bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Mining and Analysis of Management Theory on the Emotional Recognition of Students’ Physical Fitness Improvement
Authors: Fan Jingang , Yu Le , Liu Jiabao
Published: May 2022

The improvement of the economic level has brought earth-shaking changes to people’s living standards. However, with the improvement of living standards, the pace of life is also accelerating, people’s physical and psychological pressure has also increased sharply, and people’s physical and mental health problems are also increasing. As an important threshold for talent training schools, real-time monitoring of students’ physiques, and effective training of students’ physical fitness, so schools need to strengthen the scientific management of students’ work and rest and exercise according to management theory. To better pay attention to the correlation between students’ physical fitness and psychological emotions, this paper designs a data analysis system for students’ physical health based on data mining, analyzes the improvement of students’ physical fitness under health management, and then identifies students’ emotions according to this to observe the impact of students’ physical fitness improvement on emotional changes, so as to effectively improve students’ physical health level and mental health level. The experimental results of the research system in this paper show that the improvement of students’ physical fitness can promote students’ emotional recognition, and its accuracy can be improved by at least 2%, and various types of emotions can be accurately identified, indicating that changes in students’ physical fitness have a certain impact on student emotions.
A Task Assignment Method Based on User-Union Clustering and Individual Preferences in Mobile Crowdsensing
Authors: Shao Zihao , Wang Huiqiang , Zou Yifan , Gao Zihan , Lv Hongwu
Published: May 2022

Mobile crowdsensing (MCS) offers a novel paradigm for large-scale sensing with the proliferation of smartphones. Task assignment is a critical problem in mobile crowdsensing (MCS), where service providers attempt to recruit a group of brilliant users to complete the sensing task at a limited cost. However, selecting an appropriate set of users with high quality and low cost is challenging. Existing works of task assignment ignore the data redundancy of large-scale users and the individual preference of service providers, resulting in a significant workload on the sensing platform and inaccurate assignment results. To tackle this issue, we propose a task assignment method based on user-union clustering and individual preferences, which considers the influence of clustering data quality and preference-based sensing cost. Firstly, we design a user-union clustering algorithm (UCA) by defining user similarity and setting user scale, which aims to balance user distribution, reduce data redundancy, and improve the accuracy of high-quality user aggregation. Then, we consider individual preferences of service providers and construct a preference-based task assignment algorithm (PTA) to achieve the diversified sensing cost control needs. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm, which realizes the individual preference-based task assignment under the premise of ensuring high-quality data.
[Retracted] Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams
Authors: Akram Abdulrazzaq Ammar , Al-Douri Asaad T. , Abdullah Hamad Abdulsattar , Musa Jaber Mustafa , Meraf Zelalem
Published: May 2022

Schistosoma mansoniis one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image‐based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. However, the SPNN proved to be faster than the CNN.
A Reinforcement Learning-Based Dynamic Clustering Algorithm for Compressive Data Gathering in Wireless Sensor Networks
Authors: Wang Xun , Chen Hongbin
Published: May 2022

Compressive data gathering (CDG) is an effective technique to handle large amounts of data transmissions in resource-constrained wireless sensor networks (WSNs). However, CDG with static clustering cannot adapt to time-varying environments in WSNs. In this paper, a reinforcement learning-based dynamic clustering algorithm (RLDCA) for CDG in WSNs is proposed. It is a dynamic and adaptive clustering method aiming to further reduce data transmissions and energy consumption in WSNs. Sensor nodes act as reinforcement learning (RL) agents which can observe the environment and dynamically select a cluster to join in. These RL agents are instructed by a well-designed reward scheme to join a cluster with strong data correlation and proper distance. It is also a distributed and lightweight learning method. All agents are independent and operate in parallel. Additional overheads introduced by RL are lightweight. Computations of a linear reward function and a few comparison operations are needed. It is implementable in WSNs. Simulations performed in MATLAB validate the effectiveness of the proposed method and simulation results show that the proposed algorithm achieves the desired effect as well as fine convergence. It decreases data transmissions by 16.6% and 54.4% and energy consumption by 6% and 29%, respectively, compared to the two contrastive schemes.
Power IoT System Architecture Integrating Trusted Computing and Blockchain
Authors: Yan Yong , Chen Jinqian , Yao Ying , Guo Shaoyong , Xiong Ao , Zhang Wang
Published: May 2022

With the construction of power Internet of Things, the network scale is constantly expanding, the network structure is increasingly complex, and the number of terminals is increasing rapidly, resulting in huge pressure on network security protection, and it is urgent to build a credible operation environment to ensure that the Internet of Things runs safely and reliably. This paper proposes a power Internet of Things architecture combining trusted computing and blockchain. The architecture ensures the credibility and security of the terminal based on remote integrity verification and access control policy verification. A method for malicious node detection based on blockchain (MMNDB) is placed in acquisition layer. It is used to monitor the status of each acquisition devices in time, detect the malicious terminal in time, isolate the suspicious terminal with low credibility from the power Internet of Things, and ensure credibility of the acquisition terminal and the data collected. Under premise of ensuring the communication efficiency of the power communication network, this architecture can greatly ensure the stable working of the power Internet of Things.
Random Forest Algorithm Based on Speech for Early Identification of Parkinson’s Disease
Authors: Fan Ping
Published: May 2022

