bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Detection Algorithm of Football Events in Videos
Authors: Jia Yunke
Published: May 2022

The purpose is to effectively solve the problems of high time cost, low detection accuracy, and difficult standard training samples in video processing. Based on previous investigations, football game videos are taken as research objects, and their shots are segmented to extract the keyframes. The football game videos are divided into different semantic shots using the semantic annotation method. The key events and data in the football videos are analyzed and processed using a combination of artificial rules and a genetic algorithm. Finally, the performance of the proposed model is evaluated and analyzed by using concrete example videos as data sets. Results demonstrate that adding simple artificial rules based on the classic semantic annotation algorithms can save a lot of time and costs while ensuring accuracy. The target events can be extracted and located initially using a unique lens. The model constructed by the genetic algorithm can provide higher accuracy when the training samples are insufficient. The recall and precision of events using the text detection method can reach 96.62% and 98.81%, respectively. Therefore, the proposed model has high video recognition accuracy, which can provide certain research ideas and practical experience for extracting and processing affective information in subsequent videos.
Effect of Coral Aggregates of Blended Cement Concrete Subjected to Different Water Immersion Condition
Authors: Bunyamin Bunyamin , Kurniasari Febrina Dian , Munirwan Reza Pahlevi , Putra Jaya Ramadhansyah
Published: May 2022

The increasing use of concrete in construction has caused an increase in the need for resources for concrete mixtures and driving wide‐scale mining. One alternate material that can be used for aggregates replacement is coral. Coral aggregates (CA) have similar chemical characteristics to cement. The compressive strength and tensile splitting strength tests of concrete coral aggregates were performed based on the ASTM and ACI standards. This study aims are to investigate the usage of CA as a partial replacement of cement and fine aggregate to increase the strength of concrete at substitution percentages of 0, 5, 10, and 15%. Two different water submerged scenarios of distilled water and salt sea water immersion were applied. The concrete strength test was carried out after 28 days of curing time, with the specified concrete quality of 17 MPa. The results showed that immersion in distilled water has a higher concrete strength than immersion in sea water. The maximum compressive strength of concrete for distilled and sea water immersion is for 0% CA content of 18.47 and 14.89 MPa, respectively. Moreover, the highest value of the split tensile strength of concrete with distilled and sea water immersion is for CA substitution of 5% of 2.52 and 2.5 MPa, respectively. The addition of 5% coral aggregates to cement and fine sand is the optimum combination since it increases the maximum split tensile strength of concrete.
Personalized Learning Path Recommendation Based on Weak Concept Mining
Authors: Diao Xiuli , Zeng Qingtian , Li Lei , Duan Hua , Zhao Hua , Song Zhengguo
Published: May 2022

Discovering valuable learning path patterns from learner online learning data can provide follow-up learners with effective learning path reference and improve their learning experience and learning effects. In this paper, a personalized learning path recommendation method based on weak concept mining is proposed. Firstly, according to the degree of concept mastery of historical learners, concept maps of different types of learners are generated by clustering and association rule mining algorithms. A set of weak concept learning paths are then automatically generated through topological sorting algorithm. Secondly, the long short-term memory neural network based on the attention mechanism (LSTM+attention) is trained to predict the learning effect of the weak concept learning path. Finally, the personalized weak concept path that meets the expected learning effect is selected from the path prediction results. In the experiment, the proposed method is not only compared with the traditional recommendation method, but also, a comparative experiment on the impact of different learning effect prediction models is carried out. The experiment results show that our proposed method has obvious advantages in recommendation performance.
Local and Deep Features Based Convolutional Neural Network Frameworks for Brain MRI Anomaly Detection
Authors: Einy Sajad , Saygin Hasan , Hivehch Hemrah , Dorostkar Navaei Yahya
Published: May 2022

