bluuebunny/crossref_metadata_2025 huggingface dataset:
On Computational Offloading in Massive MIMO-Enabled Next-Generation Mobile Edge Computing
Authors: AlJubayrin Saad , Khan Muhammad Arslan , Khan Rehan Ali , Khan Javed , Ullah Kalim , Ali Md Yeakub
Published: August 2022

Next-generation wireless communication networks are expected to support massive connectivity with high data rate, low power consumption, and computational latency. However, it can significantly enhance the existing network complexity, which results in high latency. To ease this situation, mobile edge cloud and massive multiple input and multiple output (MIMO) have recently emerged as the effective solutions. Mobile edge cloud has the ability to overcome the constraints of low power and finite computational resources in next-generation communication systems by allowing devices to offload their extensive computation to maximize the computation rate. On the other hand, MIMO can enhance network spectral efficiency by using large number of antenna elements. The integration of mobile edge cloud with massive MIMO also helps to increase the energy efficiency of the devices; as a result, more bits are computed with minimal energy consumption. In this work, a mathematical model is formulated by considering the devices’ energy constraint, which is nonconvex in nature. Following that, to overcome this, we transformed the original optimization problem using the first approximation method and solved the partial offloading schemes. Results reveal that the proposed scheme outperforms the others by considering computational rate as a performance matrix.
DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications
Authors: Gudla Surya Pavan Kumar , Bhoi Sourav Kumar , Nayak Soumya Ranjan , Verma Amit
Published: August 2022

Nowadays, fog computing plays a very vital role in providing many services to end-based IoT (Internet of Things) systems. The end IoT devices communicate with the middle layer fog nodes and to the above cloud layer to process the user tasks. However, this large data communication experiences many security challenges as IoT devices are being compromised and thus the fog nodes at the fog layer are more prone to a very critical attack known as Distributed Denial of Service (DDoS) attack. The attackers or the compromised IoT devices need to be detected well in the network. Deep Learning (DL) plays a prominent role in predicting the end-user behavior by extracting features and classifying the adversary in the network. But, due to IoT device’s constrained nature in computation and storage facilities, DL cannot be administered on those. In this paper, a deep intelligent DDoS attack detection scheme (DI-ADS) is proposed for fog-based IoT applications. The framework mainly uses a deep learning model (DLM) to detect DDoS attacks in the network. The DLM is installed on the computation module of the fog node that predicts the end IoT device behavior. For the selection of the best DLM model at the fog layer, the performance comparison is made on Deep Neural Multilayer Perceptron (DNMLP) and Long Short-Term Memory (LSTM) models along with the conventional machine learning (ML) models such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression (LR), and Random Forest (RF). The simulation is performed using the Python Anaconda platform by considering a new DDoS-SDN (Mendeley Dataset) dataset that consists of three DDoS attacks such as TCP Syn, UDP Flood, and ICMP attacks. From the results, DNMLP showed the best accuracy of 99.44% as compared to other DL and ML models. By outperforming nature in the detection of DDoS attacks, DNMLP is considered in the proposed framework for being implemented at the fog layer.
Mechanistic Investigation of Curcuma Protection against Oral Submucous Fibrosis
Authors: Peng Haiyan , Jiang Xiaowen , Cui Linna , Zhu Yali , Ye Zhikui , Zhang Zhiming
Published: August 2022

Objective. Oral submucous fibrosis (OSMF) is a chronic, fibrotic disease that affects the oral cavity, showing a high rate of malignant transformation. Curcuma exerts therapeutic potentials in many diseases including OSMF. However, the potential targets and pathways to explain the therapeutic effects of curcuma on OSMF are outside the scope of present knowledge. Herein we intend to reveal the predictive targets and potential pathways of curcuma against OSMF by a network pharmacology-based approach followed by molecular docking technology. Methods. We searched the SymMap, GeneCards, and OMIM database to obtain curcuma and OSMF common targets. The protein-protein interaction (PPI) of curcuma and OSMF common targets were then analyzed, followed by functional enrichment analysis. The best binding mode of curcuma and target proteins was analyzed by molecular docking technology. Results. We collected 290 putative targets of curcuma molecules and 600 known therapeutic targets of OSMF, with 64 curcuma and OSMF common targets sorted out. In the PPI network, there were 63 nodes with 922 edges. The node indicates protein and the line indicates PPI relation. The most enriched GO term in the BP level is “gland development”, followed by “cellular response to chemical stress”, and then “response to oxygen levels”, while the most enriched GO term in CC and MF is “membrane raft” and “cytokine receptor binding”, respectively. We also found 131 KEGG pathways significantly enriched by curcuma and OSMF common targets. The binding energy of curcuma to ALB, TNF, TP53, IL6, and VEGFA was −9.5 kcal/mol, −3.9 kcal/mol, −3.5 kcal/mol, −3.6 kcal/mol, and −8.9 kcal/mol, respectively, which suggested ALB and VEGFA were regarded as main targets involving in the potential mechanism of curcuma against OSMF. Conclusion. The present study illustrated that the therapeutic effects of curcuma on OSMF were achieved by targeting ALB and VEGFA, which giving reference to further drug design and development for OSMF.
The Efficacy and Safety of Dexamethasone Intravitreal Implant for Diabetic Macular Edema and Macular Edema Secondary to Retinal Vein Occlusion: A Meta-Analysis of Randomized Controlled Trials
Authors: Xiaodong Li , Xuejun Xie
Published: August 2022

