bluuebunny/crossref_metadata_2025 huggingface dataset:
Trends in Intelligent and AI-Based Software Engineering Processes: A Deep Learning-Based Software Process Model Recommendation Method
Authors: Alshammari Fahad H.
Published: October 2022

In recent years, numerous studies have successfully implemented machine learning strategies in a wide range of application areas. Therefore, several different deep learning models exist, each one tailored to a certain software task. Using deep learning models provides numerous advantages for the software development industry. Testing and maintaining software is a critical concern today. Software engineers have many responsibilities while developing a software system, including coding, testing, and delivering the software to users via the cloud. From this list, it is easy to see that each task calls for extensive organization and preparation, as well as access to a variety of resources. A developer may consult other code repositories, websites with related programming content, and even colleagues for information before attempting to build and test a solution to the problem at hand. In this investigation, we aim to identify the factors that led to developing the recommender. This system analyzes the recommender’s performance and provides suggestions for improving the software based on users’ opinions.
Application of Motion Effect Evaluation Algorithm Based on Random Forest
Authors: Dong Jin , Dong Dengxiao
Published: October 2022

The development of big data technology makes the feature selection technology gradually perfect. The advantages of different feature selection technologies are different. Among them, random forest algorithm belongs to integrated feature selection algorithm. Integrated processing of classification results can screen out the most representative feature impact. Based on this background and random forest algorithm, this paper analyzes the evaluation of motion effect. After the measurement, this paper obtains the body data before and after the training. After the calculation, the change data of the body index are determined. The random forest feature selection method is used as the carrier to determine the corresponding index attribute set. In the process of data set input, the corresponding whole input data set is formed through data classification. The completion of training, through the comparative experiment, is conducive to clear the degree of influence of physical indicators and then complete the exercise effect evaluation. The research shows that the random forest algorithm has significant advantages in the evaluation of sports effect and can effectively improve the accuracy of classification.
Infrared Spectrum Analysis of Aged Asphalt Regenerated by Recycled Engine Oil Bottom
Authors: Zhao Wanfeng , Li Xinjun , Feng Zhengang
Published: October 2022

In order to analyze the feasibility of recycled engine oil bottom (REOB) as a regenerant for old asphalt and to determine its improvement effect on the performance of recycled asphalt, this paper analyzes the functional groups of two kinds of old asphalt and matrix asphalt through infrared spectrum test, such as carbonyl group, butadiene double bond, styrene double bond, methyl group, methylene group, and aromatic ring in the molecules of REOB. Find out the compatibility characteristics between them from the microscopic point of view. The recycled asphalt with 4%, 6%, and 8% content was prepared, and the physical property test was conducted to analyze the regeneration effect of different contents of REOB, and the optimum amount of REOB for 10-year-old asphalt was determined to be 6%. The study found that REOB contains aromatic hydrocarbons and colloids, which can supplement the reduction of aromatic hydrocarbons and colloids caused by asphalt aging. Therefore, REOB helps to adjust the composition of aging asphalt, improve its performance, and achieve good regeneration.
Artificial Intelligence-Based English Self-Learning Effect Evaluation and Adaptive Influencing Factors Analysis
Authors: Shu Xing , Xu Chengjun
Published: October 2022

Under the background of continuous development in our country, traditional English education can no longer meet the needs of modern times. Through the evaluation of English autonomous learning effect of artificial intelligence and the analysis of the influencing factors of adaptability, the teaching effect of English class is improved and the students’ awareness of autonomous learning is cultivated. In the pilot study, students now have an overall level of adaptation (3 < M < 4) supported by English proficiency. That is, the standard deviation is 1. The overall level of self-study in English is higher than that of boys (M = 3.40 for females, M = 3.32 for males, and M = 3.32 for males). Compared with non-English majors, English majors are more suitable for self-study of artificial intelligence in English (M = 3.59 for English majors), (M = 3.36 for non-English majors), and students can improve their adaptive ability to learn AI by creating models. Transfer learning is the key to improving learners’ English proficiency, and adaptive learning is the key to achieving this goal. Self-adaptive learning ensures the quality of students’ autonomous English homework. Improper English learning not only affects students’ learning outcomes but also affects their ability to learn English.
Factors Influencing Survival Status of HIV/AIDS after HAART in Huzhou City, Eastern China
Authors: Jin Meihua , Yang Zhongrong , Li Jing , Liu Xiaoqi , Wu Zhenqian
Published: October 2022

Background. Highly active antiretroviral therapy (HAART) can effectively reduce the risk of death and opportunistic infections in patients with HIV/AIDS. The aim of this study was to analyse the survival status and its influencing factors in HIV/AIDS after HAART. Methods. The data on patients’ sociodemographic characteristics, treatment information, and follow-up results from the Information Management System of the Chinese Center for Disease Control and Prevention were obtained. Bivariate and stepwise multivariate Cox proportional hazards regression model analyses were performed. Results. A total of 1812 participants were included in this study, of which 1716 were still alive (survival group) and 96 had died (death group). The results indicated that respondents who were elderly (HR = 1.053, 95% CI: 1.037–1.069,

