bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Inhibitory Effect of DC-CIK Cells on Lung Adenocarcinoma via Anti-Tim-3 Antibody and Antiprogrammed Cell Death-1 Antibody and Possible Mechanism
Authors: Zhou Liang , Chen Qijiu , Chen Hui , Wang Li , Zhang Jianyong
Published: July 2022

Objective. To investigate the effect and mechanism of blocking the signaling pathways of the T-cell immunoglobulin and mucin domain-containing protein 3 (Tim-3) and programmed death protein 1 (PD-1) in dendritic cell-cytokine induced killer (DC-CIK) cells on human lung adenocarcinoma A549 cells. Methods. Peripheral blood mononuclear cells (PBMCs) were isolated and induced into mature DC-CIK cells by cytokines in vitro. After blocking the Tim-3 and PD-1 signaling transduction pathways with anti-Tim-3 and anti-PD-1 antibodies, DC-CIK cells were coincubated with A549 cells. The killing effect of DC-CIK cells against A549 cells was measured by a CCK-8 assay. The impact of DC-CIK cells on the invasion and migration ability of A549 cells was detected by the Transwell test. The apoptosis rate of DC-CIK cells and the ratio of CD4+, CD8+, and DC-CIK cell subsets were determined by flow cytometry. The cell proliferation of DC-CIK was detected by the CCK-8 assay. Results. The antibodies of anti-Tim-3 antibody and anti-PD-1 could block Tim-3+ and PD-1+ DC-CIK cells and could significantly increase the killing effect of DC-CIK cells on A549 cells. The number of A549 cells under the microporous membrane of the Transwell chamber was reduced considerably in invasion and migration tests. Anti-Tim-3 and anti-PD-1 antibodies significantly reduced apoptosis of DC-CIK cells. No significant differences were observed in the ratios of CD4+ and CD8+ DC-CIK cell subsets or the proliferation capacity of DC-CIK cells in each group. Conclusion. Blocking the Tim-3 and PD-1 signaling pathways of DC-CIK cells with antibodies can enhance the killing ability of DC-CIK cells in A549 cells and significantly suppress the invasion and migration ability of A549 cells. The potential mechanism may be related to reduced apoptosis of DC-CIK cells.
DEM Investigation of Discrete Heat Transfer Behavior of the Grinding Media in Ball Mills
Authors: Yin Zixin , Wang Nan , Li Tongqing
Published: July 2022

This study presented a numerical model for the quantitative assessment of the heat transfer behavior of grinding media inside a ball mill. Effects of various mill speeds, grinding media filling, and the number of lifters on heat transfer were studied and verified by comparing the experimental results and the numerical simulations calculated by DEM (Discrete Element Method). The results show that the heat transfer of grinding media has a strong sensitivity to the variation of the mill speed, grinding media filling, and the number of lifters. The optimum grinding conditions for heat transfer behavior can be determined in terms of the temperature field of the grinding media. The maximum temperature rise of grinding media occurs at a range from 70% to 80% of critical speed. The maximum average temperature of grinding media up to 295.057 K appears at the grinding media filling 25% and the number of lifters 12. Subsequently, validation experiments are carried out to validate the numerical simulation results. The experimental results are closer to the simulation results, indicating the reasonability of the heat transfer model.
Image Recognition of Sports Athletes’ High-Intensity Sports Injuries Based on Binocular Stereo Vision
Authors: Chen Dongdong
Published: July 2022

Sports athletes are prone to certain injuries during high-intensity exercise training. In the process of treating an injury, images of the injury site need to be collected and identified. However, the traditional recognition method cannot effectively extract the features of the image. At the same time, it ignores the optimization of the damage image recognition results, resulting in low recognition accuracy and poor efficiency. Binocular stereo vision technology can quickly and accurately detect moving objects. Therefore, in order to more accurately identify high-intensity sports injury images, this study takes the high-intensity sports injury images as the basic research object. Several processes of image processing based on binocular stereo vision are analyzed, and the vulnerable parts of the body in high-intensity sports are also studied. Finally, the method in this study is verified. The experimental results show that the method proposed in this study reduces the average error rate by 0.19% compared with the traditional recognition method. It can effectively identify and detect injury images, thereby improving the accuracy and stability of sports injury image identification. The  identification time is also shortened accordingly, which has certain practicability and feasibility. In addition, the binocular camera used in this study has high accuracy, and the obtained images of sports injuries are of good quality, which lays a foundation for image detection and recognition.
Application Research of Fuzzy Control Comprehensive Model in Agricultural Economic Management
Authors: Huang Juan , Guo Ru , Wen Xiaosen
Published: July 2022