A brain tumor is an abnormal mass or growth of a cell that leads to certain death, and this is still a challenging task in clinical practice. Early and correct diagnosis of this type of cancer is very important for the treatment process. For this reason, this study aimed to develop computer‐aided systems for the diagnosis of brain tumors. In this research, we proposed three different end‐to‐end deep learning approaches for analyzing effects of local and deep features for brain MRI images anomaly detection. The first proposed system is Directional Bit‐Planes Deep Autoencoder (DBP‐DAE) which extracts and learns local and direction features. The DBP‐DAE by decomposition of a local binary pattern (LBP) into eight bit‐planes extracts are directional and inherent local‐structure features from the input image and learns robust feature for classification purposes. The second one is a Dilated Separable Residual Convolutional Network (DSRCN) which extracts high (deep) and low‐level features. The main advantage of this approach is that it is robust and shows stable results regardless to size of image database and to solve overfitting problems. To explore the effects of mixture of local and deep extracted feature on accuracy of classification of brain anomaly, a multibranch convolutional neural network approach is proposed. This approach is designed according to combination of DBP‐DAE and DSRCN in an end‐to‐end manner. Extensive experiments conducted based on brain tumor in MRI image public access databases and achieves significant results compared to state‐of‐the‐art algorithms. In addition, we discussed the effectiveness and applicability of CNNs with a variety of different features and architectures for brain abnormalities such as Alzheimer’s.
Investigation of Machinability Characteristics of EDMed Inconel 825 Alloy under Multidimensional Parametric Modeling by Using Holistic Grey-PCA Statistical Models
Authors: Sahu Nitin Kumar , Singh Mukesh Kumar , Getrude Mutono-Mwanza Bupe , Sahu Atul Kumar
Published: May 2022

The current social and industrial communities exceedingly demanded the materials that dealt with rich mechanical properties, i.e., the rich strength, hardness, reliability, high resistance against corrosion and oxidation, and high toughness and refractoriness. Recently, the researchers ascertained the comprehensive applications of these difficult to machine materials in the domain of automotive, aeronautical, nuclear industries etc. It is claimed that it is quite hard and expensive for machining these superadvanced materials by traditional machining operations. In the present study, worldwide promising nickel-based superalloy Inconel 825 material is used due to its outstanding mechanical and thermal properties at eminent temperatures and also having broad application in imperative engineering fields. The authors probed that machining cost and smart machinability index have become the gigantic concern in EDM operation; however, these can be minimized by adapting a conduit of evaluation of the optimum setting among multiple input parameters. It was a challenging task, which is respected by authors as the research gaps to be sorted out. To fulfill research gaps, the authors encountered imperative significant EDM input parameters, i.e., spark gap (Sg), gap voltage (Vg), pulse on time (Ton), pulse off time (Toff), Peak Current (Ip), Servo feed (Sf), Depth of Cut (Dc) and difficulty index (Di) corresponding to output responses, i.e., power consumption (Pc), machining time (Mt), and material removal rate (MRR) for framing the machinability index/model for conducting experiments and collecting objectives/responses/outputs. Next, the authors conducted experiments using the Taguchi L27 orthogonal array model in the nonvibratory domain for recording output responses. Later, to potentially access the results, the authors integrated the computational Taguchi methodology with dual models which is called as Taguchi-grey relational analysis (T-GRA) and Taguchi-principal component analysis (T-PCA). The optimum setting condition among considered inputs is discussed in the conclusion section. Later, confirmatory test gratified the evaluated optimum settings which is yielding an improvement of 0.16262 and 0.34398 score in executing T-GRA and T-PCA, respectively. In continuation, the current research evidenced that pulse off time is the chief significant process parameter having

p

values 0.005 and 0.001 for T-GRA and T-PCA, respectively. It is also seen that the depth of cut is also another important significant process parameter having

p

values 0.061 and 0.073 for T-GRA and T-PCA, respectively. Moreover, the current research work also investigated the effect of variation of momentous input process parameters over the surface topography and their utility in improving surface integrity and eliminating the micropores and cracks.
Cervical Lesion Classification Method Based on Cross-Validation Decision Fusion Method of Vision Transformer and DenseNet
Authors: Li Ping , Wang Xiaoxia , Liu Peizhong , Xu Tianxiang , Sun Pengming , Dong Binhua , Xue Huifeng
Published: May 2022