Purpose. The purpose of this meta-analysis was to evaluate the efficacy and safety of dexamethasone intravitreal implant (DEX) for the treatment of diabetic macular edema (DME) with retinal vein occlusion secondary to macular edema (RVO-ME). Materials and Methods. Relevant databases were searched to include randomized controlled trials (RCTs) evaluating DEX for DME and RVO-ME. The search was conducted until March 2022. Meta-analysis was performed using Rev Man 5.4.1 software after screening the literature by inclusion and exclusion criteria, extracting information, and evaluating the methodological quality of the included studies. Results. The study showed that DEX treatment of RVO-ME was associated with an improvement in best corrected visual acuity (BCVA) (MD = −9.08, 95% CI: −10.89–7.27,

P
<
0.00001

) and central retinal thickness (CRT) (MD = 93.47, 95% CI: 28.55–159.39,

P
=
0.005

). DEX treatment of DME was significantly better than anti-VEGF treatment in terms of CRT reduction (MD = −72.35, 95% CI: −115.0–29.69,

P
=
0.0009

). The safety study showed that the risk of cataract from RVO-ME (OR = 5.06, 95% CI: 1.96 to 13.06,

P
=
0.0008

) and the incidence of high intraocular pressure (OR = 6.67, 95% CI: 3.46 to 12.86,

P
<
0.00001

) were significantly higher with DEX than with anti-VEGF therapy. The risk of cataract from DME (OR = 4.70, 95% CI: 2.10 to 10.54,

P
=
0.0002

2) was significantly higher with DEX than with anti-VEGF therapy (OR = 4.70, 95% CI: 2.10 to 10.54,

P
=
0.0002

). The incidence of high IOP (OR = 13.77, 95% CI: 4.96 to 38.18,

P
<
0.00001

) was significantly higher with DEX than with anti-VEGF therapy. Conclusions. In patients with DME and RVO-ME, DEX was more efficacious but slightly less safe than anti-VEGF therapy.
Big Data-Driven Product Innovation Design Modeling and System Construction Method
Authors: Xue Huicong , Wu Depei
Published: August 2022

In order to improve the image quality of innovative design of manufacturing products, reduce the dependence on experts, increase the amount of research data, and accurately sort and select the best alternatives, this paper proposes the KENPI method, which integrates perceptual engineering and neural style transfer, normalizes the content map through nm model, realizes style transfer, and generates new product images. Use ORDD perceptual engineering to collect a large number of perceptual word data, establish product semantic space, use TF-EPA to obtain perceptual words, and use word clustering combined with degree adverbs to evaluate the sensibility of products. Under the KE-GRA-TOPSIS method, considering user preferences, accurately sort and select the product design alternatives with multiple criteria, and establish the auxiliary system of product innovative design. The experimental results show that the style transfer effect of nm model is better, the style intensity of the product is enhanced, and the average texture evaluation of sample 3 is increased by 0.30 points. The average absolute value of DOD phrase in BP neural network is 0.0765, which is lower than the MLR method, and the performance of the former is better than the latter. The relative closeness of A6 scheme under KE-GRA-TOPSIS method is 0.57, which is 0.02 higher than the KT method, indicating that the KE-GRA-TOPSIS method is better than the KT method. The research improves the way of obtaining user demand data, enhances the strength of and product style, and improves the competitiveness of products.
Efficacy of Laparoscopic Radical Resection Combined with Neoadjuvant Chemotherapy and Its Impact on Long-Term Prognosis of Patients with Colorectal Cancer
Authors: Huang Liang , Xu Xijuan , Shao Jinfan , Hong Weiwen , Yu Wenfeng
Published: August 2022

Objective. The aim of the study is to examine the efficacy of laparoscopic radical resection of colorectal cancer combined with neoadjuvant chemotherapy and its impact on the overall prognosis of patients with colorectal cancer (CC). Methods. A total of 80 CC patients hospitalized and treated at our hospital between November 2019 and June 2021 were selected at random as research subjects and divided equally into two groups: the surgical group (n = 40) and the combination group (n = 40). Patients in the surgical group were treated with laparoscopic radical resection, while patients in the combination group received laparoscopic radical resection combined with neoadjuvant chemotherapy. The two groups were compared in terms of surgery-related indicators, tumor markers (serum carcinoembryonic antigen (CEA), glycoprotein 199 (CA199), vascular endothelial growth factor (VEGF), and matrix metalloproteinase 9 (MMP9)), postoperative complications, and 1–3 years postoperative survival rate and recurrence rate. Results. The surgical duration of the combination group was significantly shorter than the surgical group (

P
<
0.05

). No significant differences were found in intraoperative blood loss, time to get out of bed, exhaust time, or hospital stay between the two groups (