To investigate the effectiveness of identifying patients with Parkinson’s disease (PD) from speech signals, various acoustic parameters including prosodic and segmental features are extracted from speech and then the random forest classification (RF) algorithm based on these acoustic parameters is applied to diagnose early-stage PD patients. To validate the proposed method of RF algorithm in early-stage PD identification, this study compares the accuracy rate of RF with that of neurologists’ judgments based on auditory test outcomes, and the results clearly show the superiority of the proposed method over its rival. Random forest algorithm based on speech can improve the accuracy of patients’ identification, which provides an efficient auxiliary method in the early diagnosis of PD patients.
Clinical Value of the Prognostic Nutrition Index in the Assessment of Prognosis in Critically Ill Patients with Stroke: A Retrospective Analysis
Authors: Liu Yang , Yang Xiaobin , Kadasah Sultan , Peng Chaosheng
Published: May 2022

Purpose. The purpose of study was to evaluate the association between prognostic nutritional index (PNI) and all-cause mortality of critically ill patients with stroke. Methods. Clinical data derived from Multiparameter Intelligent Monitoring in Intensive Care were analyzed. The primary endpoint was 30-day all-cause mortality; secondary endpoints were 90-day mortality and one-year cause mortality. The potential prognostic roles of PNI were analyzed by Cox proportional hazard models. The independent prognostic roles of PNI in the cases were analyzed by smooth curve fitting. Results. Concerning 30-day mortality, the HR (95% CI) for a high PNI (≥39.7) was 0.700 (0.544, 0.900;

P
=
0.00539

), compared to a low PNI (<39.7). After adjusting for multiple confounders, the HR (95% CI) for a high PNI (≥39.7) was 0.732 (0.547, 0.978;

P
=
0.03514

), compared to a low PNI (<39.7). Regarding 90-day and one-year mortality, a similar trend was observed. In addition, a nonlinear association between PNI and 30-day mortality was found. Using recursive algorithm and two-piecewise linear regression model, inflection point (IP) was calculated, which was 49.4. On the right side of the IP, there was a positive relationship between PNI and 30-day mortality, and the effect size, 95% CI, and

P

value were 1.04 (1.01, 1.07),

P
=
0.0429

, respectively. On the left of the IP, the effect size, 95% CI, and

P

value were 0.97 (0.96, 0.99) and 0.0011, respectively. Conclusions. The PNI was an independent predicting factor of 30-day, 90-day, and 1-year mortality of the critically ill patients with stroke. In addition, there was a U-shaped relationship between PNI and all-cause mortality of stroke patients. PNI was a risk factor for the outcome of stroke when PNI was >49.4, while PNI was a protective factor for outcome of stroke when PNI was <49.4.
Noncontact Intraocular Pressure Measurement over Bandage Contact Lens and the Effect of Pentacam and Corvis ST’s IOP Correction System
Authors: Cai Xueting , Qin Yi , Liu Sixiu , Cheng Zhewei , Lu Fan , Qu Jia , Chen Ding
Published: May 2022

Purpose. To evaluate the influence of intraocular pressure (IOP) measurement while wearing bandage contact lens (BCL) and the effect of Pentacam and Corvis ST’s correction systems. Methods. It was a prospective comparative study. Forty healthy subjects (40 eyes) were included in this study. Eyes were measured using noncontact tonometer (NCT), Corvis ST, and Pentacam before and after wearing BCL. Pentacam’s five correction formulas (Ehlers formula, Shah formula, Dresden formula, Kohlhaas formula, Orssengo/Pye formula) and Corvis ST’s correction formulas (Ehlers formula and biomechanical corrected formula) were used to correct the IOP values before and after BCL wearing. The IOP values were compared, and the correction effect of different systems were evaluated. Results. The mean age of the subjects was 24.4 ± 0.60 years. The mean IOP obtained by NCT was 14.8 ± 3.2 mmHg before BCL wearing and was 15.7 ± 3.4 mmHg after BCL wearing. The mean IOP was significantly increased after BCL wearing (0.9 ± 2.9 mmHg,

P
=
0.05

). Four of the five Pentacam’s correction formulas (except Kohlhaas formula) showed no significant difference in the mean corrected IOP values before and after BCL wearing (all

P
>
0.05

). The mean IOP obtained by Corvis ST was 13.7 ± 2.8 mmHg before BCL wearing and was 15.0 ± 4.0 mmHg after BCL wearing. The mean IOP was significantly increased after BCL wearing (1.3 ± 2.4 mmHg,

P
<
0.05

). Corvis ST’s correction formula (Ehlers formula other than biomechanical corrected formula) showed no significant difference in the mean corrected IOP values before and after BCL wearing (