Agriculture is the basic industry of the national economy, and the transformation of agricultural economic growth mode is of great significance to ensure the supply of agricultural products and improve the quality of agricultural products. Therefore, it is the only way to solve this problem to change the way of agricultural economic growth through scientific and technological investment, industrial structure adjustment, and other methods. Based on the fuzzy comprehensive evaluation method, this paper analyzes various factors of agricultural economic development and establishes a multilevel fuzzy comprehensive evaluation model of the development status of agricultural economic system. The stochastic frontier production function model of the fuzzy control comprehensive model is applied to decompose the agricultural labor productivity, and the counterfactual thinking and distribution method are used to analyze the influence of these factors on the evolution of provincial agricultural labor productivity differences. The value inequality decomposition method measures the contribution of each factor to the provincial agricultural labor productivity difference as well as the contribution of exogenous explanatory variables to the provincial agricultural technical efficiency difference. It is concluded that the relative contribution rate of nonagricultural activities to the difference in agricultural technical efficiency is the largest, and the average relative contribution rate reaches 74%. The improvement of agricultural labor productivity cannot rely solely on the increase in the input of agricultural material factors, but should rely on science and technology.
Removal of Chromium (VI) and Lead (II) from Aqueous Solution Using Domestic Rice Husk Ash- (RHA-) Based Zeolite Faujasite
Authors: Chanda Rajesh , Hosain Mukter , Sumi Sumaia Aktar , Sultana Marzia , Islam Saiful , Biswas Biplob Kumar
Published: July 2022

Rice husk ash (RHA), is a widely available biobased source for high purity silica. In this work, zeolite Faujasite (FAU) is synthesized using extracted silica from RHA (collected from a local region of Bangladesh). The synthesized zeolite FAU was used as an adsorbent for Cr(VI) and Pb (II) batch-wise adsorptive removal from respective aqueous solution. The synthesized zeolite FAU was characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), nitrogen-sorption, and Fourier transfer infrared (FT-IR) spectroscopy. Metal ion adsorption studies were performed by varying metal concentration (20–100 mg/L for Cr(VI) and 900–133 mg/L for Pb(II)), sorbent dosage (2–10 g/L for chromium and 0.5–1.5 g/L for lead), and contact time (10–120 min for both metal ions). The maximum adsorption capacity of RHA-based zeolite FAU was found to be 3.56 mg/g and 342.16 mg/g for Cr(VI) and Pb(II), respectively. Since the sorption data was found to match with Langmuir isotherm, a monolayer adsorption was occurred. The regeneration of the RHA-based zeolite FAU by NaCl solution showed the potential of repeated as well as continuous operation.
Dual Solution with Heat Transfer through Moving Porous Plate of an Unsteady Incompressible Viscous Fluid
Authors: Abbas Tasawar , Majeed Aaqib , Ahmad Bilal , Noveel Sadiq Muhammad , Ul Hassan Qazi Mahmood , Ullah Hakeem , Al-Mekhlafi Seham M.
Published: July 2022

We examine graphically the behavior of the dual solution of an unsteady incompressible viscous flow with heat transfer due to a moving porous surface. The mathematical model is developed with the implementation of conservation laws which are then tackled numerically, and the simulations are performed through the Keller–Box technique. Application of the Keller–Box technique allows the non-linear differential equations to transform to a scheme of linear differential equations which are easier to illuminate. Outcomes of velocity and thermal behavior are shown graphically for certain values of the dimensionless parameters. It is obvious from the results that temperature of fluid decreases with the increasing values of dimensionless Prandtl number Pr, whereas the velocity enhances for blowing situations.
Dynamic Optimization Method of Wireless Network Routing Based on Deep Learning Strategy
Authors: Wang Pengjun , Qin Jiahao , Li Jiucheng , Wu Meng , Zhou Shan , Feng Le
Published: July 2022

Wireless sensor network is a network that integrates sensor technology, computer technology, information processing technology, and communication technology. This paper aims to study how to analyze and study the routing optimization of wireless sensor network based on deep learning and describe the neural network. This paper puts forward the problem of routing optimization, which is based on the dynamic programming of wireless sensor network, and then elaborates around its concept and related algorithms and designs and analyzes the case of wireless sensor network optimization. Through the comparative analysis of the five algorithms in computer simulation, although the average network delay performance of DPER reached 0.47 s, it could effectively prolong the life cycle of the network. The DPER algorithm not only improves the network life but also improves the network energy utilization rate, shortens the average path length of the network, and reduces the standard deviation of the remaining energy of the node.
Fault Identification of Low-Speed Hub Bearing of Crane Based on MBMD and BP Neural Network
Authors: Guo Li-Hong , Yang Lai-Ming , Peng Yan-Feng , Guo Yong
Published: July 2022