P
<
0.05

). In the combination group, serum tumor markers (carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), vascular endothelial growth factor (VEGF), and matrix metalloproteinase 9 (MMP9)) were markedly lower than those in the surgical group (

P
<
0.05

). The combination group exhibited fewer postoperative complications than those in the operation group (

P
<
0.05

). In the combination group, the 1–3 years postoperative survival rate was higher, while the 1–3 years postoperative recurrence rate was considerably lower than that in the surgical group (

P
<
0.05

). Conclusion. CC patients benefit well from laparoscopic radical resection coupled with neoadjuvant chemotherapy. The approach is efficient in lowering blood tumor markers in patients and lowering the risk of surgery-related complications. It has the potential to enhance patients’ long-term prognoses, allowing them to live longer and lower their chance of recurrence.
Effect of Human-Oriented Fine Nursing on Psychological Emotion, Life Quality, and Nursing Satisfaction of Patients Undergoing Laparoscopic Radical Nephrectomy
Authors: Yu Hong , Dong Wei
Published: August 2022

Purpose. This study set out to explore the therapeutic effects of human-oriented fine nursing intervention on patients undergoing laparoscopic radical nephrectomy and its influence on patients’ quality of life. Methods. A total of 120 patients who underwent laparoscopic radical nephrectomy in Sheng Jing Hospital of China Medical University from January 2019 to January 2020 were randomized into group A (n = 60) and group B (n = 60) by the random table method. Group B was treated with routine nursing while group A was additionally treated with human-oriented fine nursing. Meanwhile, the study compared the two groups in terms of vital signs, postoperative recovery time, visual analogue scale (VAS), sleep quality (SQ), adverse psychological response score, and quality of life (QOL). Results. According to our findings, patients of group A had significantly lower heart rate, blood pressure, and adverse psychological response scores compared to those of group B; the postoperative recovery time of group A was considerably shorter than that of group B; the VAS scores of group A at 3 and 6 days after surgery were markedly lower than those of group B; group A had a remarkably higher number of patients with excellent postoperative SQ than group B; and group A saw substantially higher QOL scores of patients compared to group B. Conclusion. Results of this study suggested that fine nursing intervention based on humanistic care for patients undergoing laparoscopic radical nephrectomy can improve the physical condition of patients, refine their mentalities, and improve their sleep quality, thus enhancing their quality of life and accelerating the recovery process. Therefore, this nursing mode is worthy of promotion and application in clinical practice.
Sports Medical Image Modeling of Injury Prevention in Wushu Training
Authors: Feng Yuanyuan
Published: August 2022

In order to solve the problem of injury prevention in Wushu training, this paper proposes a research on modeling using sports medical images. The main content of this technology research is to drive the muscle strength modeling method based on the sports medical image data. According to the acquisition of MRI/CT images, through the research and application of DFIS, it is concluded that the research on sports medical image modeling has a high accuracy for injury prevention in Wushu training. The experimental results show that translation

accuracy
≤
0.03

~0.27,

rotation
 
accuracy
≤
0.24

~0.63,

flexion
 
and
 
extension
 
accuracy
≤
0.1

,

translation
 
retest
 
error
≤
0.2

~1.2,

rotation
 
retest
 
error
≤
0.5

~3.4,

rotation
 
retest
 
accuracy
≤
0.4

~1.04, and sports medical images have a high accuracy for injury prevention in Wushu training. It is proved that the research on sports medical image modeling is effective and accurate for the problem of injury prevention in Wushu training.
Effectiveness and Safety of Combination Therapy with Herbal Medicine and Growth Hormone Compared to Growth Hormone Monotherapy for Short Stature Children: A Systematic Review and Meta-Analysis
Authors: Shim Soo Bo , Ahn Hye Li , Lee Hyun Hee , Lee Ju Ah , Lee Hye Lim
Published: August 2022

Introduction. Herbal medicines (HM) and growth hormones (GH) are widely used to treat short stature (SS) in children. This systematic review aimed to evaluate the effectiveness and safety of combination therapy with HM and GH (CHG) compared to those of GH monotherapy (GHM) in children with SS. Methods. We searched 17 electronic databases from inception to 1 April 2021. Only randomized controlled trials (RCTs) were included. Two authors independently performed the selection and quality assessment of the included studies using Cochrane Handbook criteria. Relative risk (RR) was used to measure dichotomous outcomes with a 95% confidence interval (CI). Mean difference (MD) or standard MD (SMD) was used to measure continuous outcomes with a 95% CI. Results. Seven RCTs involving 455 participants with SS were included. Standard deviations in height (MD = 0.31, 95% CI: 0.24–0.38,

p
<
0.00001

), and insulin-like growth factor binding protein-3 (MD = 1.39, 95% CI: 0.93–1.85,

p
<
0.00001

) were significantly higher in the CHG group than in the GHM group. Growth velocity (MD = 1.82, 95% CI: 1.34–2.31,

p
<
0.00001

) and insulin-like growth factor-1 (MD = 61.85, 95% CI: 55.80–67.90,

p
<
0.00001

) were significantly higher in the CHG group. Adverse events were significantly lower in the CHG group (risk ratio: 0.10, 95% CI: 0.02–0.54,

p
=
0.007

). However, the level of evidence was low. Conclusions. CHG demonstrated significantly better efficacy than GHM for treating SS, with a low incidence of adverse events. However, since the level of evidence is low, methodologically standardized RCTs are required to verify these results.
Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care
Authors: Lu Shan
Published: August 2022

