bluuebunny/crossref_metadata_2025 huggingface dataset:
[Retracted] Economic Growth Effect of Public Health Investment and Its Impact on Living Environment
Authors: Meng Jinglei
Published: May 2022

Based on the concern of Chinese residents for health and the improvement of related service demand, this paper studies the economic growth effect of public health investment and its impact on living environment by analyzing the determination coefficient estimated by Spearman and nonlinear curve. The results show that the impact of increasing government financial public health investment on regional economic growth rate is not only related to regional economic aggregate and development level, but also related to regional education level and regional publicity level in logical analysis. The improvement of residents’ living environment brought by public infrastructure investment can effectively stimulate residents’ concept of health consumption and then make the economy of nonpublic health undertakings achieve a higher growth rate. The economic growth rate brought by nonpublic health undertakings is also an important component of the local economic growth rate. It can be seen that strengthening the infrastructure construction of urban living environment can not only improve the overall living environment quality of the city, but also promote the development of the city and the development speed of the city.
Impact of Deacetylation Degree on Properties of Chitosan for Formation of Electrosprayed Nanoparticles
Authors: Nguyen Hien Thi-Thanh , Tran Tinh Ngoc , Ha Anh Cam , Huynh Phu Dai
Published: May 2022

Biopolymer of natural origin as chitosan has been studied and applied widely in practice. In the pharmaceutical field, especially, chitosan nanoparticles have been researched for a variety of drug delivery systems. There are many factors influencing the success of the chitosan nanoparticle delivery system. Therein, the specific parameters to the physicochemical nature of chitosan greatly determine the efficiency of its drugs carrier. The degree of deacetylation (DD) of chitosan is one of those parameters. In this study, the influence of DD on chitosan properties was clarified to facilitate the preparation of nanoparticles by the electrospraying method. DD can affect the solubility, crystallinity, and surface tension of chitosan, but it cannot strongly impact the viscosity of chitosan solution as much as the molecular weight (Mv). From these results, M3 chitosan, owning a high DD of 86.70%, and crystalline index of 44%, was dissolved in acetic acid for the collection of electrosprayed nanoparticles. The M3 solution having low viscosity of under 50 mm2/s displayed the easy adjustment of the stable Taylor-cone droplet at the nozzle tip. Particularly, the M3 chitosan solution with a concentration of 1.5 wt.% in acetic acid of 90 wt.% concentration operated at the working condition of 12 kV voltage, a distance between the two electrodes of 10 cm created spherical particles with an average diameter of 338 nm, narrow size distribution. These chitosan nanoparticles can obtain the initial requirement for application as injectable drugs carrier.
Semantic Segmentation-Based Adaptive Mining Algorithm for Ceramic Cultural Resource Data
Authors: Li Jiang
Published: May 2022

Ceramic culture as Chinese culture has a long history and is the Chinese people’s spiritual home. The ceramic culture resource base contains a large number of images, videos, and other resources, so the collection of image category of data mining, as well as the finishing processing, is very critical. Thousands of years of ceramic data accumulation and impatient information demand have created a new point of contradiction for ceramic cultural resources. Therefore, in order to address this issue, we carried out a research study based on the concept of big data mining of ceramic cultural resource data, which is based on data fusion and feature extraction methods. We also considered semantic segmentation processing methods, which are used for data information management, scheduling, identification, collection, statistics, and aggregation of heterogeneous ceramic cultural big data. Further, a fine mining method for ceramic culture big data based on semantic segmentation is also proposed. As a result, the distributed storage of information and detection capability of ceramic cultural resource data are improved. The experimental results reveal that the proposed method performed better than the earlier approaches.
Deoxyschizandrin Inhibits the Proliferation, Migration, and Invasion of Bladder Cancer Cells through ALOX5 Regulating PI3K-AKT Signaling Pathway
Authors: Chi Baojin , Sun Yao , Zhao Jintao , Guo Yugang
Published: May 2022