P
>
0.05

). Conclusion. The IOP measurements over BCL by NCT and Corvis ST was found to be increased. The correction systems of Pentacam (Ehlers formula, Shah formula, Dresden formula, and Orssengo/Pye formula) and Corvis ST (Ehlers formula) are useful in correcting the IOP measuring deviation induced by BCL wearing.
Improving the Application of Blockchain Technology for Financial Security in Supply Chain Integrated Business Intelligence
Authors: Zhang Yaming , Zhang Chaozhuo
Published: May 2022

For major organizations, establishing financial shared service centers to unify the financial activities of entities in multiple countries and regions for bookkeeping and reporting has become the trend for financial and management optimization. Based on this, this paper selects security company A as a case study, introduces the emerging blockchain technology (BCT) as the core and embeds it into the concept of financial shared center, tries to explore a new model of financial shared service model under BCT from the theoretical perspective, and optimizes the original service model by improving its business process, creating visualized, supervisible, and traceable financial data processing capability, and expanding. This paper first examines the financial shared service model of Company A. This paper starts with the actual situation of Company A’s financial shared service model. Complex procedures, highly centralized information, unreasonable division of labor in corporate processes, and a lack of adequate information communication methods are discovered through process analysis. New requirements for accountants have emerged in the digital age, and the security protection of private keys in asymmetric encryption algorithms in organizations, particularly in large and medium-sized enterprises, must be done well.
Evaluation of Sustainable Environment-Friendly Interior Decoration Design from the Perspective of Low-Carbon Economy
Authors: Xu Wei , Zhang Yongqiang
Published: May 2022

With the rapid development of economic and social productivity, the living standards of our people have been continuously improved. As human society enters industrialization and urbanization, in order to meet the increasing needs of life, people have higher and higher requirements for the environmental quality of their lives. People spend nearly 90% of their time indoors. Therefore, the environmental quality of indoor spaces has become an issue that cannot be ignored. The interior space environment of the building is closely related to everyone’s life. However, when people decorate their rooms luxuriously and warmly, the subsequent interior decoration sequelae are also seriously affecting people’s health. The pollution caused by indoor decoration is serious, whether it is the use of decoration materials or improper indoor design that will cause indoor air pollution. Based on building materials and interior design, this paper systematically analyzes the pollutants in the fully decorated interior space and proposes that from the perspective of low-carbon economy, interior space design should be based on the ecological environment, and the focus of the design is on the rational use of nature. Resources and decoration methods should start from the application of natural colors and natural materials and organically combine green materials and green decoration designs to create a more comfortable, healthy, safe, and environmentally friendly indoor space environment.
Inhibition of miR-421 Preserves Mitochondrial Function and Protects against Parkinson’s Disease Pathogenesis via Pink1/Parkin-Dependent Mitophagy
Authors: Dong Xiaolin , He Xianghua , Yang Li , Li Qingyun , Xu Yanming
Published: May 2022

Mutations in PINK1 and Parkin are a major cause of Parkinson’s disease (PD) pathogenesis. In addition, PINK1 and Parkin are two mitochondrial proteins that jointly contribute to mitochondrial homeostasis via mitophagy. Mitochondrial dysfunction is the most significant mechanism underlying PD pathogenesis. Thus, understanding the regulatory mechanism of PINK1 and Parkin expression is beneficial to the treatment of PD. In this study, we found that miR-421 expression was upregulated in mice treated with MPTP, as well as in SH-SY5Y cells treated with methyl-4-phenylpyridine (MPP+). Inhibition of miR-421 alleviated neurodegeneration in MPTP-treated mice and promoted mitophagy in MPP+-treated SH-SY5Y cells. Bioinformatics software predicted that Pink1 is a downstream target protein of miR-421. In addition, miR-421-induced Pink1 and Parkin inhibition negatively modulates mitophagy in MPP+-treated SH-SY5Y cells. In addition, our study confirmed that Pink1/Parkin is responsible for miR-421-regulated cell mitophagy. Overall, this study revealed that miR-421 regulates nerve cell mitophagy through the Pink1/Parkin pathway.
A Study on Cross-Media Teaching Model for College English Classroom Based on Output-Driven Hypothetical Neural Network
Authors: Guo Xiangyu
Published: May 2022

