of the proposed method.
LncXIST Facilitates Iron Overload and Iron Overload-Induced Islet Beta Cell Injury in Type 2 Diabetes through miR-130a-3p/ALK2 Axis
Authors: Li Weiyuan , Feng Qiu , Wang Chenrong , Yin Zhao , Li Xiaolu , Li Lei
Published: June 2022

Iron overload is directly associated with diabetes mellitus, loss of islet beta cell, and insulin resistance. Likewise, long noncoding RNA (lncRNA) is associated with type 2 diabetes (T2D). Moreover, lncRNAs could be induced by iron overload. Therefore, we are going to explore the molecular mechanism of lncRNA XIST in iron overload-related T2D. Real-time quantitative PCR and Western blot were used to detect gene and protein levels, respectively. TUNEL and MTT assay were performed to examine cell survival. The glucose test strip, colorimetric analysis kit, ferritin ELISA kit, and insulin ELISA kit were performed to examine the levels of glycolic, iron, and total iron-binding capacity, ferritin, and insulin in serum. Fluorospectrophotometry assay was used to examine labile iron pool level. XIST was higher expressed in T2D and iron overload-related T2D rat tissues and cells, and iron overload-induced promoted XIST expression in T2D. Higher XIST expression was associated with iron overload in patients with T2D. Knockdown of XIST alleviated iron overload and iron overload-induced INS-1 cells injury. Further, we found that XIST can sponge miR-130a-3p to trigger receptor-like kinase 2 (ALK2) expression. Moreover, knockdown of ALK2 alleviated iron overload and iron overload-induced INS-1 cells injury by inhibiting bone morphogenetic protein 6 (BMP6)/ALK2/SMAD1/5/8 axis but reversed with XIST upregulation, which was terminally boosted by overexpression of miR-130a-3p. XIST has the capacity to promote iron overload and iron overload-related T2D initiation and development through inhibition of ALK2 expression by sponging miR-130a-3p, and that targeting this axis may be an effective strategy for treating patients with T2D.
Internet Entrepreneurship Information Resource Sharing System Based on Improved Deep Learning Algorithm
Authors: Wang Xiaohua
Published: June 2022

In order to promote the circulation of Internet entrepreneurial information, based on information theory and general security theory, this paper explains how to construct the attack channel and defense channel of the k-anonymity model, so as to provide a reliable network foundation for the transmission of related information. In order to fully solve the problems existing in multiuser requests, this paper proposes two modules, single-request sharing mode and multirequest sharing mode, to process query requests in the request queue to maximize the reuse of request results. Moreover, the model constructed in this paper needs to be processed through deep learning first and then shared through the intelligent model. Therefore, the data learning and data sharing of the model are mainly analyzed during model validation. The experimental simulation results show that the intelligent sharing model of Internet entrepreneurial information resources based on deep learning proposed in this paper has good data learning and data transmission effects.
miR-29a-3p in Exosomes from Heme Oxygenase-1 Modified Bone Marrow Mesenchymal Stem Cells Alleviates Steatotic Liver Ischemia-Reperfusion Injury in Rats by Suppressing Ferroptosis via Iron Responsive Element Binding Protein 2
Authors: Li Xiang , Wu Longlong , Tian Xuan , Zheng Weiping , Yuan Mengshu , Tian Xiaorong , Zuo Huaiwen , Song Hongli , Shen Zhongyang
Published: June 2022

Hepatic ischemia-reperfusion injury (IRI) is an inevitable result of liver surgery. Steatotic livers are extremely sensitive to IRI and have worse tolerance. Ferroptosis is considered to be one of the main factors of organ IRI. This study is aimed at exploring the role of ferroptosis in the effect of heme oxygenase-1-modified bone marrow mesenchymal stem cells (HO-1/BMMSCs) on steatotic liver IRI and its mechanism. An IRI model of a steatotic liver and a hypoxia reoxygenation (HR) model of steatotic hepatocytes (SHPs) were established. Rat BMMSCs were extracted and transfected with the Ho1 gene to establish HO-1/BMMSCs, and their exosomes were extracted by ultracentrifugation. Ireb2 was knocked down to verify its role in ferroptosis and cell injury in SHP-HR. Public database screening combined with quantitative real-time reverse transcription PCR identified microRNAs (miRNAs) targeting Ireb2 in HO-1/BMMSCs exosomes. miR-29a-3p mimic and inhibitor were used for functional verification experiments. Liver function, histopathology, terminal deoxynulceotidyl transferase nick-end-labeling staining, cell viability, mitochondrial membrane potential, and cell death were measured to evaluate liver tissue and hepatocyte injury. Ferroptosis was assessed by detecting the levels of IREB2, Fe2+, malondialdehyde, glutathione, lipid reactive oxygen species, glutathione peroxidase 4, prostaglandin-endoperoxide synthase 2 mRNA, and mitochondrial morphology. The results revealed that HO-1/BMMSCs improved liver tissue and hepatocyte injury and suppressed ferroptosis in vivo and in vitro. The expression of IREB2 was increased in steatotic liver IRI and SHP-HR. Knocking down Ireb2 reduced the level of Fe2+ and inhibited ferroptosis. HO-1/BMMSC exosomes reduced the expression of IREB2 and inhibited ferroptosis and cell damage. Furthermore, we confirmed high levels of miR-29a-3p in HO-1/BMMSCs exosomes. Overexpression of miR-29a-3p downregulated the expression of Ireb2 and inhibited ferroptosis. Downregulation of miR-29a-3p blocked the protective effect of HO-1/BMMSC exosomes on SHP-HR cell injury. In conclusion, ferroptosis plays an important role in HO-1/BMMSC-mediated alleviation of steatotic liver IRI. HO-1/BMMSCs could suppress ferroptosis by targeting Ireb2 via the exosomal transfer of miR-29a-3p.
Autophagy Induced by Muscarinic Acetylcholine Receptor 1 Mediates Migration and Invasion Targeting Atg5 via AMPK/mTOR Pathway in Prostate Cancer
Authors: Wang Qianhui , Chen Jinying , Zhang Mi , Wang Hong , Zeng Yan , Huang Yanping , Xu Chen
Published: June 2022