Objective. Deoxyschizandrin has a significant inhibitory effect on a variety of tumor cells. However, the effect of Deoxyschizandrin on bladder cancer cells and its mechanism are still unclear. Methods. Bladder cancer cells were treated with different concentrations of Deoxyschizandrin for 24 h, 48 h, and 72 h. The inhibition rate of cell proliferation was detected by CCK-8 assay. The changes of cell migration and invasion were detected by wound healing and Transwell assay. Based on the structure of Deoxyschizandrin, the protein targets of Deoxyschizandrin were predicted by bioinformatics database and verified by RNA and protein. Then, the expressions of ALOX5 and PI3K-AKT signaling pathway proteins were detected by Western blot in bladder cancer cells treated with Deoxyschizandrin. Result. Deoxyschizandrin inhibited the proliferation, migration, and invasion of bladder cancer cells in a time- and concentration-dependent manner. Bioinformatics analysis showed that Deoxyschizandrin had 100 protein targets; among them, the score of ALOX5 was the highest, and the mRNA and protein levels of ALOX5 decreased after treatment with different concentrations of Deoxyschizandrin. Western blot results showed that compared with the control group, Deoxyschizandrin could significantly reduce the expression of p-PI3K and p-AKT, and overexpression of ALOX5 could significantly enhance the expression of p-PI3K and p-AKT. Compared with Deoxyschizandrin or overexpression of ALOX5, the expression of p-PI3K and p-AKT of Deoxyschizandrin combined with overexpression of ALOX5 recovered. Conclusion. Deoxyschizandrin inhibits the proliferation, migration, and invasion of bladder cancer cells through ALOX5 regulating PI3K-AKT signaling pathway.
Cloud‐Based Framework for COVID‐19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine
Authors: Rehman Khan Amjad , Saba Tanzila , Sadad Tariq , Hong Seng-phil
Published: May 2022

Background. Cloud‐based environment for machine learning plays a vital role in medical imaging analysis and predominantly for the people residing in rural areas where health facilities are insufficient. Diagnosis of COVID‐19 based on machine learning with cloud computing act to assist radiologists and support telehealth services for remote diagnostics during this pandemic. Methods. In the proposed computer‐aided diagnosis (CAD) system, the balance contrast enhancement technique (BCET) is utilized to enhance the chest X‐ray images. Textural and shape‐based features are extracted from the preprocessed X‐ray images, and the fusion of these features generates the final feature vector. The gain ratio is applied for feature selection to remove insignificant features. An extreme learning machine (ELM) is a neural network modification with a high capability for pattern recognition and classification problems for COVID‐19 detection. Results. However, to further improve the accuracy of ELM, we proposed bootstrap aggregated extreme learning machine (BA‐ELM). The proposed cloud‐based model is evaluated on a benchmark dataset COVID‐Xray‐5k dataset. We choose 504 (after data augmentation) and 100 images of COVID‐19 for training and testing, respectively. Conclusion. Finally, 2000 and 1000 images are selected from the non‐COVID‐19 category for training and testing. The model achieved an average accuracy of 95.7%.
Innovating Pedagogical Practices for Handmade Courses in Preschool Education Using Artificial Intelligence
Authors: Zhao Jun , Wang Na
Published: May 2022

Handmade is an important part of preschool education, which was aimed at improving children’s ability to work with their hands. Preschool education is the most basic and important aspect of a country’s educational system. As a result, individuals pursuing a degree in preschool education take on a lot of responsibility. The preschool education handmade course has become an important component of preschool education due to its practicality and creativity. Preschool education major offers classes in traditional crafts such as paper cutting, paper dyeing, origami, paper three-dimensional modeling, and ornamental painting. The teaching methods for custom-made preschool education courses are always evolving with the progress of society. The question of how to assess the efficacy of unique teaching methodologies for handmade courses has become crucial. This study employs artificial intelligence to create a neural network for assessing the creativity of teaching approaches for handmade courses in preschool education. The following is the specific work: Firstly, the idea, as well as the benefits and drawbacks of genetic algorithms, is investigated. To build an improved genetic algorithm (IGA), the chromosome encoding, fitness function, and three operation options are enhanced. Secondly, by improving the genetic algorithm, the selection of weights and thresholds in the BP neural network model is improved, and a combined model (IGA-BP) is created by integrating the improved genetic algorithm with the BP network. Finally, rigorous and systematic tests confirm the work’s efficacy and viability.
Role of Neuron-Specific Enolase in the Diagnosis and Disease Monitoring of Sarcoidosis
Authors: Sunaga Noriaki , Koga Yasuhiko , Hachisu Yoshimasa , Yamaguchi Koichi , Aikawa Masaki , Kasahara Norimitsu , Miura Yosuke , Tsurumaki Hiroaki , Yatomi Masakiyo , Sakurai Reiko , Maeno Toshitaka , Hisada Takeshi
Published: May 2022