In the field of education, the development of educational big data has become an important strategic choice to promote the construction of the digital campus and educational reform, and educational big data has become a new driving force in the field of education that cannot be ignored. Based on the theoretical basis of output-driven hypothesis neural network, and combining the media spanning of contemporary art and cross-media association effect, this study changes the status quo of English teaching through traditional methods such as grammar-translation method and deductive method and constructs a new cross-media university English teaching model. Based on the existing feature learning model of two-way attention, combined with existing techniques such as generative adversarial networks and semantic hashing, the semantic association between different media data is deeply mined, and feature learning is integrated with adversarial learning and hash learning to build a unified semantic space for different media data. In this paper, we focus on the structure and characteristics of convolutional neural networks through the study of deep learning theory, discuss three classical convolutional neural network models, such as AlexNet, VGG, and GoogLeNet, and propose a convolutional neural network model applicable to cross-media teaching in college English classroom and carry out experimental validation, and the results show that the proposed neural network model is based on output-driven hypothesis. The following research has been added to the abstract: to address the key problem of the semantic gap that is difficult to cross in cross-media semantic learning, a cross-media supervised adversarial hashing model based on two-way attentional features is proposed. Based on the existing two-way attention-based feature learning model, we combine existing techniques such as generative adversarial networks and semantic hashing to deeply explore the semantic association between different media data and integrate feature learning with adversarial learning and hashing to build a unified semantic space for different media data. The results show that the proposed neural network model of cross-media teaching in college English classrooms based on the output-driven hypothesis can not only promote the improvement of students’ English literacy skills but also have a certain promotion effect on their overall performance improvement.
Multiparty Data Publishing via Blockchain and Differential Privacy
Authors: Gu Zhen , Zhang Kejia , Zhang Guoyin
Published: May 2022

Data are distributed between different parties. Collecting data from multiple parties for analysis and mining will serve people better. However, it also brings unprecedented privacy threats to the participants. Therefore, safe and reliable data publishing among multiple data owners is an urgent problem to be solved. We mainly study the problem of privacy protection in data publishing. For a centralized scenario, we propose the LDA-DP algorithm. First, the within-class mean vectors and the pooled within-class scatter matrix are perturbed by the Gaussian noise. Second, the optimal projection direction vector with differential privacy is obtained by the Fisher criterion. Finally, the low-dimensional projection data of the original data are obtained. For distributed scenarios, we propose the Mul-LDA-DP algorithm based on a blockchain and differential privacy technology. First, the within-class mean vectors and within-class scatter matrices of local data are perturbed by the Gaussian noise and uploaded to the blockchain network. Second, the projection direction vector is calculated in the blockchain network and returned to the data owner. Finally, the data owner uses the projection direction vector to generate low-dimensional projection data of the original data and upload it to the blockchain network for publishing. Furthermore, in a distributed scenario, we propose a correlated noise generation scheme that uses the additivity of the Gaussian distribution to mitigate the effects of noise and can achieve the same noise level as the centralized scenario. We measure the utility of the published data by the SVM misclassification rate. We conduct comparative experiments with similar algorithms on different real data sets. The experimental results show that the data released by the two algorithms can maintain good utility in SVM classification.
Effects of CIK Cell Therapy Combined with Camrelizumab on the Quality of Life in Patients with Nasopharyngeal Carcinoma and Analysis of Prognostic Factors
Authors: Feng Tao , Luo Xiu , Cao Wenping , Man Rongjun , Feng Xinrong , Song Yujie
Published: May 2022

Objective. To investigate the effects of CIK (cytokine-induced killer) cell therapy combined with camrelizumab on the quality of life in patients with nasopharyngeal carcinoma and prognostic factors. Methods. In this retrospective study, the materials of 80 patients with nasopharyngeal carcinoma treated in our hospital (February 2017–February 2019) were retrospectively analyzed, and they were equalized into experimental group (n = 40) and control group (n = 40) according to the order of admission. Both groups received 200 mg of camrelizumab on day 1 combined with 10 mg of anrotinib from day 2 to day 4. The patients received the above program every 3 weeks and 4 treatment cycles. The experimental group also received CIK cell therapy simultaneously. The patients’ quality of life, immune indexes, local control, metastasis, and survival rate were compared between the two groups, and the prognostic factors were analyzed by logistic analysis. Results. Compared with the control group, the experimental group achieved much higher scores of physical well-being (18.38 ± 2.31), social/family well-being (16.40 ± 2.24), emotional well-being (15.35 ± 2.30), functional well-being (17.30 ± 2.20), and head and neck cancer subscale (15.40 ± 2.01,

P

 < 0.001) and eminently better immune indexes (

P

 < 0.001) after treatment. During the 24-month follow-up, there were 2 recurrent cases (5.0%) and 2 cases (5.0%) with distant metastasis among the 40 patients in the experimental group; there were 8 recurrent cases (20.0%) and 7 cases (17.5%) with distant metastasis among the 40 patients in the control group. In the experimental group, the median survival period was 18 months and the 2-year survival rate was 97.5% (39/40). In the control group, the median survival period was 14 months and the 2-year survival rate was 85.0% (34/40). Among the 80 patients, 7 cases (8.75%) died and 73 cases (91.25%) survived. After conducting the single-factor analysis, remarkable differences in the cases of IV stage, quality of life after treatment, and immune indexes after treatment between the survival group and the death group were observed (

P

 < 0.05). According to the multiple-factor analysis, the clinical stage and immune indexes were identified as the prognostic factors. Conclusion. CIK cell therapy combined with camrelizumab can enhance the life quality and immune function of the patients with nasopharyngeal carcinoma, thus improving their prognoses.
Possibilities of Using Macrolepiota procera in the Production of Prohealth Food and in Medicine
Authors: Adamska Iwona , Tokarczyk Grzegorz
Published: May 2022