As the key bearing part of the crane, the low-speed hub bearing of the crane exists in special working conditions of low-speed and alternating heavy load. It is difficult to extract its fault characteristics accurately by existing analysis methods. The main idea of the broadband mode decomposition (BMD) method previously proposed is to search in the association dictionary library containing broadband and narrowband signals. However, when it is applied to the broadband signals interfered by strong noise, the decomposition is easy to produce modal confusion, so the modulated broadband mode decomposition (MBMD) method is proposed. The fault signal just can be analyzed by MBMD, so it is applied to the fault diagnosis of low-speed hub bearing of the crane. To realize the fault identification of low-speed hub bearing of the crane, firstly, the original signal is decomposed by MBMD. Secondly, the eigenvalues of the first three-component signals are calculated, the eigenvalue matrix is constructed, and the marked features are selected by the distance evaluation technique (DET). Finally, the marked features are input into BP neural network for training and testing to identify the types of bearing fault. Compared with EEMD, VMD, and BMD, the MBMD method combined with BP neural network has good performance in feature extraction and fault identification.
Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions
Authors: Bhende Manisha , Thakare Anuradha , Pant Bhasker , Singhal Piyush , Shinde Swati , Dugbakie Betty Nokobi
Published: July 2022

With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.
Exact Solutions of Three Types of Conformable Fractional-Order Partial Differential Equations
Authors: Ma Lijun
Published: July 2022

Fractional calculus is widely used in biology, control systems, and engineering, so it has been highly valued by scientists. Fractional differential equations are considered an important mathematical model that is widely used in science and technology to describe physical phenomena more accurately in terms of time memory and spatial interactions. The study of exact solutions of fractional differential equations helps to understand these complex physical phenomena and dynamic processes. The results show that the method is simple and clear. In addition, with the help of MAPL, we provide some 3D maps with accurate solutions.
Identification of Hub Genes and Key Pathways Associated with Follicular Lymphoma
Authors: Zhang Qing , Wang Meng
Published: July 2022

Follicular lymphoma (FL) is the second most prevalent form of non‐Hodgkin lymphoma (NHL) and accounts for almost 20% of all NHL cases. Although FL patients’ overall survival rates have steadily increased, there is still no accepted standard of care for individuals who experience recurrence or resistance to treatment. Hence, it is needed to evaluate the precise molecular cascades underlying FL to develop efficient diagnostic and treatment approaches. Herein, we aimed to evaluate variations in gene expression profiles, explore the underlying mechanisms, and find new FL targets. In the present study, Gene Expression Omnibus (GEO) database was employed to evaluate microarray datasets including GSE32018 and GSE55267. R software was employed to evaluate differentially expressed genes (DEGs) between FL and noncancer samples. The DEGs were evaluated using GO, KEGG pathway enrichment analysis, and PPI network to evaluate hub genes, which were then, examined using gene function enrichment analysis. According to the obtained results, a total of 190 upregulated and 162 downregulated DEGs were evaluated. Following the generation of PPI networks, 15 hub genes in highly connected upregulated DEGs were selected including FN1, MMP9, CCL2, CD8A, POSTN, CCR5, COL3A1, CXCL12, VCAM1, COL1A2, CCL5, SPARC, TIMP1, CXCL9, and IL18. The GO enrichment evaluation of the underlined hub genes indicated that the immunological response was the most considerably enriched term. Twelve significant cascades were found using the KEGG pathway analysis, most of which were linked to cellular structure and immunity. Our findings suggested that FN1, SPARC, POSTN, MMP9, and VCAM1 genes are potential biomarkers of FL, and cellular immunity contributes to the pathogenesis of FL. Moreover, the unique DEGs and cascades found in the present study may present new perspectives on the molecular basis of FL’s underlying mechanisms as well as a new understanding of FL’s future precise management.
[Retracted] Radiation Exposure of Crystalline Bodies during Interventional Therapy and Related Research Status
Authors: Chen Yahong , Zhao Dong , Zhong Hongwei , Zhang Yiheng , An Donghong
Published: July 2022

The aim of this study was to estimate the exposure dose level of ocular corpuscles during the common interventional procedures of interventional radiology workers. This article retrospectively analyzed the general instruments and equipment of interventional therapy, the general methods and steps of interventional therapy, and the influence of X‐ray of interventional surgery. The related contents of eye protection for interventional radiology workers were summarized. This study found that there was a significant linear correlation between the equivalent dose of lens and the exposure time. The tube voltage, tube current, and the size of fluoroscopic field of vision also have an impact on the radiation of workers during surgical exposure. Interventional surgery is difficult and easy. The exposure time of difficult surgery is relatively long, so the dose received by interventional staff is large. The intervention staff should improve their professional knowledge, be proficient in the operation technology, and try to reduce the exposure without affecting the diagnosis and treatment effect. During the intervention operation, the eye substance of the operator is exposed to a certain dose of radiation, and the wearing rate of protective lead glasses of the intervention staff is very low. Therefore, we must pay attention to the protection of the eyes, strengthen the protection management, and reduce the exposure dose of the intervention staff as much as possible.
Process and Product Characterization of Aliha, A Maize-Based Ghanaian Indigenous Fermented Beverage
Authors: Madilo Felix Kwashie , Kunadu Angela Parry-Hanson , Tano-Debrah Kwaku , Mensah Gloria Ivy , Saalia Kwesi Firibu , Kolanisi Unathi
Published: July 2022