P
<
0.01

), who had heterosexual transmission (HR = 2.422, 95% CI: 1.314–4.465,

P
<
0.01

) and whose current WHO clinical stage was stage III or IV (HR = 2.399, 95% CI: 1.215–4.735,

P
<
0.05

) were more likely to have died; respondents whose baseline CD4+ T-lymphocyte count was equal to or more than 200 cells/μL (HR = 0.412, 95% CI: 0.275–0.616,

P
<
0.05

) were unlikely to have died. Conclusions. It is recommended that HAART be provided to HIV/AIDS patients at an early clinical stage and that the health services for HIV/AIDS patients after taking medicines be strengthened, which will help promote adherence to therapeutic regimens and improve quality of life.
Artificial Intelligence Investigation on (Al‐Si‐Fe) Alloy Reinforced with Nanoceramic Particles by RSM
Authors: Kanna S. K. Rajesh , Sundar G. Naveen , Ganesan R. , Mallireddy Naresh , Shingadia Hitesh , Anandaram Harishchander , De Poures Melvin Victor , Paramasivam Prabhu
Published: October 2022

Al 4043 alloy is extensively used as a filler material for welding aluminum alloys, especially when welding alloys from the Al 6000 series. It is utilized in aerospace and automotive structural components. For longer life in automotive applications, the wear resistance of Al 4043 alloy must be improved. According to research, tungsten carbide has good wear resistance. In this research, Al 4043 alloy is reinforced with varying percentages (1, 3, and 5%) of nano‐sized tungsten carbide to increase wear resistance. Taguchi L27 orthogonal array is employed for the wear analysis. The Taguchi signal‐to‐noise ratio is used to determine the optimal parameters for minimizing wear and coefficient of friction. The regression model and artificial neural network are developed to predict the experimental results. The outcomes of the regression model and artificial neural network are compared to the experimental results to demonstrate both models’ efficacy. A confirmation test was carried out for the optimal process parameter. The result shows that the minimized specific wear rate of 0.12 mm3/Nm, coefficient of friction of 0.01, and frictional force of 1.02 N are achieved at the optimal combination.
Front-Page News Classification Model Based on the Stacking of Textual Context and Attribute Information
Authors: Wang Kan , Cao Kaichen , Chen Mingren , Yan Zhenzi , Zhong Lingfeng , Yang Hui , Cai Shimin
Published: October 2022

The front-page news in authoritative newspapers usually represents extremely significant national policies. Accurately classifying the front-page news from an amount of news helps us to quickly acquire and deeply understand the changing political and economic situations. In this paper, we propose a front-page news classification model StackText based on stacking the textual context and attribute information of news. In the proposed model, we first balance the classification size in the training set through the weighted random sampling algorithm, then construct the context and attribute feature vectors through the textual embedding and statistical analysis of news, and finally pretrain a classifier StackNet based on a neural network to realize the front-page news classification in the testing set. Taking People’s Daily as the experimental example, we compare the proposed model with benchmark methods based on statistical learning and deep learning. Based on the news sets in four stages of People’s Daily, the experimental results of the front-page news classification show that the proposed model achieves the highest average accuracy and a better balance between precision and recall, which is verified by the corresponding

F

1

-score.
Regional Economic Forecasting Method Based on Recurrent Neural Network
Authors: Liu E. , Zhu Haiou , Liu Qing , Udimal Thomas Bilaliib
Published: October 2022

Macroeconomic situation is the overall performance of the economic situation of a country and region. Making accurate forecasts of macroeconomic trends is of great significance for analyzing the success or failure of macroeconomic control policies, evaluating the quality of economic system operation, and correctly formulating future development planning strategies. The macroeconomic system is a nonlinear system, the environment is constantly changing, and additional disturbing factors directly affect the operation of the macroeconomic system, which has a great impact on the forecast results. The historical information required for macroeconomic modeling is unstable, unclear, and incomplete, which makes it very difficult to solve such problems with traditional forecasting methods. In response to the multivariate and nonlinear characteristics of macroeconomic forecasting, this paper proposes the application of artificial neural networks for forecasting. This paper introduces the recurrent neural network into the field of economic forecasting to solve the problems of the traditional BP (back propagation) neural network method. The experimental data are verified and the experimental results prove that the studied scheme based PSO-GRU improve the performance of economic forecasting.
Teaching Practice of Engineering Management Course for Engineering Education Certification under Background of Artificial Intelligence
Authors: Wang Dan , Han Fengyi , Zhao Qi , Lv Yinyin
Published: October 2022