Objective. In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161. Methods. The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific method. Then, the Vision Transformer and DenseNet161 models are trained by the fivefold cross-validation method, and the fivefold prediction results corresponding to the two models are fused by different weights. Finally, the five fused results are averaged to obtain the category with the highest probability. Results. The results show that the fusion method in this paper reaches an accuracy rate of 68% for the four classifications of cervical lesions. Conclusions. It is more suitable for clinical environments, effectively reducing the missed detection rate and ensuring the life and health of patients.
A Network Pharmacology Study to Explore the Underlying Mechanism of Safflower (Carthamus tinctorius L.) in the Treatment of Coronary Heart Disease
Authors: Meng Qingwen , Liu Huajiang , Wu Haolin , shun Ding , Tang Chaoling , Fu Xinyin , Fang Xingyue , Xu Yiqian , Chen Bocen , Xie Yiqiang , Liu Qibing
Published: May 2022

Safflower has long been used to treat coronary heart disease (CHD). However, the underlying mechanism remains unclear. The goal of this study was to predict the therapeutic effect of safflower against CHD using a network pharmacology and to explore the underlying pharmacological mechanisms. Firstly, we obtained relative compounds of safflower based on the TCMSP database. The TCMSP and PubChem databases were used to predict targets of these active compounds. Then, we built CHD-related targets by the DisGeNET database. The protein-protein interaction (PPI) network graph of overlapping genes was obtained after supplying the common targets of safflower and CHD into the STRING database. The PPI network was then used to determine the top ten most significant hub genes. Furthermore, the DAVID database was utilized for the enrichment analysis on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). To validate these results, a cell model of CHD was established in EAhy926 cells using oxidized low-density lipoprotein (ox-LDL). Safflower was determined to have 189 active compounds. The TCMSP and PubChem databases were used to predict 573 targets of these active compounds. The DisGeNET database was used to identify 1576 genes involved in the progression of CHD. The top ten hub genes were ALB, IL6, IL1B, VEGFA, STAT3, MMP9, TLR4, CCL2, CXCL8, and IL10. GO functional enrichment analysis yielded 92 entries for biological process (BP), 47 entries for cellular component (CC), 31 entries for molecular function (MF), and 20 signaling pathways, which were obtained from KEGG pathway enrichment screening. Based on these findings, the FoxO signaling pathway is critical in the treatment of CHD by safflower. The in vitro results showed that safflower had an ameliorating effect on ox-LDL-induced apoptosis and mitochondrial membrane potential. The western blot results showed that safflower decreased Bax expression and acetylation of FoxO1 proteins while increasing the expression of Bcl-2 and SIRT1 proteins. Safflower can be used in multiple pathways during CHD treatment and can exert anti-apoptotic effects by regulating the expression of Bax, Bcl-2, and SIRT1/FoxO1 signaling pathway-related proteins.
Clinical Efficacy of Single-Port Thoracoscopic Lobectomy versus Three-Port Thoracoscopic Lobectomy for Lung Cancer
Authors: Xu Yueliang , Zhou Yinxi , Lv Feng , Liu Yongshi , Ji Xiang
Published: May 2022

Objective. To evaluate the clinical efficacy of single-port thoracoscopic lobectomy versus three-port thoracoscopic lobectomy for lung cancer. Methods. From February 2020 to February 2021, 200 lung cancer patients treated in our institution assessed for eligibility were enrolled and randomly assigned (1 : 1) to either the experimental group (single-port thoracoscopic lobectomy) or the control group (three-port thoracoscopic lobectomy). The outcomes were the eligible patients’ surgical indices, pain stress indexes, and postoperative complications. Results. The experimental group outperformed the control group in terms of incision length, postoperative drainage time, extubation time, time to get out of bed, time to analgesics administration, and postoperative pain score (

P
<
0.001

). Compared with the control group, the experimental group reduced the intraoperative bleeding (

161.98
±
10.65

versus

179.65
±
14.20

,

P
<
0.001

) and length of hospital stay (

7.98
±
0.56

versus

10.46
±
1.23

,

P
<
0.001

). The operative time of the single-port thoracoscopic lobectomy was longer than that of the three-port thoracoscopic lobectomy (

P
<
0.001

). There was no statistical difference between the two groups in the intraoperative conversion to thoracotomy and the number of lymph node dissections (

P
>
0.05

). Postoperative pain stress indices and complication rates of the experimental group were significantly lower than those of the control group (