Aliha is a maize-based traditional fermented beverage prepared and consumed in Ghana, predominantly in the Volta Region and other parts of Ghana. The study sought to characterize the production processes, the nutritional values, and microbial composition of aliha. A total of 126 aliha producers in the Volta, Greater Accra, and Ashanti Regions were sampled using snowballing to identify and to recruit the producers for the study, using a pretested self-administered questionnaire. The physicochemical and microbial composition were carried out using standard methods. Four different production techniques were identified across the production sites. The variations identified during the production existed across the production chain. The main ingredients used for aliha production are corn, caramel, sugar, and water. However, aliha produced by the ‘original’ method (DN2) presented the best nutritional values (proteins, energy, and calcium), followed by backslopping techniques, AG1 (total carbohydrates and ash), and AG2 (fats and oils and phosphorus). Fungi and Enterobacteriaceae dominated the initial fermentation stages (24 h) with low acid values. However, as the fermentation time increased from 24 h to 72 h, the acid contents of the fermenting beverage increased sharply leading to a drastic reduction of fungi and Enterobacteriaceae contents with increasing records of lactic acid bacterial counts. Even though DN2 presented the best nutritional values, it was highly contaminated. Hence, the producers must be encouraged to use backslopping techniques for safety and to shorten the duration of production.
Control of Material Procurement Cost of Enterprises under the Background of the Low-Carbon Economy
Authors: Ma Yan
Published: July 2022

With the increasing prominence of global warming and serious resource consumption, the low-carbon economy has become a new growth point for the world economy and is widely recognized by all sectors of society. Purchasing cost control management is generally regarded as an applied management discipline with important theoretical, operational, time, and management roles. Due to China’s rapid economic development, high energy consumption, heavy pollution, and high emissions have resulted in catastrophic environmental damage. In recent years, the public’s awareness of environmental protection has increased, and the requirements for enterprises to reduce pollution and energy consumption have increased, and environmental issues have become more and more important in China’s economic development strategy. Based on the background of a low-carbon economy, this article studies the energy consumption of company’s buildings and weights the suppliers of building raw materials. The maximum value, CI value, and CR value of the quality factor were 7.3379, 0.0566, and 0.0429, respectively.
Residual Stress Distribution in Selective Laser Melting of SS316L Parts
Authors: Vemanaboina Harinadh , Ferro Paolo , Babu B. Sridhar , Gundabattini Edison , Kumar Kaushik , Berto Filippo
Published: July 2022

Additive manufacturing is one of the fastest-growing fields in materials engineering. This is because there is a new trend for custom, high-precision, and on-demand manufacturing. The undesired residual stress induced in the components during layer-by-layer melting and solidification of the metal powder is an important issue related to the selective laser melting (SLM) process that needs to be studied deeply. These stresses may impair mechanical performance and potentially result in premature failure. As a result, a thorough knowledge of residual stress is crucial for improved component dependability. By keeping constant process parameters, samples were produced with a difference in the scanning method. Results indicate that the defect-free parts are manufactured in all the four patterns used, and the self-balanced residual stresses are within the safe limits of yield strength.
Advanced Artificial Intelligence Model for Financial Accounting Transformation Based on Machine Learning and Enterprise Unstructured Text Data
Authors: Zheng Minyi
Published: July 2022

Machine learning belongs to the science of artificial intelligence, so its main exploration goal is artificial intelligence, mainly to accumulate experience and improve the relevant performance of the algorithm. ML is a complex discipline that learns and improves skills primarily through imitation. Unstructured text data refer to unstructured data in the form of text. Financial accounting is a basic work of an enterprise, mainly to provide decision-making reference information for enterprise managers to ensure the normal operation of the enterprise. Financial accounting is a management activity that monitors the business and provides relevant information to the relevant authorities. This paper aims to study the advanced artificial intelligence model of financial accounting transformation based on machine learning and enterprise unstructured text data, expecting to use machine learning and unstructured text technology to upgrade the management of financial accounting and improve its analysis level. In this paper, an improved KMP algorithm is proposed for the keyword matching problem of unstructured data. Then, the data crawling technology of unstructured text is studied. It mainly uses the crawler technology based on the Python language and obtains a large amount of information on the Internet by formulating appropriate regular expressions. The algorithm is used to mine the data set and generate frequent itemsets. The association rules mining algorithm is proposed and implemented, and the association rules with practical reference value are obtained. The test leads in this document indicate that the return on assets of the enterprise is −11.2%, the net interest rate on equity is −44.5%, and the business profit rate is −12.1%. This shows that the profitability of the company has been declining in recent years, and there is even the risk of bankruptcy.
Analysis of Green Financial Policy Utility: A Policy Incentive Financial Mechanism Based on State Space Model Theory Algorithm
Authors: Wan Qilong , Qian Jine , Yu Maomao
Published: July 2022