With the advancement of China’s industrial construction, the field of engineering management has also attracted more attention. However, China’s engineering management major is currently in a growing stage due to the issue of the opening years, and the teaching and practice setting of each course is also in an immature stage, which makes China’s engineering management majors present more and more problems. The truancy rate has been increasing year by year, the students’ dominant position in the class has become objectified, and their trust in teachers has decreased. Students’ learning shows the characteristics of individualization and diversity. Higher requirements are put forward for teachers’ teaching quality, and schools lack an effective supervision mechanism. In order to solve these problems better, it is imperative to reform and innovate the course teaching of engineering management majors. The core of engineering education accreditation is to confirm that engineering graduates meet established quality standards recognized by the industry. It is a unique method to test whether the course teaching of engineering management majors is qualified and attracts many scholars to discuss it. Engineering education accreditation has attracted many scholars to discuss it because it is a unique means to test the qualifications of engineering management students’ course teaching. This study was based on an in-depth exploration of the teaching practice of engineering management courses and combines artificial intelligence with an engineering education certification. Through the research and analysis of colleges and universities, the research finally showed that the engineering management professional course teaching of engineering education certification under the background of artificial intelligence can promote the attendance of students in school by about 20%. The achievement of course teaching objectives has increased by about 13% and the comprehensive ability level of graduates has increased by about 8%. It improved the overall level of students and the teaching quality and efficiency of engineering management courses and also promoted the development of college education so that today’s engineering management graduates can better meet the needs of today’s society.
Design of Political Online Teaching Based on Artificial Speech Recognition and Deep Learning
Authors: Chen Xiajin
Published: October 2022

With the emergence of the information age, computers have entered the homes of ordinary people and have become essential daily appliances for people. The integration of people and computers has become more popular and in-depth. Based on this situation, how to make computers and humans communicate intelligently and make human-computer interaction more convenient and practical is the main issue of scientific research and discussion on computers. Language recognition is widely used in industry, the economy, commerce, tourism, style, and sports. The BP neural network based on deep learning has supercomputing functions and is very suitable for speech recognition, which promotes the promotion and application of speech recognition technology in many fields. Different types of applications and diverse scenarios enable users to experience different personal experiences. This paper examines the use of application software for ideological and political theory courses in colleges and universities and the application of application software for ideological and political theory courses in colleges and universities in the management of ideological and political theory courses. Analyzed the use of apps in the management of ideological and political theory courses in colleges and universities, analyzed the possibilities and challenges that this brought to the management of ideological and political theory courses in colleges and universities based on the app, and put forward the necessary countermeasures. Including the development and design of ideological and political theory applications, combining deep learning and CTC algorithms to build acoustic models, using server-client interaction, designing an ideological and political theory course app based on speech recognition and deep learning, and forming an offline speech recognition system software platform, the app provides a certain reference for improving the skills of teaching managers.
Active Learning Assisted Admission and Bandwidth Management in HWN for Facilitating Differential QoS under Multicriteria Factors
Authors: Biradar Pallavi , Shweta Patil , Padmanaban K. , Suresh A. , Bakhar Mohammed , Yeshitla Alazar
Published: October 2022

Control of call admission and management of bandwidth are the two important functionalities to achieve higher call handling capacity in heterogeneous wireless networks (HWN). This work addresses the problem of supporting differential qualities of services (QoS) with adherence to multicriteria factors in addition to reducing call-dropping probability in HWN. Toward this end, learning-assisted admission and bandwidth management are proposed. The decision to control the call acceptance ratio and bandwidth allocation level is learned continuously based on current network dynamics and the differential QoS requirements of the current calls. This learning reduces the call drop probability and slippage in QoS for calls. The parameters employed for evaluation in the suggested approach for call admission control include call priority, service type, service delivery mode, bandwidth availability for scalable and nonscalable calls, QoS distortion rate, and call ratio.
Intelligent Evaluation Method of Engineering Cost Feasibility Model Based on Internet of Things
Authors: Chen Liangqiong , Zhang Hao , Wang Fang
Published: October 2022

In order to solve the problem of low accuracy of high-rise building cost evaluation, the author proposes an intelligent evaluation method of engineering cost feasibility model based on the Internet of Things. According to the overall investment composition of the construction project, this paper classifies the cost index, determines the overall cost correction coefficient, and uses the gray correlation analysis method to build the evaluation index system; according to the structure of BP neural network, the network error is calculated, and the error signals of output layer and hidden layer are defined by gradient descent method to obtain the network weight adjustment formula; finally, this paper uses the adaptive learning rate adjustment formula to set the network parameters, introduces the key parameters in the construction project to the input layer, and establishes the final cost evaluation model. Experimental results show that the error rate of this system is controlled within 10%, which meets the requirements of investment estimation. Conclusion. The proposed method can accurately and quickly evaluate the best solution for the cost of high-rise buildings with less information and has strong nonlinear information processing capabilities.
Inhibition of Cerebral Ischemia/Reperfusion Injury by MSCs-Derived Small Extracellular Vesicles in Rodent Models: A Systematic Review and Meta-Analysis
Authors: Zhang Lei , Pei Chaoying , Hou Dan , Yang Guoshuai , Yu Dan
Published: October 2022