In order to alleviate the “difficulty in seeing a doctor” for the masses, continuously optimize the service process, and explore new financial service processes for admission and discharge, this study proposes a cloud-fog hybrid model UCNN-BN based on an improved convolutional neural network and applies it to financial services in smart medical care. Decision-making applications: this research improves and designs the UCNN network based on AlexNet and introduces small convolution layers to form convolution groups, making the network more adjustable. The network structure is simpler and more flexible, and it is easy to adjust the algorithm. The number of parameters is small, and it can be directly superimposed without having to add new network hidden layers. The experimental results show that the recognition rate of the UCNN network on the FER2013 and CK+ datasets is higher than that of other recognition methods, and the recognition rates on the FER2013 and CK+ datasets are 98% and 68.01%, due to other methods. This shows that the improved convolutional neural network used in this study for financial services in smart medical care has certain applicability, and small convolution kernels help to extract more subtle features, so as to identify more accurately.
A Decision-Making System for Low Carbon Manufacturing
Authors: Ma Changsong , Yuan Tiantong , Yao Yuzhong , Wang Lixu , Gao Hanyao , Zhang Ming , Liang zhou  , Li Jiaonan , Guan Lian
Published: August 2022

Based on the stochastic market demand of products, this paper studies the low-carbon manufacturing decisions making of manufacturing enterprises considering downward substitution and green technology input under the carbon cap-and-trade policy. The results show that the government’s carbon trade policy will have a great impact on the production of manufacturing enterprises. Therefore, manufacturing enterprises must attach importance to the constraints of the government’s carbon emission reduction policies. In terms of it, there are strategies for manufacturing enterprises such as adjusting the output, trading the carbon emission right, and so on. On this case, green technology input can increase the expected profit of manufacturing enterprises, especially in the case of downward substitution.
[Retracted] Effect of Supplementation of Vitamin D in Patients with Periodontitis Evaluated before and after Nonsurgical Therapy
Authors: Mishra Shree Mohan , Ravishankar P. L. , Pramod V. , Rajula Prem Blaisie , Gayathri K. , Alam Mohammad Khursheed , Raj A. Thirumal , Bhandi Shilpa , Patil Shankargouda
Published: August 2022

Background. Vitamin D has anti‐inflammatory properties and the potential to increase the generation of antimicrobial peptides like cathelicidin and defensins that may have a good impact on oral health. Higher vitamin D consumption has also been linked to a reduced risk of periodontal disease progression. Hence, the primary objective of this study was to evaluate and compare the clinical and laboratory parameters of oral supplementation of vitamin D as an adjuvant to scaling and root planing and to assess the bone mineral density via qualitative ultrasound bone density scanner in chronic periodontitis patients. Methodology. This study included 40 patients with periodontitis categorized into 2 groups with twenty patients each, Group I comprising scaling and root planing (SRP) alone and Group II comprising SRP along with vitamin D supplementation. Plaque index, gingival index, probing pocket depth, and clinical attachment loss was measured as clinical parameters. Serum vitamin D levels were assessed before and after SRP at both baseline and 6 weeks. Results. The intergroup comparison of clinical parameters (PI, GI, PPD, and CAL) at 6 weeks for both the groups showed statistical significance. Intragroup comparison of clinical parameters from baseline to 6 weeks showed a statistically significant reduction in both groups. The mean bone mineral density level in both the control and test groups demonstrated a mean T score of –1.3 and –1.21, respectively. The mean vitamin D levels were 27.8460 and 28.1020 for the test and control groups, respectively, which was statistically insignificant (p = 0.705) and those at six‐week intervals improved to 31.3650 and 28.0240 which were statistically significant (p ≤ 0.001). Conclusion. It could be stated that a positive relationship exists between periodontitis and osteopenia which could aggravate periodontal destruction. All periodontitis cases should thus be evaluated for BMD and supplemented with vitamin D3 in an appropriate dosage and time frame to treat both these diseases.
Correlation between 24-Hour Ambulatory Blood Pressure Variability and White Matter Lesions in Patients with Cerebral Small Vascular Disease: A Cross-Sectional Study
Authors: Liu Ping , Yin Changhao , Liu Meilingzi , Chang Lu , Wu Tianjiao , Li Zihao , Luo Ruidi , Du Xiao , Zhao Weina
Published: August 2022

Objective. The goals of this study are to assess the correlation between 24-hour ambulatory blood pressure (BP) variability and white matter lesions (WML) in patients with cerebral small vascular disease (CSVD) and to provide guidance for the prevention of WML. Methods. A total of 136 patients diagnosed with CSVD and essential hypertension were recruited and divided into two groups. The Fazekas scale was used to quantify the severity of WML. The basic information, BP levels, BP variability, and circadian rhythm changes across these groups were recorded and compared. Results. The control group consisted of 40 subjects without WML (