Sarcoidosis is a systemic granulomatous disease of unknown etiology. The diagnosis of sarcoidosis is based on clinicopathologic findings accompanied by the formation of granulomas in multiple organs, including the lung. Although angiotensin-converting enzyme (ACE) and soluble interleukin 2 receptor (sIL-2R) are traditionally used for the diagnosis of sarcoidosis, specific diagnostic markers remain to be determined. In the current study, we found that serum neuron-specific enolase (NSE) levels were elevated in patients with sarcoidosis. Serum NSE levels were positively correlated with serum ACE and sIL-2R levels. The sensitivity of NSE alone was modest, but its combination with sIL-2R and ACE had the highest sensitivity compared to those of each single marker. When comparing serum NSE and pro-gastrin-releasing peptide (ProGRP) levels in SCLC patients with those in patients with sarcoidosis and nonsarcoidotic benign diseases, serum NSE could be used to distinguish SCLC from sarcoidosis and nonsarcoidosis by setting at a cutoff value of 17.0 ng/ml with a sensitivity of 73.5% and a specificity of 90.2%, which were comparable to those of ProGRP. Serum NSE levels were associated with organ involvement and were higher in sarcoidosis patients who had been treated with oral corticosteroid (OCS) than in those who had never received OCS therapies; there was a positive association between elevated serum NSE levels and OCS use. Increased concentrations of serum NSE in patients at the nonremission phase decreased after spontaneous remission, whereas serum NSE levels fluctuated in accordance with serum ACE or sIL-2R levels during the follow-up period in patients with sarcoidosis. These findings suggest that NSE could be a marker for the diagnosis and monitoring of the clinical outcome of patients with sarcoidosis.
GC-MS and Cellular Toxicity Studies on Smokeless-Tobacco Show Alerting Cytotoxic effect on Human Gingiva and Lung Fibroblasts
Authors: Mesaik M. Ahmed , Khalid Asaad , Abdalla Ashraf N. , Sultana Shahnaz , Youssef Abdel-Rahman , Ahmed Izzaddinn E. , Mohammed Yassin I. , Mirghani Hyder O. , Rehman Zia ur , Alhazmi Hassan A. , Al Bratty Mohammed
Published: May 2022

Smokeless tobacco (SLT) has been reported to have deleterious effects on the health of its users. This study aims to analyze the constituents of locally collected SLT sample extracts (S1–S11) from Tabuk region of Saudi Arabia using GC-MS and investigate their cytotoxic effect on human gingival fibroblasts (hGFs), normal human fibroblasts (MRC5), and two cancer cell lines (HT29 and HepG2) using MTT assay. GC-MS results showed that pyridine, 3-(1-methyl-1H-pyrrol-2-yl)-, tetracyclo[4.4.1.1(7,10).0(2,5)]dodec-3-en-11-ol, and cotinine were found in S1, while ethyl iso-allocholate was traced in S2. Compounds 9,12-octadecadienoic acid, ethyl ester, 7-methyl-Z-tetradecen-1-ol acetate, cis-10-heptadecenoic acid and octadecanoic acid, ethyl ester, and nicotine traces were found in S4, while compound 3,7,11,15-tetramethyl-2-hexadecen-1-ol, tetradecamethyl-hexasiloxane, and phytol in S5. Additionally, octadecamethyl cyclononasiloxane, oleic acid, and trimethylsilyl ester were found in S6 and S9, respectively. Interestingly, extracts S4, S10, and S6 were the most cytotoxic to the normal fibroblasts (hGF and MRC5, with low selectivity index: <1), compared with doxorubicin and with their effect on the cancerous cells (HT29 and HepG2). Various components detected in SLT samples were carcinogenic, including nicotine and its derivatives, hexadecanoic acid, 1,2-benzenedicarboxylic acid, and octadecanoic acid. The present study showed that the cytotoxic and possibly carcinogenic effects of the SLT samples on gingiva and lung cells are attributed to many compounds and not only nicotine derivatives, all of which could create health threats for SLT users and lead to various types of cancers, including oral, lung, colon, and liver cancers.
AI-Enabled Energy-Efficient Fog Computing for Internet of Vehicles
Authors: Tariq Hira , Javed Muhammad Awais , Alvi Ahmad Naseem , Hasanat Mozaherul Hoque Abul , Khan Muhammad Badruddin , Saudagar Abdul Khader Jilani , Alkhathami Mohammed
Published: May 2022

Future autonomous electric vehicles (EVs) are equipped with several IoT sensors, smart devices, and wireless adapters, thus forming an Internet of Vehicles (IoVs). These intelligent EVs are envisioned to be a promising solution for improving transportation efficiency, road safety, and driving experience. Vehicular fog computing (VFC) is an evolving technology that allows vehicular application-related tasks to be offloaded to nearby computing nodes and process them quickly. A major challenge in the VFC system is to design energy-efficient task offloading algorithms. In this paper, we propose an optimal energy-efficient algorithm for task offloading in a VFC system that maximizes the expected reward function which is derived using the total energy and time delay of the system for the computation of the task. We use parallel computing and formulate the optimization problem as semi-Markov decision process (SMDP). Bellman optimal equation is used in value iteration algorithm (VIA) to get an optimal scheme by selecting the best action for the current state that maximizes the energy-based reward function. Numerical results show that the proposed scheme outperforms the greedy algorithm in terms of energy consumption.
Safety Technology Risks and Countermeasures in the Intelligent Construction of Coal Mines
Authors: Bai Jinyan , Zheng Dezhi , Jia Chen
Published: May 2022