In recent years, in the context of “double carbon” and innovation-driven synthesis, the volume of green finance has been growing year by year, and the intensity of environmental regulation has been stabilizing. As green financial technology innovation cannot be separated from the support of financial market and government policies, how to promote green financial technology innovation with green finance and environmental regulation has become a hot issue. How to control the appropriate strength of environmental regulations to promote green financial technology innovation is a matter of continuous exploration by local governments. The research of this paper is about the utility analysis of green finance policy: a policy incentive financial mechanism based on the state space model theory algorithm. Therefore, this paper introduces the theory of green finance based on the state space model algorithm and neural network model algorithm to study China’s green finance policy incentive mechanism, profoundly study the current situation of domestic green finance development, and put forward further strengthen the leading role of the government in green financial innovation. At the same time, suggestions for achieving coordinated regional development were made in terms of giving full play to the role of financial markets in promoting green technology innovation.
Intelligent Analysis of Logistics Information Based on Network Dynamic Data
Authors: Yan Honglin
Published: July 2022

Based on the concept of market sharing, the author has developed Internet technology-based logistics distribution data exchange to control the cost of shipping to benefit the transfer for new types of management and operations. Investigate the needs of export shipments, such as external management responsibilities, requirements of management responsibilities, data recovery, and data recovery requirements, based on the current state of the shared platform model integration, and determine the business model. Based on this, three types of IoT cloud, such as single-center multiterminal, multicenter multiterminal, and data and application layer, can be completed and the Internet of Things can be defined by education. About the use of the cost of distribution, the transportation contract is to identify the needs of Internet technology-based logistics distributions and complete the construction of Internet technology-based logistics distribution information exchange platform. The results of the experiment showed that the results recorded in the test group did not differ from the actual results only twice, the level of difference was small, and the mean error was less than 1%. In terms of logistics distribution exposure, the average level of the assessment group is significantly higher than that of the management group, with a minimum collection rate of 90.07%, while the minimum value of the control group is as low as 76.32%. Compared with blockchain-based integration, Internet of Things technology-enabled sharing platforms can capture real-time data, transfer data, monitor shipping costs, and improve management and operations.
Simulation of Transmission System of Crawler Self-propelled Rotary Tiller Based on Deep Learning
Authors: Wu Luofa , Wu Yanqi
Published: July 2022

Because of its good performance, crawler-type running gear plays a very important role in the fields of modern agriculture. This article aims to study the construction of the drive system of the crawler self-propelled rotary tiller with the deep learning network and carry out the system simulation experiment. In this article, deep learning-related algorithms, auto-encoding networks, convolutional neural networks, and structural design of crawler self-propelled rotary tillers are proposed. It then used the self-developed crawler-type rotary tiller and straw paddle machine to compare the field operation performance with the combination of ordinary wheeled tractors and rotary tillers. The experimental results show that the tillage performance indicators such as the working depth, tillage depth stability, ground flatness, stubble pressing depth, and vegetation coverage qualification rate of the “crawler self-propelled tractor + straw stubble pulper” are better than those of “wheel tractor + ordinary rotary tiller” and “crawler tractor + ordinary rotary tiller,” increased by 9.92% and 4.88%, 4.31% and 4.13%, 42.59% and 19.12%, 40.15% and 34.57%, and 13.04% and 7.16%, respectively. The mechanical transplanting index was significantly better than other treatments. The yield increase effect of the field test is remarkable, with the average yield increase rate of 9.63% and 4.57%, which is suitable for popularization and application in the southern double-cropping rice area.
Meta-Study of the Clinical Effect of Conservative Treatment in Uterine Fibroids
Authors: Zhu Hanxiao , Lai Xiaoli , Wu Jinhong , Guan Chenan , Yu Junhui
Published: July 2022