Fazekas
 
score
=
0

), and the WML group was composed of 96 patients with WML (

Fazekas
 
score
≥
1

). Patients in the WML group were then divided into three subgroups: mild WML (

n
=
43

,

Fazekas
 
score
=
1

), moderate WML (

n
=
24

,

Fazekas
 
score
=
2

), and severe WML (

n
=
29

,

Fazekas
 
score
=
3
–
4

). Age, history of diabetes, and serum uric acid levels were significantly increased between the WML and control groups (

P
<
0.05

). The levels of 24-hour mean diastolic BP (

F
=
3.158

,

P
=
0.026

) and daytime mean systolic BP (

F
=
3.526

,

P
=
0.017

) were significantly increased between the control and WML groups. There was no significant difference in the rhythmic classification of BP among all groups (

P
>
0.05

). An ordered multinomial logistic regression analysis revealed that age, triglyceride levels, and nondipper BP were independent risk factors in WML. Conclusion. Age, history of diabetes, serum uric acid levels, 24-hour mean systolic level, and daily mean systolic BP level were significantly increased between the WML and control groups. Age, triglyceride levels, and nondipper BP were independent risk factors in WML in patients with CSVD, while the 24-hour dynamic blood pressure standard deviation and 24-hour dynamic blood pressure coefficient of variation were not associated with the occurrence of WML.
DOA Estimation for Noncircular Signals under Strong Impulsive Noise
Authors: An Chunlian , Yang Guyue , Tian Liangliang , Song Jing , Wang Yi
Published: August 2022

Direction of arrival (DOA) estimation under impulsive noise has been an important research area. Most present methods are based on the fractional lower order statistics, while the computation load is heavy, and the estimation property degrades when the noise impact is strong. To get around this conundrum, a novel solution is presented, where the noncircular signals are introduced for modelling, a filtering preprocessing method is introduced to eliminate the impulsive noise, and a matrix reconstruction method is presented to smooth the residual noise. Firstly, the filtering preprocessing method is implemented to cut out the impulsive noise. Secondly, the characteristic of the noncircular signal is utilized to extend the array aperture. Thirdly, a new matrix reconstruction method is proposed to smooth the residual noise. Finally, the classical ESPRIT algorithm is adopted to estimate the DOAs. Simulations under different comparison of dimensions are conducted, and the mainstream methods are selected as comparison. The simulation results illustrate the outstanding performance of the proposed method in a strong impulsive noise environment.
Protein Regulator of Cytokinesis 1 (PRC1) Upregulation Promotes Immune Suppression in Liver Hepatocellular Carcinoma
Authors: Zhang Canjing , Xu Huiwen , Sui Xianxian , Wu Ting , Chen Bobin , Wang Songmei , Wang Xuanyi
Published: August 2022

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with widespread prevalence. The suppressive immune environment causes largely refractory to current treatment. The protein regulator of cytokinesis 1 (PRC1) is an essential gene for cytokinesis and is involved in cancer pathogenesis. However, the functions of PRC1 have been barely clarified, especially in LIHC. Here, we investigated the expression, prognostic value, and functions of PRC1 in LIHC. Pan-cancer analysis revealed the overexpression of PRC1 in the Cancer Genome Atlas (TCGA) database. Four LIHC datasets from the Gene Expression Omnibus (GEO) database confirmed the PRC1 overexpression in LIHC. The mRNA and protein levels of PRC1 in LIHC cells were higher than in normal liver cells. The overexpression of PRC1 predicted progressed clinical stage and poor prognosis of LIHC. We further investigated the functions of PRC1 by performing the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and Gene Set Enrichment Analysis (GSEA) of its coexpressing genes. High PRC1 expression was associated with increased genome instability of LIHC. Moreover, PRC1 was positively correlated with the infiltration of suppressive immune cells like T regulatory cells (Tregs) and polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and was negatively correlated with the effector immune cells’ infiltration, including B cells and CD8+ T cells. In addition, PRC1 was positively correlated with the expression of tumor immune checkpoint molecules. Taken together, PRC1 overexpression contributes to the genome instability and the suppressive immune microenvironment of LIHC. Thus, PRC1 has the potential to be a prognostic marker and therapeutic target of LIHC.
Risk Factors for Recurrence of Gestational Diabetes Mellitus and Its Correlation with Maternal and Infant Prognosis
Authors: Wei Xiaohui , Hu Ruihong , Gao Ying , Yu Zhongqin , Zhang Xin
Published: August 2022