Parasol mushroom (Macrolepiota procera) is a fungus that is often included in the menu of people looking for replacements for meat products and at the same time appreciating mushrooms. Its fruiting bodies are known for their delicate flavor and aroma. The aim of the publication was to analyze the latest information (mainly from 2015 to 2021) on the chemical composition of the M. procera fruiting bodies and their antioxidant properties. The data on other health-promoting properties and the possibilities of using these mushrooms in medicine were also compiled and summarized, taking into account their antibacterial, antioxidant, anti-inflammatory, regulatory, antidepressant, and anticancer effects. Moreover, the influence of various forms of processing and conservation of raw mushroom on its health-promoting properties was discussed. The possibilities of controlling the quality of both the raw material and the prepared dishes were also discussed. Such an opportunity is offered by the possibility of modifying the growing conditions, in particular, the appropriate selection of the substrate for mushroom cultivation and the deliberate enrichment of its composition with the selected substances, which will then be incorporated into the fungus organism.
The Influence of Obesity on Small Bowel Capsule Endoscopy
Authors: Omori Teppei , Sasaki Yu , Koroku Miki , Kambayashi Harutaka , Murasugi Shun , Yonezawa Maria , Nakamura Shinichi , Tokushige Katsutoshi
Published: May 2022

Objective. Intestinal motility may be different in obese and nonobese patients, but this has not been determined. Here, we sought to evaluate the effect of obesity on small bowel capsule endoscopy (SBCE). Patients and Methods. We retrospectively analyzed the cases of the 340 patients who underwent SBCE for small intestinal disease (excluding cases of unobservable total small bowel, small bowel stenosis, and bowel resection) at our hospital during the period January 2014 to December 2020 to extract patient background factors and the bowel transit times of SBCE according to the presence/absence of obesity (defined as a

body
 
mass
 
index
 

BMI

≥
25
 
kg
/

m

2

). Results. The obese group was 54 patients (nonobese,

n
=
286

). The small bowel transit time (SBTT) was significantly shorter in the obese patients compared to the nonobese patients (

p
=
0.0026

), and when we divided the patients by their short/long SBTTs using 216.5 min as the cutoff, we observed significant between-group differences in the patients’ age (≥60 years) and in the patients’ hospitalization status at the time of the SBCE examination. A multivariate analysis revealed that hospitalized status at the examination is a factor contributing significantly to a long SBTT (OR 0.25, 95% CI: 0.15–0.42,

p
<
0.0001

). An analysis using the outpatient/inpatient conditions showed that obesity was an independent factor in the inpatient status at the SBCE examination with a significant short SBTT (OR 2.91, 95% CI: 1.06–7.97,

p
=
0.0380

). Constipation at the examination was also a factor contributing to a long SBTT (OR 0.26, 95% CI: 0.07–0.99,

p
=
0.0493

). Conclusion. The SBTT of the SBCE was significantly shorter in the obese patients. This tendency was especially evident in the hospitalized state.
Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization
Authors: Hammam Rania E. , Attar Hani , Amer Ayman , Issa Haitham , Vourganas Ioannis , Solyman Ahmed , Venu P. , Khosravi Mohammad R. , Deif Mohanad A.
Published: May 2022

One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness

R

a

values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments.
Timosaponin AIII Suppresses RAP1 Signaling Pathway to Enhance the Inhibitory Effect of Paclitaxel on Nasopharyngeal Carcinoma
Authors: Li Xiaofeng , Lu Wen , Zhou Tianjiao , Zhao Feng , Yang Li
Published: May 2022

Although PTX has been identified as an effective drug for nasopharyngeal carcinoma (NPC) therapy, it has serious side effects in the human body. Previous studies have shown that timosaponin AIII (TSAIII) can inhibit the malignant progression of NPC cells. This study investigated the active mechanism of the combination of TSAIII and paclitaxel (PTX) on NPC. Cellular viability, apoptosis, apoptotic factors, and RAP1 signaling regulators were detected in the PNC cells (CNE-1 and HNE-2) and the subcutaneous CNE-1 transplanted nude mice treated with PTX or/and TSAIII. The results showed that TSAIII notably strengthened the inhibitory effect of PTX on the proliferation of NPC cells CNE-1 and HNE-2; upregulated the expression of Bax B-cell lymphoma 2 (Bcl-2)/Bcl-xL-associated death promoter (Bad), and Ras-associated protein1 (RAP1) GTPase activating protein (Rap1GAP); inhibited the level of Bcl-2, RAP1, and Ras guanine nucleotide releasing protein (RasGRP2); and significantly enhanced the promoting effect of PTX on apoptosis in the CNE-1 and HNE-2 cells. Besides, TSAIII strengthened the inhibitory effect of PTX on xenograft tumor in nude mice without adverse reactions. In conclusion, the combination administration of TSAIII and PTX had a significantly therapeutic effect on NPC and avoided the PTX’s side effects, which may have acted as a new direction for the study of therapeutic approaches for NPC clinically.
Inhibitory Effect of Lotus Leaf‐Enriched Flavonoid Extract on the Growth of HT‐29 Colon Cancer Cells through the Expression of PI3K‐Related Molecules
Authors: Li Chong , Zhou Zhanming , Long Xingyao , Pan Yanni , Wang Rui , Chen Xiufeng , Zhao Xin
Published: May 2022