Heavy menstrual bleeding (HMB), distress in the pelvis, infertile, and stressed feelings are all indications of fibroids in the uterus, the most prevalent type of benign uterine tumor. Nearly one-third of women with fibroid in the uterus seek medical help. The goal of this analysis is for a better understanding of the mechanisms that relate fibroids to these symptoms and to assess several treatment options, including the application of the gonadotropin-releasing hormone (GnRH) antagonist. We compiled the commonest as well as appropriate studies on the most common symptom of fibroids, as well as medicinal and surgical treatment options. Those who said they used GnRH antagonists orally were probed further. The underlying mechanisms myoma-caused menorrhagia as well as sterility were examined since those have been critical to understand the detailed mechanism as well as the targeted treatment modality. New treatments are determined by the amount, dimension cum localization of fibroids, and the women’s age and also her choice on future childbirth. Myomas have considerable economic consequences with respect to direct expenditure, wage losses, as well as difficulties. In this context, medical, surgical, and nonsurgical techniques were examined. The novelty applied in this research article is the implementation of the GnRH antagonist-based methodology for the removal of fibroids in the uterine layer. The methodology is superior to the existing techniques for the treatment of fibroids in the uterine membrane. Novel medical techniques including GnRH antagonists were investigated and proved to be a viable new option. Alternatives to surgical-surgical modalities are desperately needed, specifically for those who are looking forward for future childbirth. GnRH antagonists have been shown to effectively alleviate the symptoms of fibroids and welcome new techniques for myoma treatment.
Real-Time Testing of Synchrophasor-Based Wide-Area Monitoring System Applications Acknowledging the Potential Use of a Prototyping Software Toolchain
Authors: Kumar Lalit , Ahmed Shehab , Vanfretti Luigi , Kishor Nand
Published: July 2022

This article presents a study on real-time testing of synchrophasor-based “wide-area monitoring system’s applications (WAMS application).” Considering the growing demand of real-time testing of “wide-area monitoring, protection, and control (WAMPAC)” applications, a systematic real-time testing methodology is formulated and delineated in diagrams. The diagrams propose several stages through which an application needs to be assessed (sequentially) for its acceptance prior to implementation into a production system. However, only one stage is demonstrated in this article which comprises the use of a prototyping software toolchain and whose potential is assessed as sufficient for preliminary real-time testing (PRTT) of WAMS applications. The software toolchain is composed of two components: the MATLAB software for application prototyping and other open-source software that allows ingesting prerecorded phasor measurement unit (PMU) signals. With this software toolchain, a PRTT study is presented for two WAMS applications: “testing of the PMU/phasor data concentrator (PDC)” and “testing of wide-area forced oscillation (FO) monitoring application.”
Medical Health Data‐Driven Physical Education Scheme: Public Environment‐Oriented Exercise Health Management
Authors: Yue Nan
Published: July 2022

Smart wearable devices can encourage users to take an active part in exercise to a certain extent. The most important reason is that their entertainment function can bring users a better exercise experience, which is also one of the reasons why most people wear smart wearable devices during exercise. Another reason is that the medical and health data feedback of smart wearable devices can play a stimulating role, which is also the motivation of this study. Through the feedback of health data, students can perceive their own exercise situation, and teachers can plan more targeted exercise courses based on medical and health data, so as to improve the quality of physical education. This paper studies the origin and development of health data‐driven learning; clarifies the logical mechanism between health data‐driven learning and physical education; analyzes the internal needs of physical education design; discusses the characteristics of thinking, process, tools, and other elements of health data‐driven learning in the new era of physical education reform; and carries out physical education teaching practice. Experiments have proved that health data‐driven physical education is beneficial for improving students’ emotional attitude and values, optimizing learning process and methods, and strengthening knowledge literacy and exercise skills. However, in today’s poor public environment, the medical and health data‐driven physical education scheme proposed in this paper is also applicable to the public‐oriented exercise health management. Medical Health; Public Environment; Health Management; Data Analysis.
Construction of a Knowledge Map Based on Text CNN Algorithm for Maritime English Subjects
Authors: Wang Hui , Wei Aimin
Published: July 2022

Knowledge map is a new method of knowledge management with the information revolution. This paper is aimed at forming a systematic and standardized huge redundant knowledge structure, which can be used to mine the knowledge structure and the relationship between knowledge and visualize it in a graphical way, in order to obtain more representative information and improve the classification accuracy of text classification model. In this paper, a knowledge map construction method based on the Text CNN algorithm is proposed for the subject of Nautical English. It is of practical significance and academic value to make use of knowledge map to study Chinese Maritime English, which is helpful to the development of Chinese Maritime English and provides guidance. In order to maintain the diversity of particle swarm optimization, the Text CNN algorithm is combined with the construction of Maritime English subject knowledge map, and the network parameters and structure are optimized. Using knowledge map to study China Maritime English has important practical significance and academic value and has certain guiding significance for the development of China Maritime English.
MITF-Mediated lncRNA CCDC183-As1 Promotes the Tumorigenic Properties and Aerobic Glycolysis of Bladder Cancer via Upregulating TCF7L2
Authors: Cai Wei , Wei Meng , Su Zhen
Published: July 2022