Small extracellular vesicles (sEVs) secreted by mesenchymal stem cells (MSCs) have shown great therapeutic potential in cerebral ischemia-reperfusion injury (CIRI). In this study, we firstly performed a systematic review to evaluate the efficacy of MSCs-derived sEV for experimental cerebral ischemia/reperfusion injury. 24 studies were identified by searching 8 databases from January 2012 to August 2022. The methodological quality was assessed by using the SYRCLE ‘s risk of bias tool for animal studies. All the data were analyzed using RevMan 5.3 software. As a result, the score of study quality ranged from 3 to 9 in a total of ten points. Meta-analyses showed that MSCs-derived sEVs could effectively alleviate neurological impairment scores, reduced the volume of cerebral infarction and brain water content, and attenuated neuronal apoptosis. Additionally, the possible mechanisms of MSCs-derived sEVs for attenuating neuronal apoptosis were inhibiting microglia-mediated neuroinflammation. Thus, MSCs-derived sEVs might be regarded as a novel insight for cerebral ischemic stroke. However, further mechanistic studies, therapeutic safety, and clinical trials are required. Systematic review registration. PROSPERO CRD42022312227.
Serum Zinc Ion Concentration Associated with Coronary Heart Disease: A Systematic Review and Meta-Analysis
Authors: Meng Heyu , Ruan Jianjun , Chen Yanqiu , Yan Zhaohan , Meng Xin , Li Xiangdong , Liu Jinsha , Mao Cuiying , Yang Ping
Published: October 2022

Aim. Coronary heart disease is a major cause of mortality in developed and developing countries. Changes in the trace element concentration in the human body are one of the main reasons for the transition of the human body from a healthy to a diseased state. In this meta-analysis, we have studied the relationship between the reduction in serum zinc ion concentration and coronary heart disease. Methods. We used PubMed and Cochrane (as of June 30, 2021) databases for the literature search. Per the requirements of this systematic review, case-control studies involving serum zinc ion concentration and coronary heart disease were searched, and the quality of the included studies was evaluated before the meta-analysis. Results. A total of 3,981 cases were found across seven articles. The standard mean deviation (SMD) of serum zinc ion concentration was −0.22 [−0.28, −0.15], z = 6.52, and

P
<
0.05

indicated that the difference was statistically significant. The forest plot results show that I2 = 34% < 50%, and the Q test showed

P
=
0.17
>
0.1

. These results suggest a lack of heterogeneity among the selected articles. Results from the funnel chart indicated that this study was free from publication bias. Conclusion. The results of this meta-analysis reveal that a decrease in serum zinc ion concentration is related to the occurrence of coronary heart disease. Clinically, monitoring the serum zinc ion levels is proven to be of great significance for patients with coronary heart disease.
A New Radar Echo Generation Model for Ultra-Low Altitude Targets in Far-Field Conditions
Authors: Wang Zhao Long , Tong Chuang Ming , Wang Tong , Wang Yi Jin , Wang Qing Kuan
Published: October 2022

This paper proposed a new radar echo simulation method for ultra-low altitude targets in far-field conditions. Based on the electromagnetic (EM) scattering calculation of target and environment, combined with the weighted four-path model, the scattering data of target, environment, and multipath are obtained. The Range-Doppler ring partitioning method is used to determine the size of the minimum resolution units, and then the environment is divided into several scattering elements. By using the method of temporal decomposition, the wide-time pulse is decomposed into a plurality of narrow pulse signals, and the narrow pulses act on scattering elements with different distances and orientations in space. The total echo is obtained by a linear superposition of the responses of each scattering unit. In addition, the numerical results with different parameters, including signal bandwidths, target types, and target height, are simulated and analysed. The simulation results demonstrate that the proposed method can provide a better description of the scattering characteristics of sea-skimming targets in complex scenes in far-field conditions. Meanwhile, it can be applied to the detection and recognition of ultra-low altitude targets above the sea surface.
Motivation of Student Management Subjects in Universities Based on Network Intelligence
Authors: Xin Like , Wang Feng , Sun Xiaodong , Ding Guoqing , Wang Hui
Published: October 2022

With the intelligent development of the society and the development of the big data era, the network intelligence applications in various fields have deeply penetrated them, and colleges and universities, as the place where information technology is most widely used, have been profoundly affected by the network intelligence. In this context, besides the traditional use of digitalization, informatization, and networking to assist in managing students’ studies and life, the work of the main body of student management in colleges and universities is also gradually developing towards intelligence. In the research of this study, through the application and development of network intelligence, the intelligent research of college student management is carried out by using the technical advantages of massive data access, so as to improve the motivation of college student management subjects through intelligence, make students feel the orderly and reliable humanized management while improving the efficiency of student management, and provide sufficient technological guarantee for college management.
[Retracted] Influence Mechanism of Educational Leadership on Environmental Accounting Based on Big Data Algorithm
Authors: Jin Minyan , Yao Li
Published: October 2022