Objective. To explore and analyze the risk factors of recurrence of gestational diabetes mellitus (GDM) and its correlation with maternal and infant prognosis. Methods. The clinical data of 128 GDM patients admitted to our hospital from May 2018 to May 2020 were retrospectively analyzed, and they were divided into a recurrence group (n = 65) and a nonrecurrence group (n = 63) according to the presence or absence of recurrence after one year of follow-up. The general data and clinical data of the two groups of patients were compared by single factor, and the factors with statistical significance were analyzed by logistic regression, and the maternal and infant outcomes and prognosis of the two groups of patients were compared. Results. Compared with the nonrecurrence group, the recurrence group had a higher proportion of patients aged ≥35 years, with first fasting blood glucose ≥7.0 mmol/L, and with BMI value index ≥25 kg/m2 during repregnancy, and the differences were statistically significant (

p
<
0.05

). Multivariate logistic regression analysis showed that elder maternal age, high blood glucose level in the previous pregnancy, and high BMI index during this pregnancy were all high-risk factors for GDM recurrence (

p
<
0.05

). Compared with the nonrecurrence group, the recurrence group had a lower rate of vaginal delivery, lower rate of premature rupture of membranes, lower rate of premature birth, lower rate of macrosomia, lower rate of neonatal asphyxia, lower rate of postpartum hemorrhage, and lower Apgar score within 1 minute of delivery (

p
<
0.05

). Conclusion. Older maternal age, high blood glucose level in the previous pregnancy, and high BMI index during the present pregnancy are high-risk factors for GDM recurrence that can further lead to adverse outcomes for mothers and infants. Clinicians should place sufficient emphasis on targeted early measures responding to high-risk factors to minimize the risk of GDM recurrence and optimize maternal and infant outcomes.
English Interpretation Learning System Combining Cognitive Ability and Collaborative Filtering Algorithm
Authors: Liang Yahong
Published: August 2022

In the context of globalization, English interpretation is the language hub for political and economic exchanges in different countries. English interpretation is a technology different from the English translation, which requires translation accuracy and real-time. Traditional English interpreting learning methods can only learn English grammar and some English language and cultural information. At the same time, traditional English interpreting learning methods are inefficient and cumbersome tasks. English interpreting participants also need to interpret accurately in real-time based on the speaker’s behavioral information and the cultural information contained in English. This puts forward more requirements for the learning of English interpreting learners. This research uses the collaborative filtering (CF) algorithm and the algorithm based on ConvLSTM cognitive ability to study the three characteristics of English grammar, behavioral information, and English cultural information in the English interpreting learning system. The CF algorithm can recommend effective English knowledge for English interpreting learners, and the ConvLSTM algorithm can interpret the effectiveness of this recommended knowledge. The research results show that the CF algorithm and the ConvLSTM algorithm have good performance in recommending and predicting the grammar, behavioral information, and cultural characteristics of the English interpreting learning system. The largest similarity index reached 0.97, and the smallest similarity index also reached 0.93. ConvLSTM can better predict the changing trend and data value size of English interpretation-related feature data, and the largest prediction error is only 2.48%.
Association of ANKRD55 Gene Polymorphism with HT: A Protective Factor for Disease Susceptibility
Authors: Fang Yu-die , Zhao Jing , Zhuang Xin-juan , Xu Jian-bin , Cai Tian-tian , Yang Xiao-rong , Mu Kai-da , Zhang Jin-an
Published: August 2022

Purpose. Recent studies have shown that Ankyrin Repeat Domain 55 (ANKRD55) gene polymorphism is a risk factor for multiple autoimmune diseases, but its association with autoimmune thyroid diseases (AITDs) has not been reported. The purpose of this study was to investigate the potential relationship between polymorphism of the ANKRD55 gene and AITDs. Methods. For this study, we enrolled 2050 subjects, consisting of 1220 patients with AITD and 830 healthy subjects. Five loci (rs321776, rs191205, rs7731626, rs415407, and rs159572) of the ANKRD55 gene were genotyped using Multiplex PCR combined with high-throughput sequencing. Results. The results showed that the allele frequencies of rs7731626 and rs159572 loci in HT patients were lower than those in normal controls (

P
=
0.048

and

P
=
0.03

, respectively). In different genetic model analyses, rs7731626 and rs159572 were also significantly correlated with HT in allele, dominant and additive models before and after age and sex adjustment. There were no differences in rs321776, rs191205, or rs415407 of the ANKRD55 gene in allele frequency or genotype frequency between AITDs patients and controls. Conclusions. This study for the first time found that rs7731626 and rs159572 of ANKRD55 were significantly correlated with HT, and individuals carrying the A allele at these two loci had a lower probability of developing HT.
Neutrosophic Cost Pattern of Inventory System with Novel Demand Incorporating Deterioration and Discount on Defective Items Using Particle Swarm Algorithm
Authors: Bhavani G. Durga , Georgise Fasika Bete , Mahapatra G. S. , Maneckshaw B.
Published: August 2022

The potential to obtain defective or damaged items with non-defective commodities is common to experience at the production unit or when shipping products from one layer to another. This research focuses on the faulty things that retailers receive from suppliers. The retailer has set a restriction on the percentage of defective things, and the retailer receives a discount on the cost of purchasing defective items. The proposed inventory system handles the uncertainty in inventory costs and also considers the demand and deterioration of items with prioritized maximum product life. This work minimizes total inventory cost when demand rate as a function of reliability and power pattern of time under a crisp and triangular neutrosophic environment. The inventory system for degrading items considers the predictability and power pattern of time with a reasonable payment delay. The interest charges are applied only after a specific permissible time limit in the proposed inventory system. The neutrosophic number that defines three different kinds of membership functions representing the truth, hesitation, and falseness is applied in the inventory model in handling the uncertainty of the cost pattern. The proposed inventory model is investigated using a particle swarm optimization algorithm, and the results are validated using a numerical example and a sensitivity analysis for various parameters.
Study on Impact Test System of Manipulator Based on Six Axis Force Sensor and Intelligent Controller
Authors: Hua Guoxin , Wang Fei , Zhang Jianhui
Published: August 2022