Increasing numbers of researchers discovered the expression of muscarinic acetylcholine receptor 1 in human cancers, while its function in human prostate cancer is still unclear. Our present study focused on CHRM1 to clarify its role in mediating autophagy in prostate cancer. We used immunohistochemistry, western blotting, and immunofluorescence experiments to observe the expression of muscarinic acetylcholine receptor 1 both in nude mice with subcutaneous tumors and in prostate cancer cells. The autophagy was observed through transmission electron microscopy, western blotting, quantitative real-time PCR, and immunofluorescence. After that, we used lentivirus to establish CHRM1 and Atg5 knockdown models. Then, the migration and invasion abilities after knocking down muscarinic acetylcholine receptor 1 and Atg5 were detected by transwell assays. In addition, the AMPK/mTOR pathway-related targets were detected by western blotting. We found that muscarinic acetylcholine receptor 1 was abundantly expressed both in vitro and in vivo in prostate cancer. The overexpression of muscarinic acetylcholine receptor 1 positively regulated migration and invasion in tumor cells as well as the activation of autophagy. Muscarinic acetylcholine receptor 1 was highly correlated with Atg5 and activated the AMPK/mTOR signaling pathway. Downregulation of Atg5 inhibited cell autophagy in prostate cancer cells and the migration and invasion of prostate cancer cells. Meanwhile, abnormal expressions of AMPK/mTOR pathway-related proteins were found. In conclusion, the present findings indicated that muscarinic acetylcholine receptor 1 is highly expressed in prostate cancer cells and promotes cell invasion and migration of prostate cancer. Autophagy is activated in prostate cancer cells and the activation of muscarinic acetylcholine receptor 1 positively regulates autophagy in prostate cancer cells. Moreover, muscarinic acetylcholine receptor 1 induces autophagy-mediated cell migration and invasion by targeting Atg5 in prostate cancer cells via AMPK/mTOR pathway, which uncovered that regulating muscarinic acetylcholine receptor 1, identified in this study, can be a promising solution for treating prostate cancer.
Nutritional Composition of Biscuits from Wheat-Sweet Potato-Soybean Composite Flour
Authors: Roger Ponka , Bertrand Bisso Monesso Marvist , Gaston Zomegni , Nouhman Bissada , Elie Fokou
Published: June 2022

The aim of this study was to evaluate the nutritional composition of biscuits from wheat-sweet potato-soybean composite flours. Substitutions of wheat flour with sweet potato and soybean flours at the rate of 25% (wheat flour 375 g, sweet potato flour 100 g, and soybean flour 25 g) (T1), 50% (wheat flour 250 g, sweet potato flour 200 g, and soybean flour 50 g) (T2), 75% (wheat flour 125 g, sweet potato flour 300 g, and soybean flour 75 g) (T3), and 100% (wheat flour 0 g, sweet potato flour 400 g, and soybean flour 100 g) (T4) were made to obtain the wheat-sweet potato-soybean composite flours. Wheat flour without sweet potato and soybean flour was used as control (T0). The functional properties (water absorption capacity and water solubility index) of the flours were determined. Sensory evaluation of biscuits was determined. The proximate and mineral contents of the most preferred biscuits were determined. Results showed that the substitution significantly increases the functional properties of the flours (

P
<
0.05

). Sample 75% (T3) is the most preferred biscuit. The incorporation of sweet potato and soy flour in the formulation significantly increases the moisture, fat, protein, fiber, copper, manganese, calcium, magnesium, and potassium contents of the biscuits (

P
<
0.05

). Biscuit 75% (T3) records the highest levels in protein and fiber while sample T0 (control) records the highest levels of iron, zinc, and sodium. In terms of overall acceptability and nutrient contents, sample 75% (T3) is the best biscuits.