Over the last 20 years, big data techniques in teaching have been overgrown. Making educational decisions now includes information knowledge as a crucial component. This started a trend for using big data algorithms strategically. Technological advances have been used to analyze the enormous amount of information and develop strategic judgments. The current study aims to address issues with the conventional instructional, administrative management solution focused on manual rule formulation in big data storage and interpretation and has poor efficiency in analyzing big data and lacking value in developing education leadership qualities. The study suggests an educational leadership model based on big data algorithm (ELM‐BDA) to explore the student leadership performance that relies on cooperative filtration and fuzzy C‐means (FCM) and big data. The different influencing mechanisms and factors directly linked to educational leadership were also analyzed using a big data algorithm. To build an intelligent institutional administrative system, the research also exposes it to organizational studies. By evaluating the big data research already in existence, this study emphasizes the expanding significance of big data. Additionally, this study explores the effects of big data analytics on educational leadership qualities by utilizing an FCM. A scoring system is designed to predict the student’s leadership level, and using the big data algorithms, the students are motivated and trained to improve their skills. The education and learning method can be enhanced at educational institutions through better decision‐making to use this big data for leadership development. Big data facilitates efficient educational decision‐making by merging various data and telecommunications technologies. Using big data in schooling will increase—leadership quality among students. To effectively use big data for decision‐making, academic leaders must create new types of learning and monitoring systems.
The Impact of Molar Proportion of Sodium Hydroxide and Water Amount on the Compressive Strength of Slag/Metakaolin (Waste Materials) Geopolymer Mortar
Authors: Al-Husseinawi Fatimah N. , Atherton William , Al-Khafaji Zainab , Sadique Monower , Yaseen Zaher Mundher
Published: October 2022

This investigation aimed to improve great early geopolymer mortar strengths under various parameters with various binder proportions to reduce the use of cement since the OPC production process leads to high emissions of CO2. Hence, to solve this problem, alternative materials were used. In this research, metakaolin (MK) and ground‐granulated blast‐furnace slag (GGBFS) waste materials were utilized and mixed together with the sodium hydroxide and alkaline activator sodium silicate (NaOH and Na2SiO3). The performance of the various mixtures was assessed via compressive strength testing based on British standards. The compressive strength was found to be highly affected by molar proportion and water amount. The optimum strength was 77.8 MPa for a mix design of 95% GGBFS +5% MK and a 2.5 mass proportion between Na2SiO3 and NaOH (12 Molar), together with a 0.2 water/binder proportion.
Establishment and Validation of a Nomogram Prognostic Model for Epithelioid Hemangioendothelioma
Authors: Li Yujun , Zhang Zibo , Zhang Chunxia , Li Jia , Zhang Bin , Dong Yan , Cui Xiaonan
Published: October 2022

Background. Epithelioid hemangioendothelioma (EHE) is an ultrarare vascular sarcoma. At present, the epidemiological and clinical characteristics and prognostic factors are still unclear. Our study attempted to describe clinical features, investigate the prognostic indicators, and establish the nomogram prediction model based on the Surveillance, Epidemiology, and End Results (SEER) database for EHE patients. Methods. The patients diagnosed with EHE from 1986 to 2018 were collected from the SEER database and were randomly divided into a training group and a validation group at a ratio of 7 : 3. The Cox proportional hazard models were used to determine the independent factors affecting prognosis and establish a nomogram prognostic model to predict the survival rates for patients with EHE. The accuracy and discriminative ability of the model were measured using the concordance index, receiver operating characteristic curves, and calibration curves. The clinical applicability and application value of the model were evaluated by decision curve analysis. Results. The overall age-adjusted incidence of EHE was 0.31 patients per 1,000,000 individuals, with a statistically significant difference per year. Overall survival at 1, 5, and 10 years for all patients was 76.5%, 57.4%, and 48.2%, respectively. Multivariate Cox regression analysis identified age, tumour stage, degree of tissue differentiation, surgical treatment, chemotherapy, and radiotherapy as independent factors affecting prognosis (

P
<
0.05

). The C-index values for our nomogram model of training group and validation group were 0.752 and 0.753, respectively. The calibration curve was in good agreement with the actual observation results, suggesting that the prediction model has good accuracy. The decision curve analysis indicated a relatively large net benefit. Conclusions. The nomogram model may play an important role in predicting the survival rate for EHE patients, with good concordance and accuracy, and can be applied in clinical practice.
Cognitive Feature Extraction of Puns Code-Switching Based on Neural Network Optimization Algorithm
Authors: Zhang Jing , Liao Qiaoyun , Li Lipei
Published: October 2022