At present, China’s intelligent coal mine construction is still in the primary development stage. The integrated development of a new generation of information and communication technology and the coal industry is accelerating, and this industry’s intelligent green transformation and upgrading is being promoted. With the continuous acceleration of China’s intelligent coal mine construction and the continuous improvement in the degree of coal mine intelligence, system compatibility, information acquisition, and intelligent research have raised awareness of new mining safety risks. Based on the analysis of the process of China’s intelligent construction, this paper describes the safety management and control risks and the technical challenges faced by China’s intelligent coal mine construction in terms of norms and standards, data acquisition, subsystem and terminal sensors, multisystem integration, safety risk management, and control ability. It is important to make full use of the collaborative advantages of intelligent scientific and technological coal mine resources. Technological innovation and intelligent equipment safety access verification need to be supported, so as to promote the intelligent and high-quality development of coal mines.
Predicting Intensive Care Unit Admission for COVID-19 Patients from Laboratory Results
Authors: Azad Allarakia Basmah M. , Gattan Hattan S. , Abdeen Rawan H. , Al-ahmadi Bassam M. , Shater Abdullah F. , Bazaid Mohammed B. , Althomali Omar W. , Bazaid Abdulrahman S.
Published: May 2022

Trends in routine laboratory tests, such as high white blood cell and low platelet counts, correlate with COVID-19-related intensive care unit (ICU) admissions. Other related biomarkers include elevated troponin, alanine aminotransferase, and aspartate transaminase levels (liver function tests). To this end, the aim of this study was to investigate the effect of changes in laboratory test parameters on ward-based and ICU COVID-19 patients. A total of 280 COVID-19 patients were included in the study and were divided based on admission status into ICU (37) or ward (243) patients. ICU admission correlated significantly with higher levels of several tested parameters, including lactate dehydrogenase, creatinine, D-dimer, creatine kinase, white blood cell count, and neutrophil count. In conclusion, routine laboratory tests offer an indication of which COVID-19 patients are most likely to be admitted to the ICU. These associations can assist healthcare providers in addressing the needs of patients who are at risk of COVID-19 complications.
Status Analysis and Future Development Planning of Fitness APP Based on Intelligent Word Frequency Analysis
Authors: Wang Siqiu , Wang Shengze
Published: May 2022

In order to analyze the current situation analysis and future development plan of fitness APP, this paper analyzes the current situation of fitness APP combined with the intelligent word frequency analysis algorithm. In order to deal with the problem that the existing qPMS algorithm is very time-consuming to discover motifs in the large DNA sequence dataset D, a structural motif discovery algorithm for large DNA sequence datasets, SMS, is proposed. Moreover, this paper performs motif discovery by mining substrings with high frequency in the input sequence. Through data research, it can be seen that the fitness APP based on intelligent word frequency analysis proposed in this paper has good results. On this basis, this paper analyzes the problems existing in the existing fitness APP and provides suggestions for its future development.
Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network
Authors: Zhang Jinwen , Shen Tianxing
Published: May 2022

Action recognition is the basis of intelligent sports training and has a unique role in improving athletes’ sports training ability. Traditional motion recognition cannot accurately identify changes in human skeletal motion, and the generalization ability is weak, which cannot meet the needs of modern sports. This paper is aimed at studying a new method of human motion recognition and better realizing the intelligent development of the Institute of Physical Education. This paper proposes an improved recognition mode of spatiotemporal graph convolutional network, builds a human skeleton feature recognition model, learns the changes of human skeleton motion, and masters the laws of human skeleton motion changes. This paper comprehensively compares the differences of action recognition of several different algorithms and proves the superiority of this method. Afterwards, a comprehensive comparison of 2D and 3D information data and a comparison of whole body and local action recognition methods were conducted to investigate the experience of athletes and trainers. The experimental results of this paper show that the spatiotemporal graph convolutional network can better realize the recognition of motion features, improve the sports training ability of athletes by 20%, and promote the development of sports.
Generation and Characterization of Silver Nanoparticles in Mentha pulegium Extract and Evaluation of Biological Activities of the Prepared Extract
Authors: Naghmouchi Souheila , Al-Zaban Mayasar I. , Al-Zaben Maha , Alharbi Nada , Bahatheq Aisha
Published: May 2022