P
<
0.001

). Conclusion. Single-port thoracoscopic lobectomy can improve the perioperative indices of lung cancer patients, reduce their pain stress response, and accelerate postoperative recovery. However, its operation is difficult and time-consuming, requiring experienced surgeons for improved surgical outcomes in practice.
Long Noncoding RNA H19 Derived from M2 Tumor-Associated Macrophages Promotes Bladder Cell Autophagy via Stabilizing ULK1
Authors: Guo Yuanyuan , Sun Wei , Gao Wuyue , Li Liqiang , Liang Yujie , Mei Zhijie , Liu Beibei , Wang Rui
Published: May 2022

Purpose. M2-like tumor-associated macrophages (TAMs) are crucial component of immune infiltration in tumor microenvironment (TME), and exosomes derived from TAMs contributed to the regulation of tumor progression through cellular communication. However, in bladder cancer, the role of exosomal components still remains largely unknown. In the current study, we investigated the role of exosomes derived from M2-like TAMs in the regulation of autophagy in bladder cancer (BC) cells. Methods. THP-1 cells were stimulated with IL-4 and IL-13 for the polarization of TAMs, and exosomes were extracted by ultracentrifugation. H19 overexpression plasmid and H19 siRNAs were used in the study. Fluorescent analysis was performed for GFP-LC3 detection. Levels of autophagy and potential target were confirmed by western blot assay and immunoprecipitation. Results. We found that TAMs-exosome treatment significantly enhanced autophagy in BC cells, and the expression of lncRNA H19 was greatly upregulated in TAMs-exosome. Silencing of lncRNA H19 in TAMs-exo obviously decreased the levels of LC3-II expression whereas the p62 levels were increased. Mechanistically, silencing of exosomal H19 from TAMs alleviated ULK1 stabilization in BC cells through promoting K48-linked polyubiquitination of ULK1. At last, we found that overexpression of exosomal H19 from TAMs suppressed the interaction between ULK1 and its specific E3 ligase NEDD4L in BC cells. Conclusion. We revealed the effect of TAMs-exo-contained lncRNA H19 on regulating autophagy of bladder cancer cells, which indicated that targeting TAMs-H19 is a promising therapeutic strategy for the treatment of BC.
Screening of Aqueous Extract of Persea americana Seeds for Alpha-Glucosidase Inhibitors
Authors: Lawal Tajudeen Afolayan
Published: May 2022

Activity of α-glucosidase enzyme in the gastrointestinal tract has been implicated in postprandial hyperglycaemia. If not properly controlled, postprandial hyperglycaemia might progress to diabetes mellitus, a metabolic syndrome. Diabetes is associated with many complications such as retinopathy, heart attack, nephropathy, neuropathy, stroke, and lower limb amputation. Antidiabetic medications presently in use have little effect on postprandial glycaemic excursion and hence do not bring down the blood glucose level to baseline. This study extracted, fractionated, and screened the aqueous extract of Persea americana seeds for hypoglycaemic potential. Inhibitory effects of the fractions and subfractions of the extract on α-glucosidase activity were investigated. The most active subfraction was subjected to Fourier transform infrared (FTIR) and gas chromatography mass spectroscopy (GC-MS) analysis to elucidate the active components. The active subfraction showed a significant inhibition (

p
<
0.05

) on α-glucosidase. The subfraction competitively inhibits α-glucosidase (with IC50 = 09.48 ± 0.58 μg/mL), though less potent than the standard drug, acarbose (IC50 = 06.45 ± 0.47 μg/mL). FTIR analysis of the subfraction showed the presence of carbonyl group, hydroxy group, carboxyl group, double bonds, methylene, and methyl groups. GC-MS analysis suggests the presence of cis-11,14-eicosadienoic acid, catechin, and chlorogenic acid as the active components. In conclusion, the components obtained from this study can be synthesised in the laboratory to further confirm their hypoglycaemic activity. The most active subfraction can be explored further to confirm its inhibitory activity against the enzyme and to determine its extent in the treatment of diabetes mellitus in vivo.
Research on the Correlation Model and Algorithm between Intangible Assets and Enterprise Value of Sports Listed Enterprises Based on Deep Learning
Authors: Dong Xiaoyan , Xu Ziqi
Published: May 2022