Objectives. Lotus leaf is rich in flavonoids, and this study is aimed at examining the inhibitory effect of lotus leaf‐enriched flavonoid extract (LLEFE) on HT‐29 colon cancer cells through phosphatidylinositol 3‐kinase/protein kinase B (PI3K/Akt) expression regulation. Methods. Lotus leaves were extracted by ethanol and purified using FL‐3 macroporous resin to create the LLEFE. HT‐29 colon cancer cells were tested using various methods: their proliferation was observed by 3‐(4,5)‐dimethylthiahiazo(‐z‐y1)‐3,5‐di‐phenytetrazoliumromide (MTT) assay, their survival status was observed by fluorescence staining, their oxidative stress level was observed by biochemical kits, and their mRNA expression was determined by quantitative polymerase chain reaction (qPCR) assay. Additionally, the composition of the flavonoids in lotus leaf was determined by HPLC. Results. The results showed that the proliferation of NCM460 normal human colon cells was not affected by 0–500 μg/mL LLEFE but the proliferation of HT‐29 human colon cancer cells decreased. LLEFE increased the LDH level in an HT‐29 colon cancer cell culture medium; also increased the superoxide dismutase (SOD), catalase (CAT) activities, and glutathione (GSH) level in HT‐29 cells; and decreased the malondialdehyde (MDA) level. Further experimental results showed that LLEFE upregulated the expression of SOD1, CAT, and GSH mRNA and downregulated the expression of PI3K, Akt, and mammalian target of rapamycin (mTOR) in HT‐29 cells. The high‐performance liquid chromatography (HPLC) results showed that kaempferin, hyperoside, astragaloside, phloridzin, and quercetin were the main chemical constituents of lotus leaf. Conclusion. Lotus leaves contain functional flavonoids that inhibit the proliferation of HT‐29 colon cancer cells and regulate the expression of PI3K/Akt through five important chemicals.
An Intrusion Detection Model Based on Improved ACGAN in Big Data Environment
Authors: Liao Jianfeng
Published: May 2022

With the development of big data technology, network intrusion problems against server vulnerabilities emerge one after another. To improve the accuracy of intrusion detection, this paper designs an intrusion detection platform based on the ACGAN (auxiliary classifier generative adversarial network) model in a big data environment. Firstly, by introducing a self-attention mechanism, the global characteristics of attack samples are extracted to improve the quality of generated samples. Then, by adding a gradient penalty, the model's convergence speed and training stability are improved. Finally, this method enhances and expands the attack samples and verifies the dataset. The experimental results show that compared with other comparison methods, the overall detection accuracy of this system is higher, and the false-positive rate and false-negative rate are lower.
Ferulic Acid Protects Human Lens Epithelial Cells against Ionizing Radiation-Induced Oxidative Damage by Activating Nrf2/HO-1 Signal Pathway
Authors: Chen Yueqin , Zhu Lei , Meng Hu , Sun Xiangdong , Xue Chunyan
Published: May 2022

Ionizing radiation- (IR-) induced oxidative stress has been recognized as an important mediator of apoptosis in lens epithelial cells (LECs) and also plays an important role in the pathogenesis of IR-induced cataract. Ferulic acid (FA), a phenolic phytochemical found in many traditional Chinese medicine, has potent radioprotective and antioxidative properties via activating nuclear factor erythroid 2-related factor 2 (Nrf2) signal pathway. The goals of this study were to determine the protective effect of FA against IR-induced oxidative damage on human lens epithelial cells (HLECs) and to elucidate the role of Nrf2 signal pathway. HLECs were subjected to 4 Gy X-ray radiation with or without pretreatment of FA. It was found that FA pretreatment protected HLECs against IR-induced cell apoptosis and reduced levels of ROS and MDA caused by radiation in a dose-dependent manner. IR-dependent attenuated activities of antioxidant enzymes (SOD, CAT, and GPx) and decreased ratio of reduced GSH/GSSG were increased by pretreatment of FA. FA inhibited IR-induced increase of Bax and cleaved caspase-3 and the decrease of Bcl-2 in a dose-dependent manner. Furthermore, FA provoked Nrf2 nuclear translocation and upregulated mRNA and protein expressions of HO-1 in a dose-dependent manner. These findings indicated that FA could effectively protect HLECs against IR-induced apoptosis by activating Nrf2 signal pathway to inhibit oxidative stress, which suggested that FA might have a therapeutic potential in the prevention and alleviation of IR-induced cataract.
DEDGCN: Dual Evolving Dynamic Graph Convolutional Network
Authors: Zhong Fengzhe , Liu Yan , Liu Lian , Zhang Guangsheng , Duan Shunran
Published: May 2022