The aim of the present study was to synthesize silver nanoparticles (AgNPs) using Saudi Mentha pulegium leaves, to characterize the physicochemical properties of the resulting AgNPs, and to evaluate the biological activities of the resulting AgNP‐containing extract. The formation of AgNPs in M. pulegium extract was indicated by a change in color following the addition of silver nitrate and was confirmed using UV‐visible spectroscopy with a maximum absorbance at 425 nm. Energy dispersive X‐ray spectroscopy (EDX) indicated that the anisotropic AgNPs were spherical, and Fourier transform infrared spectroscopy (FTIR) spectral analysis indicated that the aqueous M. pulegium extracts were responsible for reducing Ag+ to Ag0. The secondary metabolite contents of the methanolic M. pulegium extract corresponded to 17 mg GAE/g DW. DPPH and ABT radical‐scavenging assays indicated that the M. pulegium extracts possessed antioxidant activity (IC50 = 6 and 3 μg/mL, respectively). Disc and broth dilution assays revealed that the extracts exerted significant antibacterial activity, with the inhibition zone diameters and minimal inhibition concentrations of 17–24 mm and 0.08–0.62 mg/mL, respectively. These findings clearly indicate that modified plant extracts have high biological importance and potential use as preservatives in the pharmaceutical and food industries.
Crude Oil Foulant Deposition Studies on a Heated Surface Using a Novel Batch Stirred Coupon Test Rig
Authors: Singh Pragya , Krishnaswamy Srinivas , Ponnani Krishnaswamy , Verma Ankur , Rawat Jaya
Published: May 2022

The petroleum refining industry employs a wide variety of heat transfer-based equipment which tend to foul due to the complex nature of associated fluid streams and process conditions. Over the years, different test methods have been researched to understand fouling at the lab, pilot, and/or plant level. Several of these investigations have been limited to understanding fouling in static refinery streams or under non-practical operating conditions. The present study experimentally demonstrates the potential of a high-temperature batch stirred coupon test rig to characterize fouling under noncoking conditions for a specific refinery stream of interest. Experiments were conducted using a representative crude oil stream (obtained from a refinery) in a 2-liter batch autoclave system with a facility to immerse coupons attached to a rotating shaft into the stream during related test runs. The coupon Material of Construction (MOC) was chosen similar to the MOC of the tubes used in a refinery crude heat exchanger train. Experiments covered a range of bulk temperatures from 250°C to 300°C at 100 RPM. The foulant deposits obtained were found to have a strong (hard) and weakly (soft) adhering component. In the temperature range investigated, the hard and soft deposits were largely inorganic and organic in nature, respectively. The fluid bulk temperature was found to influence hard and soft deposit formation. The foulant deposits were characterized using TGA, CHNS, XRD, and SEM/EDAX, respectively. The iron and sulfur content in the hard deposit was more than 45% in total, indicating that the fouling mechanism was corrosion-based resulting from the formation of FeS on the surface. The thermal history of the soft deposit was distinct from the crude oil used. The results obtained confirm the potential of the coupon test rig for understanding fouling in refinery streams.
A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis
Authors: Nagarajan G. , Simpson Serin V. , Venkatachalam K. , Alrasheedi Adel Fahad , Askar S.S. , Abouhawwash Mohamed , P Parthasarathi
Published: May 2022

The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system.
Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network
Authors: Nawabi Awais Khan , Jinfang Sheng , Abbasi Rashid , Iqbal Muhammad Shahid , Heyat Md Belal Bin , Akhtar Faijan , Wu Kaishun , Twumasi Baidenger Agyekum
Published: May 2022

Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apoptosis is thought to have at least three functionally distinct phases: induction, effector, and execution. Recent studies have revealed that reactive oxygen species (ROS) and the oxidative stress could play an essential role in apoptosis. Advanced microscopic imaging techniques allow biologists to acquire an extensive amount of cell images within a matter of minutes which rule out the manual analysis of image data acquisition. The segmentation of cell images is often considered the cornerstone and central problem for image analysis. Currently, the issue of segmentation of mitochondrial cell images via deep learning receives increasing attention. The manual labeling of cell images is time-consuming and challenging to train a pro. As a courtesy method, mitochondrial cell imaging (MCI) is proposed to identify the normal, drug-treated, and diseased cells. Furthermore, cell movement (fission and fusion) is measured to evaluate disease risk. The newly proposed drug-treated, normal, and diseased image segmentation (DNDIS) algorithm can quickly segment mitochondrial cell images without supervision and further segment the highly drug-treated cells in the picture, i.e., normal, diseased, and drug-treated cells. The proposed method is based on the ResNet-50 deep learning algorithm. The dataset consists of 414 images mainly categorised into different sets (drug, diseased, and normal) used microscopically. The proposed automated segmentation method has outperformed and secured high precision (90%, 92%, and 94%); moreover, it also achieves proper training. This study will benefit medicines and diseased cell measurements in medical tests and clinical practices.
Comprehensive Budget Execution Performance Evaluation of Companies Incorporating EVA Unsupervised Learning Model
Authors: Zhao Jin
Published: May 2022