Code-switching is the choice of a language, a variant of using multiple languages in the same conversation. Broadly speaking, code-switching refers to adjusting one’s language style, appearance, behavior, and expression in order to improve the comfort of others in exchange for fair treatment, quality service, and employment opportunities. “Besieged City” is considered a masterpiece of 20th-century China. From the data point of view, this work has a total of 110 code shifts, but there are many studies on this language phenomenon, but none of them involve the perspective of register. However, language translation research based on register theory is of great significance. It is generally believed that the human brain’s thinking is divided into three basic ways: abstract (logical) thinking, image (intuitive) thinking, and inspiration (awareness) thinking. Artificial neural networks are the second way to simulate human thinking. Therefore, this paper proposes research on cognitive feature extraction of pun code-switching based on a neural network optimization algorithm. It mainly introduces code-switching under cognitive language and also briefly analyzes code-switching and speech feature extraction and uses a neural network optimization algorithm to conduct an in-depth analysis of code-switching. Finally, in the experimental part, the experimental analysis of the famous novel “Besieged City” is carried out, the application of 89 language code-switching in the text is deeply analyzed, and the data analysis of its three variables is carried out from the perspective of register. The experimental results show that: in novels, there are two types of code-switching: preparation and improvisation. 24 code-switches are prepared, accounting for 26.9%, and 10 code-switches are improvisation, accounting for 8.9%. As for the verbal code-change, there are both preparatory and impromptu ones. 36 code-switching cases were improvised, accounting for 40.4%, and 19 code-switching cases were prepared, accounting for 21.3%. The analysis also confirms that the more formal the text, the less linguistic transformation it contains.
Therapeutic Effects and Mechanisms of Action of Garlic (Allium sativum) on Nonalcoholic Fatty Liver Disease: A Comprehensive Systematic Literature Review
Authors: Pourreza Sanaz , Azar Pouria Sefidmooye , Sanaie Sarvin , Noshadi Nooshin , Jalali Saeedeh , Niazkar Hamid Reza , karimi Arash , Vajdi Mahdi
Published: October 2022

Nonalcoholic fatty liver disease (NAFLD) is globally the leading cause of hepatic dysfunction. Garlic has many physiological benefits, including anti-inflammatory, antioxidant, anticancer, lipid-lowering, and antidiabetes effects. The present study aimed to systematically review the effects of garlic (Allium sativum) and its mechanisms of function in managing NAFLD and its associated complications. The guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statements were applied to perform the study (CRD42021289348). The Scopus, Embase, Web of Science, Cochrane PubMed, and Google Scholar databases were searched until February 2022. According to the inclusion criteria, finally, 12 studies were entered into the study. The evidence provided in the study revealed that garlic could regulate the development of NAFLD via several mechanisms of action, such as lowering body weight, modulating lipid and glucose metabolism, and reducing inflammation and oxidative stress (OS). Overall, the beneficial effects of garlic in the treatment of NAFLD make it a potential therapeutic and efficient agent in managing NAFLD and its related risk factors. There is an insufficient number of clinical trials addressing the effects of garlic in humans; therefore, conducting more human research in the future is recommended.
Liquid-Liquid Phase Separation Promotes Protein Aggregation and Its Implications in Ferroptosis in Parkinson’s Disease Dementia
Authors: Li Mengzhu , Fan Yaohua , Li Qinglian , Wang Xiaoling , Zhao Lijun , Zhu Meiling
Published: October 2022

The pathological features of PDD are represented by dopaminergic neuronal death and intracellular α-synuclein (α-syn) aggregation. The interaction of iron accumulation with α-syn and tau was further explored as an essential pathological mechanism of PDD. However, the links and mechanisms between these factors remain unclear. Studies have shown that the occurrence and development of neurodegenerative diseases such as PDD are closely related to the separation of abnormal phases. Substances such as proteins can form droplets through liquid-liquid phase separation (LLPS) under normal physiological conditions and even undergo further liquid-solid phase transitions to form solid aggregates under disease or regulatory disorders, leading to pathological phenomena. By analyzing the existing literature, we propose that LLPS is the crucial mechanism causing abnormal accumulation of α-syn, tau, and other proteins in PDD, and its interaction with iron metabolism disorder is the key factor driving ferroptosis in PDD. Therefore, we believe that LLPS can serve as one of the means to explain the pathological mechanism of PDD. Determining the significance of LLPS in neurodegenerative diseases such as PDD will stimulate interest in research into treatments based on interference with abnormal LLPS.
A Multimodel-Based Deep Learning Framework for Short Text Multiclass Classification with the Imbalanced and Extremely Small Data Set
Authors: Tong Jiajun , Wang Zhixiao , Rui Xiaobin
Published: October 2022