With the wide application of graph data in many fields, the research of graph representation learning technology has become the focus of scholars’ attention. Especially, dynamic graph representation learning is an important part of solving the problem of change graph in reality. On the one hand, most dynamic graph representation methods focus either on graph structure changes or node embedding changes, ignoring the internal relationship. On the other hand, most dynamic graph neural networks require learn node embeddings from specific tasks, resulting in poor universality of node embeddings and cannot be used in unsupervised tasks. Hence, Dual Evolving Dynamic Graph Convolutional Network (DEDGCN) was proposed to solve the above problems. DEDGCN uses the recurrent neural network to push the evolvement of GCN and nodes, from which it can extract the structural features of dynamic graph and learns the stability features of nodes, respectively, forming an adaptive dynamic graph convolution network. DEDGCN can be classified as unsupervised graph convolutional network. Thus, it is capable of training the unlabeled dynamic graph, it has more extensive application scenarios, and the calculated node embedding has strong generality. We evaluate our proposed method on experimental data in three tasks which are node classification, edge classification, and link prediction. In the classification task, facing the graph with large scale, complex connection relationship, and uncertain change rule, the F1 value of node classification task obtained by DEDGCN reaches 77%, and the F1 value of edge classification task reaches more than 90%. The results show that DEDGCN is effective in capturing graph features, and the effect of DEDGCN is much higher than other baseline methods, which proves the importance of capturing node stability features in dynamic graph representation learning. At the same time, the ability of DEDGCN in unsupervised tasks is further verified by using clustering and anomaly detection tasks, which proves that DEDGCN learning network embedding is widely used.
Nonpolypous Hamartomas of the Gastrointestinal Tract: An Updated Review on Classification, Denominations, and Clinical Management
Authors: Gurzu Simona , Burlacu Diana , Jung Ioan
Published: May 2022

Purpose. To perform the first systematic report about histological subtypes of nonpolypous hamartomas of the gastrointestinal (GI) tract, from esophagus to anal canal. Design. From over 19,000 studies about hamartomas, most of them published as case series or case presentations, we have selected the most representative ones for the GI tract, excluding polyposis syndromes. To have a whole picture of these hamartomas, all of the data were combined with the personal experience of the authors who are GI pathologists. Results. The examined articles showed predominance of vascular and combined vascular and mesenchymal hamartomas. Arteriovenous hamartomas or Brunner gland hamartomas are mainly diagnosed in the small intestine, with preponderance for duodenum. Other malformations such cavernous hamartomas are more specific for the colorectal segments, whereas chondromatous hamartomas or those derived from the neural ectoderm were mostly reported in the esophagus. As newly recognized entities were admitted in the last years, misdiagnosis is frequent, and the best therapeutic approach is far to be known. Conclusion. Even rare, hamartomas of the GI tract need to be differentiated from tumors and familial polyposis syndromes. Knowing their proper denominations and possible complications is valuable for gastroenterologists, pathologists, and surgeons, to be aware in the differential diagnosis.
Effect of Carbon Nanotube Size on Electrical Properties of Cement Mortar under Different Temperatures and Water Content
Authors: Liu Yanwen , Cheng Xu
Published: May 2022

The effects of temperature and water content on the electrical conductivity of cement mortar with different sizes of carbon nanotubes were studied, and the effect of the size of carbon nanotubes on the electrical conductivity of cement mortar was revealed. The results show that the small diameter carbon nanotubes have the best enhancement effect on the electrical conductivity of cement mortar. The electrical conductivity of cement mortar with different diameters of carbon nanotubes is positively correlated with water content, and with the decrease of the diameter of carbon nanotubes in the sample, the effect of water content on the electrical conductivity of carbon nanotube cement mortar becomes smaller and smaller. With the increase of temperature, the electrical conductivity of cement mortar containing carbon nanotubes of different diameters increases in varying degrees, but the increase of samples with small diameter carbon nanotubes is the smallest, indicating that the electrical conductivity of cement mortars with small diameter carbon nanotubes is less affected by temperature.
An LSTM with Differential Structure and Its Application in Action Recognition
Authors: Chen Weifeng , Zheng Fei , Gao Shanping , Hu Kai
Published: May 2022