In order to improve the company’s comprehensive budget execution performance and the effect of corporate strategy formulation, this paper integrates the EVA unsupervised learning model to evaluate the company’s comprehensive budget execution performance. Moreover, this paper improves the algorithm model by comparing the budgeting process of the company under the traditional budget and the budgeting process of the company under the guidance of EVA. Aiming at the company’s massive data, this paper uses combined with the big data audit algorithm to process data and builds a company’s comprehensive budget execution performance evaluation system based on the EVA unsupervised learning model. In addition, this paper combines cluster analysis to verify the effect of the system in this paper. The research shows that the company’s comprehensive budget execution performance evaluation system based on EVA unsupervised learning model can effectively improve the effect of the corporate financial budget.
The Evaluation Value of Diffusion‐Weighted Imaging for Brain Injury in Patients after Deep Hypothermic Circulatory Arrest
Authors: Zhuang Jiaxiang , Lin Xiandong , Lin Jianbing , Yu Shun , Dai Shuangbo , Yan Licheng , Chen Yuanxiang , Wang Ren
Published: May 2022

Objective. Cerebral complications may occur after surgery with deep hypothermic circulatory arrest (DHCA). Diffusion‐weighted imaging (DWI) has shown promising results in detecting early changes of cerebral ischemia. However, studies in human models are limited. Here, we examined the significance of DWI for detecting brain injury in postoperative patients after DHCA. Methods. Twelve patients who had undergone selective cerebral perfusion with DHCA were enrolled. All patients underwent magnetic resonance imaging (MRI) examinations before and after the operation with T1‐weighted phase (T1W) and T2‐weighted phase (T2W). Magnetic resonance angiography (3D TOF) was applied to observe intracranial arterial communication situations. DWI was employed to calculate the apparent diffusion coefficient (ADC) values. The neurocognitive function of patients was assessed preoperatively and postoperatively using the Montreal Cognitive Assessment Scale (MoCA), Hamilton Depression Scale (HAMD), and Hamilton Anxiety Scale (HAMA). Results. The ADC values of the whole brain of patients after surgery were significantly higher than before surgery (P = 0.003). However, no significant difference in the ADC values of other regions before and after the operation was observed. There was no significant effect on the postoperative cognitive function of patients after surgery, but visual‐spatial and executive abilities were significantly reduced, while psychological anxiety (P = 0.005) and depression levels (P < 0.05) significantly increased. Correlation analysis revealed a significant association between ADC change values and depression change values (P < 0.05). Conclusion. DHCA demonstrated no significant effect on the cognitive function of patients but could affect the mood of patients. On the other hand, DWI demonstrated promising efficiency and accuracy in evaluating brain injury after DHCA.
Intelligent Construction and Mechanism of Educational Big Data Information for Resource Sharing
Authors: He Yuan , Yi Liqiong , Xu Si
Published: May 2022

The word “sharing” is not unfamiliar to everyone now, but many people do not understand the sharing of educational resources (ER), and the distribution of ER does not seem to be the topic that people are most concerned about. This paper is aimed at conducting research on the construction of educational big data information intelligence based on resource sharing. A parallel association rule mining approach for educational big data is proposed in this research. Now, the educational big data is classified, and then, the data is operated. At the same time, a CcG-based educational information resource sharing platform has also been built for educational resource sharing, which can be used for cloud download and upload functions of cloud computing (CcG) for teaching and educational resource sharing. The experimental results of this paper show that the educational information resource sharing platform in this paper processes about 150 information per second, and its service performance must be better than that of traditional educational platforms.
Antidiarrheal Effect of 80% Methanol Extract and Fractions of the Leaves of Ocimum lamiifolium in Swiss Albino Mice
Authors: Adela Alemu Muluken , Andargie Yared , Sisay Weretaw , Mengie Teklie , Tessema Desta Getaye , Ayalew Tessema Tewodros , Belete Abebe Rahel , Melese Birru Eshetie , Tarekegn Gebyaw Sisay , Adugna Ayanaw Meaza
Published: May 2022

Introduction. Worldwide, in children of under five years of age, diarrhea is responsible for more than 760,000 annual deaths. It is treated with both modern drugs and traditional medicinal plants, including O. lamiifolium. But the use of this plant as an antidiarrheal agent is not scientifically validated. Therefore, this study was aimed to evaluate antidiarrheal efficacy of the plant. Method. The leaf powder was macerated by 80% methanol and then fractionated using n-hexane, n-butanol, and distilled water. Antidiarrheal activity was evaluated through different models (castor oil-induced diarrhea, enteropooling, and motility) using onset, number of wet feces, fluid content, weight and volume of intestinal content, and motility as test parameters by administering treatment doses to groups of mice. Group I received 10 mL/kg of the dissolving vehicle, Group II received either loperamide or atropine, and Groups III-V received extract doses of 100, 200, and 400 mg/kg, respectively. One-way ANOVA was used to analyze the data, followed by Tukey’s post-hoc test. Results. The crude extract exhibited a significant effect on the fluid content of feces at all tested doses. Additionally, the n-butanol and distilled water fractions revealed significant effects on onset of diarrhea at 400 mg/kg (