In order to explore the relationship between intangible assets and enterprise value of sports listed enterprises and build a multiple regression model, obtain the enterprise value of enterprise variables through parallel neural fuzzy network calculation, so as to verify the correlation between intangible value and enterprise value. With the development of economy, the intangibility of enterprises has been widely concerned. No matter what angle scholars study it from, our common concern is the research on the value of intangible assets and the definition of its existence. Taking sports listed companies as the research object, this study analyzes and discusses the correlation between intangible assets and enterprise value of sports listed companies by using the statistical analysis model algorithm based on deep learning and the method of constructing multiple regression model. The results show that intangible assets have a significant correlation with enterprise value, which is conducive for enterprises to promote the realization of enterprise value by improving intangible assets and provide favorable support for the sustainable development of the company.
A Novel Robust Grey Model and Its Application
Authors: Lan Fei , Kong Jiayang , Yao Riquan , Wei Cong
Published: May 2022

Previous studies paid attention to improving the predicted capability of the classical grey model, but its robustness is still unclear and not explored, in particular when these exhibit outliers in the time series, which is due to measurement error, uncorrected record, and censored date. In this study, we proposed a novel robust grey model. The novel robust grey model adopts the median regression method to address these problems caused by outliers, which provides the robust parameters. The analytical expression for the time response function and the forecasting values is derived by the grey system technique and mathematical tool. With annual observational data of Chinese electricity demand, we examine the fitness capability of the novel robust grey model, by comparing it with the classical grey model. Also, we adopt the bootstrapping test to further illustrate the sensitivity for the new robust grey model when there are outliers in the time series. To our knowledge, it is the first to introduce the bootstrapping test to the literature related to the grey model and to focus on the robustness of the grey model. The computational results suggest that the new robust grey model has higher precision than the classical grey model, but it is also very robust to outliers, whose accuracy and robustness are better than the classical grey model. Finally, we apply the novel grey model to forecast the future values in Chinese electricity demand during the year 2022 to 2025. This new model proposed in this study estimates that the Chinese electricity demand would continue to increase after the year of 2022, arriving at 10.446×105 million kW·h in the year of 2025 approximately.
Effect of Vitamin A Supplementation on Growth Performance, Lipid Deposition, Antioxidant Ability, and Immunity in Juvenile Chinese Mitten Crab Eriocheir sinensis Fed Diet with Fish Oil Totally Replaced by Palm Oil
Authors: Huang Qincheng , Wang Xiaodan , Liu Jiadai , Wang Han , Miao Yixin , Zhang Cong , Zhang Meiling , Qin Chuanjie , Qin Jianguang , Chen Liqiao
Published: May 2022

This research evaluated the protective effect of vitamin A (VA) on the adverse effect of fish oil (FO) substitution with palm oil (PO) in an economical crab Eriocheir sinensis. Three diets of FO, PO, and

PO
+
8000
 
IU
/
kg

VA as the main lipid sources were fed to crabs, respectively, for 8 weeks. Compared to crabs fed FO diet, crabs fed PO diet showed reduced hemolymph VA concentration, feed utilization efficiency, and growth performance. Besides, crabs fed PO diet showed elevated lipid content in hepatopancreas and body and triglyceride content in hepatopancreas, leading to decreased antioxidant enzyme and immune parameters activities from biochemical analysis, enzymatic determination, and quantitative polymerase chain reaction. In contrast, compared to crabs fed PO only, VA supplementation in PO improved the growth performance and utilization of fatty acids and reduced lipid deposition in the hepatopancreas. In addition, VA supplementation suppressed gene expression related to triglyceride synthesis (dgat1) and positively affected gene expression related to lipid catabolism (cpt1a, cpt1b, cpt2, and caat). Furthermore, VA supplementation upregulated antioxidant genes (CuZnSOD and CAT) through downregulating gene expression of upstream regulator Keap1. Furthermore, VA supplementation upregulated immune genes (Lzm and proPO) expression and reduced proinflammatory genes (LITAF, ADAM17, and IL-16) expression related to Toll2/MyD88/Relish signaling pathway. This study shows the necessity of VA addition in the feed with FO totally replaced by PO because it can relieve PO’s adverse effects and improve the growth of crabs.
An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients
Authors: Tian Jiahui , Fu Chunyan , Zeng Xuan , Fan Xiaoxiao , Wu Yi
Published: May 2022