As a primary malignancy tumor of the urology system, bladder cancer (BC) is characterized by its high recurrence and metastasis characteristics. Despite the great improvement in clinical interventions over the past decades, the outcomes of BC patients are still unsatisfactory. Novel molecular mechanisms for developing effective diagnostic and therapeutic strategies are urgently needed; therefore, we screened the lncRNA expression profile in four pairs of BC tissues, showing that CCDC183-AS1 was the most upregulated lncRNA. Subsequently, results of CCK-8, EdU, Transwell, and aerobic glycolysis detection showed that CCDC183-AS1 plays an oncogene role in BC progression. Furthermore, an investigation of the downstream and upstream factors of CCDC183-AS1 identified a novel MITF/CCDC183-AS1/miR-4731-5p/TCF7L2 axis in BC progression, which might furnish novel insights for developing effective diagnostic and therapeutic strategies for BC.
Postcorona Pandemic Educational Science Research in Saudi Universities
Authors: Altwijri Areej Mohammed , Ali AlHothali Huda Muttar
Published: July 2022

The present study attempted to determine the actuality of scientific research in educational sciences from the perspective of Saudi faculty after the coronavirus disease 2019 pandemic (COVID-19). The study also revealed the obstacles facing academic science research postpandemic. The study predicted the future of educational science research in Saudi universities postpandemic. The study employed a descriptive survey method on 255 educational science faculty members. The study’s most important outcome is that scientific research is a precondition for professor promotions. Universities (the focus of the study) have a scientific research system and faculty with research skills. The biggest problem for scientific research in educational sciences in Saudi universities during the pandemic was that faculty members were too busy with teaching and administrative tasks to focus on research. Both experts predicted the future of educational sciences research at Saudi universities postpandemic. The researchers recommend that universities offer the infrastructure to facilitate scientific research, minimize teaching loads and administrative work, and increase the relationship between “Research Centers” and “Deanships of Scientific Research.”
Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection
Authors: Zhang Xinghua , Sun Yongjie , Sun Yongxin
Published: July 2022

Food safety is a major concern that has an impact on the national economy and people’s lives. The food industry has grown in quality and innovation in tandem with the rapid development of the economy and society. The emergence of new food technologies, as well as changes in dietary habits, has increased public concern about food safety. With the emergence of various counterfeit and substandard products, food quality and safety testing have become even more important. Traditional testing methods rely on sensory analysis and physical and chemical analysis. This approach is subjective and poorly adapted to the general public. It requires a high level of technical operation and is difficult to carry out on a large scale. To address this situation, this paper proposes an intelligent approach to food safety quality testing. The core idea is, first, to use sensors to collect data on the various components of the sample to be tested. Second, the random forest (RF) model used in this paper is trained. Third, the trained model is used to classify and identify the test samples. Based on the classification results, a conclusion is drawn as to whether the food product is a variant or a counterfeit. The advantage of this study is that the training model used is a weighted RF algorithm based on mutual information. The correlation between any two decision trees is calculated using mutual information, and for the more correlated decision trees, only the one with the highest evaluation accuracy is retained to form a new RF, and the evaluation accuracy is converted into voting weights, resulting in an RF model with less redundancy and higher evaluation accuracy. The experimental results show that the method used in this paper can successfully identify spoiled or counterfeit products and has good practicality.
Development and Teaching Application of Interactive Virtual Algorithm Animation of C Language Program
Authors: Wan Lei , Cao Yongli , Shi Lili , An Ying
Published: July 2022

In today’s world, almost every domain has certain and mandatory requirements to understand at least one of the programming languages which is defined as a way of interaction with a computer to resolve real world problems or more specifically for the implementation of algorithms. Language programming is a course with strong theory and practice. In view of the fact that C language beginners in colleges and universities generally report that it is difficult to learn well due to various reasons. Therefore, in this paper, we have discussed the development and teaching of interactive virtual animation of C language programs. The proposed approach relies on a typical recursive algorithm case to analyze the teaching strategy of virtual algorithm animation brought into the classroom, the application in the algorithm teaching of C language and the teaching strategy in improving students’ understanding of abstract knowledge using computer C language program interactive virtual animation development and implementation of the teaching system is the research content. According to the basic flow of the interactive virtual algorithm of the C language program, a reasonable system structure design is carried out.
Effect of SWOT Analysis Combined with the Medical and Nursing Integration Emergency Nursing Process on Emergency Treatment Efficiency and Prognosis of Patients with Acute Myocardial Infarction
Authors: Wu Cuihuan , Wu Ling , Jin Pan
Published: July 2022