p
<
0.05

), while the n-hexane fraction showed significant effects on number of wet feces, onset, and fluid content of feces at all tried doses. The crude extract and all the fractions (at 200 and 400 mg/kg) decreased the weight and volume of intestinal content significantly. Similarly, both the crude extract and distilled water fraction at 400 mg/kg as well as n-butanol and n-hexane fractions at 200 and 400 mg/kg showed meaningful differences on peristaltic index as compared to the negative control. Conclusion. The results revealed that the leaf extract of O. lamiifolium has an antidiarrheal activity, which supports the traditional medical practice.
Comprehensive Evaluation System for Realizing the Ability of University Piano Teaching
Authors: Ma WeiNa
Published: May 2022

Students benefit from teacher’s action during education in a variety of subjects. Identifying the need for a better understanding of the influence of various presentation types at different levels of student proficiency is extremely important. Moreover, these skills are also important to any technique of learning piano in a school environment. The significance of music in the school’s curriculum, new concepts for teaching music in the school, and new ideas for teaching piano were among them. This study focuses on a teaching environment and piano learning in which beginning and advanced piano students study to produce a particular style of staccato in three types of group instruction scenarios. The musical difficulty is only demonstrated through audio and observation of the teacher’s action performance. The comprehensive ability evaluation system for piano teaching can, to a certain extent, evaluate and sort the ability of college piano teaching. Therefore, this paper utilizes the attribute method in combination with instant analysis which provides seven attribute indicators. The approach is reasonable, and evaluating characteristics such as the level of the school’s teaching plan set and related psychological weights may well be revised as necessary to address users’ requirements. After completely integrating the qualities of the school and comprehensively examining various contributing factors, the students’ learning quality in different grades or the same grade of colleges and universities may be determined. A comparison against the existing schemes can effectively represent the school’s overall teaching level.
Application Research of Graphic Design Based on Information Resource-Sharing and Big Data Technology
Authors: Xu Dan
Published: May 2022

The field of graphic design is an important industry rising in recent years if a new graphic design solution requires the designer to design from the ground up, it will consume a lot of time and material resources. The information resource-sharing platform already has many element characteristics to provide the designer to carry on the reference, and this will greatly save the designer time and the material resources. The traditional graphic design method will consume some resources only by relying on the designer and the solutions designed by this method may not be innovative enough. This research will design a graphic design system and management method from the point of data and big data of information resource-sharing platform. The results show that the IRM platform can obtain more effective successful cases of graphic design feature data. The clustering method and CNN method can effectively deal with the pattern feature, color feature, shape feature, and character feature of graphic design. It can not only effectively analyze the feature data value of graphic design but also fits well with the trend of data values. This is a valuable research work for graphic designers. The largest prediction error is only 2.34%, and this part of the error mainly comes from the prediction of pattern features of graphic design. All other forecast errors are within 2.03%.
Antidandruff Activity of Polyherbal (Murraya koenigii, Moringa oleifera, and Psidium guajava) Extract against Malassezia Species: In Silico Studies
Authors: Sharma Akshata , Sakthiselvan Punniavan , Karthick V. , Subraja R. , Srinivasan T. , Amudha K. , John Shani T
Published: May 2022

Dandruff is one of the leading causes of hair fall and is commonly caused by bacterial and fungal species especially the Malassezia species. The lipolytic enzymes of the Malassezia species play an important role in obtaining lipids from the environment, and they are necessary for the growth and pathogenicity of Malassezia. Therefore, the lipases of the three most commonly (Malassezia globosa, Malassezia furfur, and Malassezia restricta) affecting Malassezia species have been taken for the In-silico studies. The three-dimensional structure of the lipase of Malassezia globosa (PDB ID: 4ZRE) was retrieved from protein data bank, and the other two proteins of the two species (Malassezia furfur and Malassezia restricta) were built using homology modelling by Swiss modeler. The lipases of the Malassezia species along with the 16 bioactive compounds of the polyherbal (Murraya koenigii, Moringa oleifera, and Psidium guajava) ethanolic extract predicted by GC-MS analysis were taken for the molecular docking studies. The pharmacokinetic properties of the bioactive compounds were predicted. Thus, the molecular docking results revealed that the bioactive compound flavone with score of −7.160 Kcal/mol, isopropyl stearate with binding score of −10.107 Kcal/mol, and eugenol with a binding score of −8.296 Kcal/mol have shown good binding affinity against the lipase of Malassezia restricta, Malassezia globosa, and Malassezia furfur, respectively. Hence, these compounds can be further investigated by in-vitro and in-vivo studies to be used as potential drug candidates.
The Application of Wireless Sensor Technology of Internet of Things in Korean Language Teaching
Authors: Bi Yajie
Published: May 2022