Purpose. Pancreatic cancer (PC) is a common, highly lethal cancer with a low survival rate. Autophagy is involved in the occurrence and progression of PC. This study aims to explore the feasibility of using an autophagy-related long noncoding RNA (lncRNA) signature for assessing PC patient survival. Methods. We obtained RNA sequencing and clinical data of patients from the TCGA website. Autophagy genes were obtained from the Human Autophagy Database. The prognostic model, generated through univariate and multivariate Cox regression analyses, included 10 autophagy-related lncRNAs. Receiver operating characteristic (ROC) curves and forest plots were generated for univariate and multivariate Cox regression analyses, to examine the predictive feasibility of the risk model. Gene set enrichment analysis (GSEA) was used to screen enriched gene sets. Results. Twenty-eight autophagy-related lncRNAs were filtered out through univariate Cox regression analysis (

P
<
0.001

). Ten autophagy-related lncRNAs, including 4 poor prognosis factors and 6 beneficial prognosis factors, were further screened via multivariate Cox regression analysis. The AUC value of the ROC curve was 0.815. GSEA results demonstrated that cancer-related gene sets were significantly enriched. Conclusion. A signature based on ten autophagy-related lncRNAs was identified. This signature could be potentially used for evaluating clinical prognosis and might be used for targeted therapy against PC.
Comprehensive Analyses of Stromal-Immune Score-Related Competing Endogenous RNA Networks In Colon Adenocarcinoma
Authors: Tong Yalin , Peng Mengle , Li Jinbei , Niu Ying
Published: May 2022

Although recent clinical investigations emphasize the roles of myriad diversities of RNAs in stromal and immune components in the tumor microenvironment, especially in colon adenocarcinoma, however, analyses of “competing endogenous RNAs (ceRNA)” network in association with stromal and immune scores have yet to be determined. This study was conducted to explore the regulatory mechanisms of a stromal-immune score-based ceRNA network in colon adenocarcinoma. Stromal and immune scores of colon adenocarcinoma tumor samples were calculated by using the ESTIMATE algorithm. Differential expression analysis between samples with high/low stromal and immune scores was performed, followed by functional annotation for the overlapping DEmRNAs. The ceRNA network was constructed by differential expression analysis, prediction of RNA-RNA interaction, and correlation with clinicopathological parameters of the patients, which were further verified by external datasets and experiments. Colon adenocarcinoma patients having higher immune scores exhibited prolonged overall survival. RNA dataset analyses from TCGA revealed aberrant expressions of a total of 2052 mRNAs, 108 lncRNAs, and 70 miRNAs between high and low stromal/immune groups. Functional annotation mapped the differentially overexpressed mRNAs for immune-associated GO terms. To construct the ceRNA network, a total of 48 lncRNAs, 40 miRNAs, and 199 mRNAs were sorted out. A dysregulated ceRNA network consisting of 6 lncRNAs, 11 miRNAs, and 39 mRNAs was constructed by comparing RNA expressions between cancer as well as adjacent normal tissues. The ceRNA regulatory axis “MIAT/miR-532-3p/STC1” was regarded as a potential hit by the comprehensive analysis. The RT-qPCR assay showed upregulation of MIAT and STC1 while downregulation of hsa-miR-532-3p expression in cancer. Thus, our study highlights the potential role of a stromal-immune score-based ceRNA network in the colon adenocarcinoma microenvironment. The ceRNA axis MIAT/miR-532-3p/STC1 could serve as a promising therapeutic target for colon adenocarcinoma.
[Retracted] Activating IL‐6/STAT3 Enhances Protein Stability of Proteasome 20S α+β in Colorectal Cancer by miR‐1254
Authors: Ren Weiguo , Zhang Xuexiu , Li Qiang , Pu Chibin , Zhang Decai
Published: May 2022