Text classification plays an important role in many practical applications. In the real world, there are extremely small datasets. Most existing methods adopt pretrained neural network models to handle this kind of dataset. However, these methods are either difficult to deploy on mobile devices because of their large output size or cannot fully extract the deep semantic information between phrases and clauses. This paper proposes a multimodel-based deep learning framework for short-text multiclass classification with an imbalanced and extremely small dataset. Our framework mainly includes five layers: the encoder layer, the word-level LSTM network layer, the sentence-level LSTM network layer, the max-pooling layer, and the SoftMax layer. The encoder layer uses DistilBERT to obtain context-sensitive dynamic word vectors that are difficult to represent in traditional feature engineering methods. Since the transformer part of this layer is distilled, our framework is compressed. Then, we use the next two layers to extract deep semantic information. The output of the encoder layer is sent to a bidirectional LSTM network, and the feature matrix is extracted hierarchically through the LSTM at the word and sentence level to obtain the fine-grained semantic representation. After that, the max-pooling layer converts the feature matrix into a lower-dimensional matrix, preserving only the obvious features. Finally, the feature matrix is taken as the input of a fully connected SoftMax layer, which contains a function that can convert the predicted linear vector into the output value as the probability of the text in each classification. Extensive experiments on two public benchmarks demonstrate the effectiveness of our proposed approach on an extremely small dataset. It retains the state-of-the-art baseline performance in terms of precision, recall, accuracy, and F1 score, and through the model size, training time, and convergence epoch, we can conclude that our method can be deployed faster and lighter on mobile devices.
Application of Data Mining System in User Network Environment Based on SVM Optimization Algorithm
Authors: Yanying Yang
Published: October 2022

Nowadays, different types of data and information combine and interact with each other, forming a complex and huge information network. Using data mining technology, one can effectively obtain the hidden data contained in the data bureau. This technology is the most commonly used way to obtain network target data at present. In this paper, we try to practically apply related algorithms by studying the theory of multi-information fusion. Aiming at the diversity and practicality of the network, the multi-information fusion method was optimized and improved on the basis of the traditional multi-information fusion method. Secondly, a data mining system based on the concept and algorithm of association rules is established, which simplifies the working mode of frequent mining and then improves the data mining model. Finally, an empirical analysis is designed. A group of data samples are selected from the network for preliminary processing, and the data set is brought into the system for testing. From the test results, it can be seen that the algorithm designed in this paper can effectively obtain the target data and works well in a complex network environment, can analyze meaningful data association using user network rules, and provides important guidance for optimizing network information and improving extraction efficiency. This paper combines data mining technology and multi-information fusion technology to conduct in-depth research and further complete the algorithm design by combining the two technologies, which proves the accuracy and processing efficiency of the algorithm.
Research on the Evaluation of Ecotourism Resources: Based on the AHP Model
Authors: Ren Lei , Lei Wen
Published: October 2022

The comprehensive evaluation of ecotourism resources in the Qinling Mountains scenic spot using the AHP method can provide a reference for the sustainable construction and development of ecotourism resources, as well as the development of other tourism areas. Based on the analytic hierarchy process, the evaluation system of Qinling Mountain Ecotourism Resources is constructed, and the construction system is used to evaluate tourism ecological resources. The results show that, among the subtarget levels, the highest score is the interaction of tourism areas in the same region and the second is the applicability of regulations. These two indicators have a high impact on the score of Qinling Mountain Ecotourism Resources. In the project layer, the source of passenger flow has the largest weight, which is 0.1717, and the distribution of resource types has a smaller weight. At the index level, participation needs to be considered for tourism participation design. The Qinling scenic area has high historical and cultural value, diverse resource types, good tourist source market conditions, and great tourism development potential. However, problems exist, such as low resource aggregation and imperfect tourism supporting facilities development. It is necessary to develop ecotourism resources on the premise of ensuring environmental quality while taking into account the in-depth excavation of the experience and ornamental values of tourism resources, establishing the tourism characteristics of scenic spots from many aspects, such as improving tourism participation.
Estrogen Protects against Renal Ischemia-Reperfusion Injury by Regulating Th17/Treg Cell Immune Balance
Authors: Zhang Yang , Chang Yuechen , Han Ziwei , Ma Ketao , Zeng Xiansi , Li Li
Published: October 2022