The Internet of Things sensor technology network can be regarded as consisting of three parts: data acquisition network, data distribution network, and control management center. Sensor technology has the comprehensive processing ability to obtain various information signals, and the technology is integrated into the sensor equipment, so that the sensor technology is connected to form a sensor network with comprehensive information processing capability, which has been widely used in foreign language teaching in recent years. This paper analyzes the shortcomings of traditional Korean language teaching methods in the new situation in detail. It starts from the actual situation and focuses on stimulating students’ interest and proposes a Korean language teaching method based on the Internet of Things wireless sensor technology. This article focuses on analyzing the application of the wireless sensor technology of the Internet of Things in Korean language teaching and applies this method in practical teaching. It verifies the feasibility and effectiveness of the technology for Korean language teaching through the analysis and comparison of the collected data. In this paper, the experiment shows that the application of the wireless sensor technology of the Internet of Things to the Korean language teaching can improve the students’ grades by about 30%. This stimulates students’ interest in learning very well and is of great help to improve students’ learning ability, oral communication ability, and Korean thinking ability, which stimulates students’ interest in learning and improves students’ Korean listening, speaking, reading, writing, and comprehensive learning abilities.
Application of Grammar Error Detection Method for English Composition Based on Machine Learning
Authors: He Haiyin , Maia Darchia
Published: May 2022

With the development of grammar-checking technology and algorithms, the grammar-checking system has been widely used in various fields. This paper designs and implements a grammar-checking system for English composition. The grammar-checking system adopts a multimodule design. The grammar-checking system is composed of a multilayer rule error-correcting module and a machine learning error-correcting module. This study aims to build a machine learning algorithm model that can detect English grammar errors by analysing and comparing different algorithm models currently applied in the field of education and then apply the trained model to the English composition grammar detection system. The results show that the system can save a lot of time and labor cost of manual marking, liberate teachers from heavy and repeated evaluation activities, and put more time and energy on teaching. At the same time, it can provide learners with more objective and timely feedback so that learners can intuitively and clearly know that they are prone to make grammatical mistakes in the process of English learning. It plays a certain assisting and guiding role in English learners’ autonomous learning.
Feasibility of Music Composition Using Deep Learning-Based Quality Classification Models
Authors: Zhang Shuang
Published: May 2022

Polyphonic music technique is the foundation of students’ understanding of musical works. The mastery of polyphonic music techniques enables students to better understand the meaning of musical works and get in touch with the soul of music. Hence, teaching polyphonic music is a compulsory course for composition theory. In the past, all the concepts taught in the composition theory class included the use of the main key, and the minimal amount of polyphonic music works was covered. Also, even if students encountered polyphonic music, a brief inclusion of the same would be included in teaching, creating difficulties for the students to understand polyphonic music well. Intelligent music composition, however, refers to a formalized process that allows the composer to create music with the help of a computer, ensuring minimal human intervention. With the popularity of the Internet and the rapid development of multimedia technology, the majority of the users now use online music applications. Therefore, the need to automatically organize and manage the huge amount of music data effectively has evolved. Studying intelligent music composition helps to understand and simulate the way of thinking of composers in making compositions. It also helps to assist composers in making music, in addition to entertaining people. Considering the aforementioned, the present paper uses a deep learning-based quality classification model for music composition feasibility. The experimental results show that the algorithm has the advantages of fast detection speed and high quality. It helps composers to compose music, greatly reduces the workload, and also ensures certain promotion value.
Biomechanical Analysis of Martial Arts Movements Based on Improved PSO Optimized Neural Network
Authors: Yan Shifang , Chen Jun , Huang Hai
Published: May 2022

Monitoring the development trend of martial arts biomechanics, which provides ideas for the scientific research of martial arts. It also proposes discussions and suggestions based on research results in related fields. As a traditional sport in China, martial arts have been around for a long time, and there is still a lot of research space. The biomechanical research of Wushu must be contemporary, forward-looking, and creative, and fully meet the needs of Wushu. This paper adopts the bibliometric method: it conducts statistical analysis on the research status and development trend of core journals and master and doctoral dissertations in the field of sports from 2011 to 2015. It aims to understand the latest developments in the biomechanical research of martial arts routines in the field of Chinese sports. The results show that the BP neural system model effectively improves the accuracy, recall, and F value of test classification. In the Naive Bayes method, the accuracy of the opening report is only 77.1%, the recall rate is 75.8%, and the F value is 76.3%. In the literature method, the accuracy of the opening report is 82.2%, the recall rate is 83.5%, and the F value is 82.8%. In the text method, the accuracy of the opening report is 86.5%, the recall rate is 88.3%, and the F value is 87.2%. In contrast, it outperforms simple Bayesian algorithms and methods in the literature.