With the development and application of intelligent nervous system, intelligent control system represented by machine vision has been promoted and used in various fields, including manipulator control system. Mechanical arm is the main actuator of robot and other mechanical components, but the application environment of mechanical arm is extremely complex, and with the development of industrial process, the sensitivity of technology and operating system for its arbitrary posture operation is also steadily improved. Based on the above problems, this paper designs a manipulator control system based on machine vision theory and orthogonal parallel six-dimensional force sensor to meet the working requirements of manipulator in high-precision environment. Systems hardware are consisted by machine vision structure and six dimensions of force sensor. The practical application effect of the model was verified by collision detection experiment. The relevant research can provide theoretical guidance and practical reference for the research of manipulator control field. Dance and practical application reference for the research of manipulator control field.
Anti-Inflammatory and Anti-Colon Cancer Activities of Mung Bean Grown in Burkina Faso
Authors: Kabré Wendmintiri Jeanne d’Arc , Dah-Nouvlessounon Durand , Hama Fatoumata/Ba , Kohonou N. Arnaud , Sina Haziz , Senou Maximin , Baba-Moussa Lamine , Savadogo Aly
Published: August 2022

Widely used in traditional medicine in Asia and recently introduced in Burkina Faso under the name Beng-tigré, mung bean is a legume consumed throughout the world and more so in India. The objective of this study was to evaluate the cytotoxicity of the mung bean grown and consumed in Burkina Faso and to study its biological properties such as anti-inflammatory and anticancer activity of the natural and sprouted seeds. The cytotoxicity of the extracts was tested on Artemia salina larvae, and the anti-inflammatory activity was evaluated in vitro by albumin denaturation method using diclofenac as reference molecule. The anticancer activity of hydro-ethanol extracts was evaluated on rats made cancerous with 1,2-dimethylhydrazine (DMH) using 5-fluorouracil as reference molecule. The results showed that the highest yield of the plant extraction was observed with the hydro-ethanol solvent, both for the natural form of mung bean (MBN) and for its sprouted form (MBG). The cytotoxicity test showed no toxicity of the extracts toward shrimp larvae. The ethanolic extract of germinated mung bean seeds gave the highest anti-inflammatory activity at 95.13 ± 0.22% inhibition with significant difference (

p
<
0.05

) between the extracts. Cancer induction with DMH was inhibited by both MBN and MBG extracts. The test of preventive effects of the extracts showed the best activity with significant difference in biochemical results. These results confirm that the mung bean grown in Burkina Faso, as a nontoxic legume, is a functional food that can be integrated into the population’s dietary habits for a double interest. Moreover, they open perspectives for the research of active principles of plant origin with anti-inflammatory and anticancer properties.
Practical Research on Primary Mathematics Teaching Based on Deep Learning
Authors: Tian Xiuling , Zhao Jianan , Nguyen Kevin T.
Published: August 2022

With the rapid development of the knowledge economy, learning that stays at a shallow level is no longer able to meet the challenges of the times and deep learning has come into being. The key to deep learning is to develop students’ critical thinking, information integration, communication and collaboration, and constructive reflection skills, which can be cultivated in the process of problem solving. By exploring the theories of deep learning and problem solving, the basic elements of deep learning theory are incorporated into the design system of problem-solving teaching, and the problem-solving teaching model based on deep learning is proposed in conjunction with the content of elementary school mathematics, so that students can further develop their core literacy based on acquiring deep learning skills.
Two-Dimensional DOA Estimation for Coprime Planar Arrays Based on Self-Correlation Tensor
Authors: Li Hao , Cui Weijia , Jian Chunxiao , Xu Haiyun , Mei Fengtong
Published: August 2022

In the coprime planar array (CPA), the existing tensor DOA estimation has the problem that the statistics are not fully utilized. We propose a two-dimensional DOA estimation method based on tensor self-correlation, which realizes the high-resolution and high-precision joint estimation of elevation angle and azimuth angle. Firstly, we represent the received signals of two subarrays with tensors and then obtain the self-correlation covariance tensor of the subarrays themselves and the cross-correlation covariance tensor of the two subarrays. Then, we extract the covariance tensor corresponding to the maximum continuous virtual array and prove the expression of the maximum continuous virtual array aperture of the proposed method. Compared with the existing methods, the proposed method effectively improves the maximum aperture of the continuous virtual array. Finally, the signal subspace is solved by tensor expansion and tensor decomposition. Simulation results show that under the same conditions, the proposed method has higher estimation accuracy and degree of freedom than the cross-correlation tensor method, and the resolution is also improved significantly.
Antiocclusion Visual Tracking Algorithm Combining Fully Convolutional Siamese Network and Correlation Filtering
Authors: Tao Xiaomiao , Wu Kaijun , Wang Yongshun , Li Panfeng , Huang Tao , Bai Chenshuai
Published: August 2022