Inflammation is a critical mediator of renal ischemia-reperfusion (I/R) injury (IRI), and T lymphocytes exert a key role in the renal IRI-induced inflammation. Connexin 43 (Cx43) is related to the maintenance of T lymphocyte homeostasis. Various preclinical researches have reported that estrogen is a renoprotective agent based on its anti-inflammatory potential. The present research is aimed at studying the role of T lymphocytes activated by Cx43 in 17β-estradiol-mediated protection against renal IRI. Female rats were classified into six groups: control rats, I/R rats, ovariectomized rats, ovariectomized I/R rats, and ovariectomized rats treated with 17β-estradiol or gap27. Levels of serum creatinine (Scr) and blood urea nitrogen (BUN) and Paller scoring were dramatically increased in I/R rats, especially in ovariectomized rats. By contrast, these indicators were markedly decreased by administering estradiol or gap27. Immunofluorescence staining revealed that CD4+ T cells infiltrated kidney tissues in the early stage of IRI. In both peripheral blood and renal tissue, the proportion of CD3+CD4+ T cells and ratio of CD4+ to CD8+ were high in I/R rats, especially in ovariectomized rats. The proportion of CD3+CD8+ T cells was low in peripheral blood but high in renal tissues. Administration of estrogen or Gap27 reversed these effects. IL-17 levels in both serum and tissue homogenate were significantly increased in ovariectomized rats subjected to I/R but significantly decreased in estrogen or gap 27 treated rats. The opposite trend was observed for IL-10 levels. Correlation analysis demonstrated that IL-17 was correlated positively with BUN, Scr, and Paller scores, while IL-10 was negatively correlated with these indicators. Western blot showed that Cx43 expression was markedly increased in the peripheral blood T lymphocytes of I/R rats, especially ovariectomized rats. After intervention with estrogen and gap27, Cx43 expression was significantly downregulated. These findings indicate that Cx43 may participate in the regulation of Th17/Treg balance by estrogen against renal IRI.
Random Algorithm and Skill Evaluation System Based on the Combing of Construction Mechanism of Higher Vocational Professional Group
Authors: Jia Wei
Published: October 2022

At present, the employment situation in China is very severe, and there are both recruitment and employment difficulties in the society. There are many reasons for this problem, among which, the incompatibility between talents training and social demand in higher vocational colleges is one of the main reasons, and it is necessary for higher vocational colleges to effectively evaluate students’ skills training with the real demand. Based on this, this paper studies the study of stochastic algorithm and skill evaluation system based on the combing of the construction mechanism of higher vocational professional clusters and builds a higher vocational skill evaluation system based on a simple analysis of the skill evaluation system and related evaluation algorithms in higher vocational institutions. Principal component analysis was selected to realize the quantitative processing of skill evaluation indexes, and random forest algorithm was used to realize skill evaluation. The simulated annealing algorithm is introduced to realize parameter selection, parameter optimization, and weight setting, and experiments are designed to analyze the performance of the algorithm. The simulation results show that the random forest algorithm is applied to skill evaluation with high accuracy, small error, and better generalization ability.
Overexpression of FAM83A Is Associated with Poor Prognosis of Lung Adenocarcinoma
Authors: Liu Xin , Fu Meng , Xia Daqing , Ji Zimei , Hu Nana , Leng Zaijun , Xie Wang , Fang Yuan , Zhang Junqiang
Published: October 2022

Family with sequence similarity 83, member A (FAM83A) plays an essential and fundamental role in the proliferation, progression, and apoptosis of many malignant tumors, including lung cancer. This study aimed to determine the expression pattern of FAM83A in lung adenocarcinoma (LUAD) and its correlation with the prognosis of cancer and the survival of the patients. Bioinformatics analysis, immunohistochemistry, and Western blotting were used to explore and detect the expression of FAM83A in LUAD cells. The mechanism of FAM83A in proliferation and migration was examined. The correlation between FAM83A expression and survival rate was assessed by the Kaplan-Meier and Cox regression. FAM83A expression was elevated in LUAD tissues and was related to shorter overall survival (

P
<
0.05

). A significant increase in FAM83A protein was observed in the LUAD tissue (

P
<
0.05

). Compared with patients with early-stage tumors (stage I-II), those with advanced stage tumors (stage III-IV) had significantly higher FAM83A expression levels (

P
<
0.05

). Downregulation of FAM83A led to a reduction in cell proliferation, a decrease in migration ability, and diminished epithelial-mesenchymal transition (EMT) in the lung cancer cell lines. Overexpression of FAM83A was associated with early lymph node metastasis and poor overall survival among LUAD patients. The findings indicated that FAM83A may play a critical role in promoting the LUAD progression and thus might serve as a novel prognostic marker in LUAD.
1030 nm All-Fiber Closed-Loop Fiber Optic Gyroscope with High Sensitivity
Authors: Keskin Hakan , Vural Hüseyin Avni , Altınöz Bağış , Bektik Ümit , Altan Hakan
Published: October 2022

Angle random walk (ARW) is a critical noise component of a typical gyroscope alongside with bias instability noise. ARW has dominant effects especially in short-term accuracy. The measurement uncertainty degrades with the deterioration in ARW resulting in the lowered overall gyroscope accuracy. Many inertial navigation applications such as satellite control and gyro-compassing require low ARW. For an interferometric fiber optic gyroscope, lowest detectable rotation is proportional to scale factor of sensor and inversely proportional to optical bandwidth of light source used in the gyroscope. In this study, using a novel pump laser control closed-loop method, gyroscope performance is able to significantly enhance including signal-to-noise ratio (SNR). Fiber optic gyroscope includes an Yb-doped amplified spontaneous emission (ASE) source with broad emission spectra of 15 nm bandwidth used as light source in order to improve gyroscope sensitivity. The final ARW performance is about 0.008°/√hr for a fiber coil of 150 m length.