Because of the broad application of human action recognition technology, action recognition has always been a hot spot in computer vision research. The Long Short-Term Memory (LSTM) network is a classic action recognition algorithm, and many effective hybrid algorithms have been proposed based on basic LSTM infrastructure. Although some progress has been made in accuracy, most of those hybrid algorithms have to have more and more complex structures and deeper network levels. After analyzing the structure of the classic LSTM from the perspective of control theory, we determined that the classic LSTM could strengthen the differential characteristics of human action recognition technology to reflect the change of speed. Thus, an improved LSTM structure with an input differential characteristic module is proposed. Furthermore, in this article, we considered the influence of first-order and second-order differential on the extraction of movement pose information, that is, the influence of movement speed and acceleration on action recognition. We designed four different LSTM units with first-order and second-order differential. Moreover, the experiments were performed for the four units on three common datasets repeatedly. We found that the LSTM network with the input differential feature module proposed in this article can effectively improve action recognition accuracy and stability without deepening the complexity of the network and can be used as a new basic LSTM network architecture.
Priority Roles of Stakeholders for Overcoming the Barriers to Implementing Education 4.0: An Integrated Fermatean Fuzzy Entropy‐Based CRITIC‐CODAS‐SORT Approach
Authors: Gonzales Roselyn , Almacen Rose Mary , Gonzales Gamaliel , Costan Felix , Suladay Decem , Enriquez Lynne , Costan Emily , Atibing Nadine May , Aro Joerabell Lourdes , Evangelista Samantha Shane , Maturan Fatima , Selerio Egberto , Ocampo Lanndon
Published: May 2022

This work defines various stakeholder roles (or strategies) to overcome the barriers to implementing Education 4.0 (EDUC4), which were recently identified in the domain literature. The stakeholder roles are evaluated against these barriers, and such evaluation is structured as a multicriteria sorting problem. To this end, an integrated entropy‐based CRITIC‐CODAS‐SORT under a Fermatean fuzzy (FF) environment addresses epistemic uncertainties inherent in decision‐making. The FF CRITIC assigns the priority weights of the barriers, while the FF CODAS‐SORT determines the high‐priority stakeholder roles. A case of an HEI evaluating 57 possible roles of 5 stakeholders is demonstrated here. Findings suggest the lack of collaboration, apprehensive stakeholders, cybersecurity threats, health issues, and cost as crucial barriers to the HEI. The sorting process yields 13 high‐priority roles, encompassing those within the influence of the government (i.e., cybersecurity awareness, allocation of necessary funds, designing more aligned curricula, and streamlining the basic education agenda), university management (i.e., investing in efficient technologies and forging extensive stakeholder collaboration), human resource function (i.e., implementing periodic skills training for educators), and educators (i.e., engaging in continuous learning about cybersecurity threats, integrating awareness of applicable laws against cyberbullying, devising alternative cost‐efficient teaching strategies, taking part in initiatives to improve curricula, efficient preparation of learning materials, and participating in skills development initiatives). Various managerial insights are offered as inputs to the design of initiatives in EDUC4 implementation.
The Influence of the Number of Joints on the Size Effect of Shear Strength and Characteristic Strength
Authors: Hu Gaojian , Zhang Juan , Liang Wenxu , Huang Rui , Fu Wei
Published: May 2022

The number of joints influences the shear and characteristic strength of structural plane; however, the relationship of the influence is yet to be derived. This study formulates 11 simulation programs using numerical simulation and realistic failure process analysis software. The influence of the number of joints and size on the shear strength of structural plane is studied. The stress-strain curves of different numbers of joints and sizes are analyzed. The mathematical models of the shear strength of structural plane and the number of joints and sizes are proposed, and their specific expressions are obtained. The mathematical models of the shear characteristic size and strength of structural plane and the number of joints are established.
HOTAIRM1 Maintained the Malignant Phenotype of tMSCs Transformed by GSCs via E2F7 by Binding to FUS
Authors: Liu Liang , Zhou Yanling , Dong Xuchen , Li Suwen , Cheng Shan , Li Haoran , Li Yongdong , Yuan Jiaqi , Wang Liping , Dong Jun
Published: May 2022

Objective. Mesenchymal stromal/stem cells (MSCs) are an important part of the glioma microenvironment and are involved in the malignant progression of glioma. In our previous study, we showed that MSCs can be induced to a malignant phenotype (tMSCs) by glioma stem cells (GSCs) in the microenvironment. However, the potential mechanism by which tMSCs maintain their malignant phenotype after malignant transformation has not been fully clarified. Methods. The expression of HOTAIRM1, FUS, and E2F7 was detected by qRT-PCR. Clone formation, EdU, and Transwell assay were used to explore the role of HOTAIRM1, FUS, and E2F7 on the proliferation, migration, and invasion of tMSCs. Bioinformatics analysis and RNA immunoprecipitation were used to explore the relation among HOTAIRM1, FUS, and E2F7. Results. HOTAIRM1 was upregulated in tMSCs compared with MSCs. Loss- and gain-of-function assays showed that HOTAIRM1 promoted the proliferation, migration, and invasion of tMSCs. qRT-PCR and functional assays revealed that E2F7 might be the downstream target of HOTAIRM1. A further study of the mechanism showed that HOTAIRM1 could bind to FUS, an RNA-binding protein (RBP), and thus regulate E2F7, which could promote the malignant phenotype of tMSCs. Conclusion. Our study revealed that the HOTAIRM1/FUS/E2F7 axis is involved in the malignant progression of tMSCs transformed by GSCs in the glioma microenvironment and may function as a novel target for glioma therapy.