Machine learning only uses single-channel grayscale features to model the target, and the filter solution process is relatively simple. When the target has a large change relative to the initial frame, the tracking effect is poor. When there is the same kind of target interference in the target search area, the tracking results will be poor. The tracking algorithm based on the fully convolutional Siamese network can solve these problems. By learning the similarity measurement function, the similarity between the template and the target search area is evaluated, and the target area is found according to the similarity. It adopts offline pre-training and does not update online for tracking, which has a faster tracking speed. According to this study, (1) considering the accuracy and speed, the target tracking algorithm based on correlation filtering performs well. A sample adaptive update model is introduced to eliminate unreliable samples, which effectively enhances the reliability of training samples. With simultaneous changes in illumination and scale, fast motion and in-plane rotation IPR can still be maintained. (2) Determined by calculating the Hessian matrix, in the Struck function, Bike3 parameter adjustment can achieve fast tracking, and Boat5 ensures that the system stability is maintained in the presence of interference factors. The position of the highest scoring point in the fine similarity score map of the same size as the search image is obtained by bicubic interpolation as the target position. (3) The parallax discontinuity caused by the object boundary cannot be directly processed as a smooth continuous parallax. The MeanShift vector obtained by calculating the target template feature and the feature to be searched can increase the accuracy by 53.1%, reduce the robustness by 31.8%, and reduce the error by 28.6% in the SiamVGG algorithm.
[Retracted] Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction
Authors: Zhou Jian , Li Jun
Published: August 2022

The blockage of blood in the vessels results in heart attacks and cardiac arrests which are referred to as myocardial infarction. Early detection of such infarction is feasible through percutaneous coronary intervention (PCI) based on electrocardiogram (ECG) monitoring. The variations in blood flow and clot are precisely observed through periodic ECG monitoring and previous correlations. This article introduces a concentrated value assessment model (CVAM) for determining PCI levels in treating myocardial infarction. The ECG observations from the previous observation sessions are accumulated and organized for validating the infarction rate. This requires the accompanying concentrated data like a heartbeat, blood pressure, and flow rate observed in different sessions. Based on the session observation and normal data correlation, the PCI level is recommended for the patient. In this analysis process, the value shift due to blocks and high and low blood pressure is accounted for through the deep learning paradigm. This paradigm correlates the above factors with the ECG values for precisely determining PCI from the last known concentration. The learning paradigm is trained based on session and normal observation data through different intervals. This model is validated using the metrics precision, analysis rate, diagnosis recommendation, and complexity.
Effectiveness of Network Classroom Teaching Based on Genetic Algorithm
Authors: Zou Chunjie , Wang Weijuan , Zhu Libo
Published: August 2022

In online classroom teaching, the function of teaching system can play an important role in the effectiveness of classroom teaching. How to use genetic algorithm to optimize online classroom teaching system has become a research hotspot. Based on genetic algorithm, this paper proposes an adaptive genetic algorithm model based on the traditional algorithm. After setting the appropriate mutation probability, the model can improve the convergence speed. Moreover, based on adaptive genetic algorithm, combined with the direct value method and BT neural network theory, this paper constructs the online classroom teaching quality evaluation model and the teaching system test paper data model, and optimizes adaptive mutation genetic algorithm and BP neural network to evaluate the teaching effectiveness. Simulation experiments are carried out based on the algorithm model, and the visual parameter values are obtained. After experimental comparison, the initial value of the mutation rate is set between 0.002 and 0.004. For the network classroom teaching system, this paper introduces the system demand analysis, function module design, and database design in detail. Finally, through the questionnaire survey, this paper understands the network situation of students in class and the use of online classroom teaching platform in detail, analyzes the problems and influencing factors of online teaching, and finally puts forward the strategies to improve the effectiveness of online classroom teaching.
Experimental Study on Stability of Long‐Span PC Cable‐Stayed Bridge during the Construction Periods
Authors: Zeng Yong , Wang Yuxiao , Shi Zhenwei , Tan Hongmei , Gu Anbang
Published: August 2022

With the increasing span of cable‐stayed bridges, the towers are getting higher and higher, and the stiffening beams are becoming more and more slender. The increase in span causes the axial pressure of the beams and towers to increase rapidly, and the sag effect of the cables, geometric nonlinearity, and material nonlinear effects are significantly increased. The influence of these disadvantages greatly reduces the stability of the cable‐stayed bridge, and the stability of the cable‐stayed bridge becomes prominent. In this paper, the finite element method is used to analyze the first kind of stability problem and the second kind of stability problem of cable‐stayed bridges. On this basis, a large‐span prestressed concrete (PC) cable‐stayed bridge is taken as an example to analyze and compare the two types of stability problems of the bridge during the construction phase and the operation phase. Besides, the model experimental study on the stability of the bridge during the maximum double cantilever construction period was carried out. The theoretical values and the model test results are compared to verify the correctness of the calculation method and design theory, which may provide a reference for the design, construction, and scientific research of cable‐stayed bridges.