Acute myocardial infarction (AMI) is a common clinical emergency. Effective emergency treatment at the early stage of onset can effectively reduce the mortality rate. Time is the key of emergency treatment, which is directly related to the treatment effect and the prognosis of patients, and clinical intensive nursing intervention for emergency treatment is of great significance in improving the efficiency of emergency treatment and prognosis. In this study, the effects of routine emergency care flow and SWOT analysis combined with medical and nursing integration on emergency treatment efficiency and prognosis of patients with acute myocardial infarction were compared. The results showed that the combined scheme could improve the rescue effect and success rate of patients with acute myocardial infarction, shorten the rescue time, and reduce the mortality and complication rate of myocardial infarction, which provided a new direction for clinical emergency treatment of acute myocardial infarction.
Clinical Significance of Serum Biomarkers in Stage IV Non-Small-Cell Lung Cancer Treated with PD-1 Inhibitors: LIPI Score, NLR, dNLR, LMR, and PAB
Authors: Chen Jia , Wei Sheng , Zhao Tianye , Zhang Xunlei , Wang Yilang , Zhang Xiaodong
Published: July 2022

Background. To assess the prognostic value of pretreatment serum biomarkers in stage IV non-small-cell lung cancer (NSCLC) patients treated with PD-1 (programmed cell death protein 1) inhibitors and their value as a predictor of benefit. Methods. We performed a retrospective study including patients with stage IV NSCLC who were treated with anti-PD-1 drugs in first or advanced lines of therapy in the Affiliated Tumor Hospital of Nantong University. Serum biomarkers such as NLR, dNLR, LMR, PAB, ALB, and LIPI scores were calculated and analyzed in detail. Results. A total of 85 patients with stage IV NSCLC treated with PD-1 inhibitors in the first or advanced lines of therapy were included in this subject. According to the tumor response of PD-1-based treatment, ORR was 42.4% (36/85) and DCR was 68.2% (58/85). The median OS and PFS were 20.0 months and 7.0 months, respectively. The ROC curves showed that the serum biomarkers of NLR, dNLR, LDH, LMR, PAB, and ALB were significantly associated with overall survival and helped to determine the cut-off value. The multivariate Cox proportional hazard analyses for stage IV NSCLC patients treated with PD-1 inhibitors indicated that dNLR (

P
<
0.001

) and ALB (

P
=
0.033

) were independent prognostic indicators of PFS, while liver metastasis (

P
=
0.01

), NLR (

P
=
0.01

), dNLR (

P
=
0.001

), and LMR (

P
=
0.006

) were independent prognostic indicators of OS. Moreover, patients of the good LIPI group showed prolonged PFS and OS than those with intermediate/poor LIPI score (

P
<
0.001

and

P
=
0.006

, respectively). Conclusions. Pretreatment dNLR is an independent prognostic indicator of both PFS and OS in stage IV NSCLC patients treated with PD-1 inhibitors. Pretreatment LIPI, combining

dNLR
>
3

and LDH>ULN, was correlated with worse outcome for stage IV NSCLC patients treated with ICI. High NLR, high dNLR, low LMR, and low ALB at baseline might be useful as an early predictive biomarker of benefit.
Application of Neural Network Sample Training Algorithm in Economic Information Management System
Authors: Wang Xianmei
Published: July 2022

In order to make up for the shortcomings of traditional financial evaluation, this paper proposes a method of enterprise comprehensive performance evaluation system based on the balanced scorecard method and BP artificial neural network model. This method focuses on the application methods and steps of BP artificial neural network model in enterprise comprehensive performance evaluation system. In addition, it also makes a detailed study on the initial weight and threshold assignment of the BP network, the selection of training samples, the determination of hidden layer, the convergence of network, and so on. The experimental results show that this method verifies the comprehensive performance evaluation results of 15 manufacturers, and the results are 4 excellent, 5 good, 3 average, 2 poor, and 1 very poor. This method effectively reduces the limitations of enterprise comprehensive performance evaluation by introducing nonfinancial evaluation indicators.
Antioxidant and Immunomodulatory Activities of Essential Oil Isolated from Anti-Upper Respiratory Tract Infection Formulation and Their Chemical Analysis
Authors: Fan Heming , Zhang Lin , Li Yue , Soo Khoo Cheang , Han Donghe , Liu Qingquan , Li Ping , Zhang Xian
Published: July 2022

This study evaluates the in vitro antioxidant and immunomodulation activities of essential oils isolated from an anti-upper respiratory tract infection (URTI) formulation with a view to their therapeutic potential. The chemical components of the essential oil were analysed by gas chromatography-mass spectrometry (GC-MS). The antioxidative activity of the oils was investigated with regard to their ability to scavenge DPPH●, ABTS●+, and hydroxyl free radical (•OH). Their immunostimulatory activities were determined using murine macrophage cells. The main components of the oil with pharmacological and biological activities include 1,8-eucalyptol (42.9%), patchouli alcohol (19.9%), trans-erinolide (9.2%), and guaiacol (5%). The oils displayed high DPPH, ABTS, and hydroxyl radical scavenging activities and anti-inflammatory activities by reducing tumour necrosis factor-α (TNF-α) and interleukin-6 (IL-6) production. The results indicate that essential oils have the potential to be used in products for anti-URTI treatment.