A widely recognized feature of colorectal cancer (CRC) is an increase in cytokine levels, which result in an inflammatory environment in the tumor. Interleukin‐6 (IL‐6) is a robust protumor cytokine. Several studies suggest that IL‐6 plays a role in the development of tumors. Most intracellular protein breakdown occurs in eukaryotes via the ubiquitin‐proteasome pathway; this mechanism may also be involved in cancer pathogenesis. The tumor tissues and paracancerous tissues were collected from 90 patients with colorectal cancer. The expressions of pSTAT3, proteasome 20S α+β, miR‐1254, and PSMD1 in tissues were detected by immunohistochemistry, ELISA, and qRT‐PCR, and the effects of pSTAT3 and proteasome 20s α+β expressions on the survival of patients were studied. HCT116 and HCT116‐R cells were cultured and added IL‐6, AG490, STAT3 plasmid, or overexpression/knockdown of miR‐1254 in cells. Immunofluorescence, western blot, qRT‐PCR, double luciferase gene reporter assay, and flow cytometry were used to detect the expression of pSTAT3, STAT3, proteasome 20s α+β, miR‐1254, and PSMD1 and cell cycle. The nude mouse xenograft model was constructed and divided into 3 groups: PBS group, IL‐6 treatment group, and IL‐6+miR‐1254 mimic group. After 28 days, the tumor tissues were collected, and the expressions of miR‐1254, pSTST3, proteasome 20s α+β, and PSMD1 in the tissues were detected by qRT‐PCR and immunohistochemistry, respectively. Our study discovered that the level of proteasome 20S α+β had a strong connection with pSTAT3 in CRC patients. They were also linked to the development and clinical outcome of CRC. In addition, we found that IL‐6 dramatically increased the expression of proteasome 20S α+β and pSTAT3; however, it did not affect the proteasome 20S α+β mRNA synthesis. Circulating proteasome concentration correlated with tumor tissue proteasome 20S α+β. STAT3 could occupy the miR‐1254 promoter to inhibit transcription, and it could suppressed miR‐1254 which targeted PSMD10, promoting proteasome 20S α+β protein stability. This is a prospective target for developing a new colorectal cancer therapy strategy.
Image Encryption Algorithm Based on an Improved ML Neuron Model and DNA Dynamic Coding
Authors: Cao Yan , Shi Peng , Wu Kaijun , Li Wenqin
Published: May 2022

Aiming at the problems of small key space, low security, and low algorithm complexity in a low-dimensional chaotic system encryption algorithm, an image encryption algorithm based on the ML neuron model and DNA dynamic coding is proposed. The algorithm first performs block processing on the R, G, and B components of the plaintext image to obtain three matrices, and then constructs a random matrix with the same size as the image components through logistic mapping and performs DNA encoding, DNA operation, and DNA decoding on the two parts. Second, it performs determinant permutation on the matrix by two different chaotic sequences obtained by logistic mapping iteration. Finally, it merges the block and image components to complete the image encryption and obtain the ciphertext image. Wherein, DNA encoding, DNA operation, and DNA decoding methods are all randomly and dynamically determined by the chaotic sequence generated by the ML neuron chaotic system. According to simulation results and performance analysis, the algorithm has a larger key space, can effectively resist various statistical and differential attacks, and has better security and higher complexity.
Effect of Evaluation of Popular Song Creation and Literature Based on RITNN Model
Authors: Song Rui
Published: May 2022

The era of modernity and information technology has entered people's lives, which has brought new challenges to innovative culture and art to a great extent, especially changing the relationship between traditional culture and pop music creation. In order to better automate music creation, this paper proposes a mixed model of cultural term evaluation of Chinese pop songs based on RITNN, which sets up RNN structure composition models of different channels. Through this method, a data reconstruction model of important cultural elements of simultaneous interpretation is established. The experiment fully proves that the pop music creation in traditional cultural materials not only integrates the essence of traditional cultural elements but also makes new creation. This scheme can evaluate the integration effect of traditional cultural elements in pop music creation.
Correlation Analysis of Nodular Sonographic Parameters with Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma
Authors: Zhou Liuhua , Zhu Qiaodan , Yao Jincao , Yang Chen , Xu Dong
Published: May 2022

Background. Papillary thyroid carcinoma (PTC) is the most common thyroid carcinoma and is prone to cervical lymph node metastases (CLNM). We aim to analyze the correlation between clinical information, ultrasonic parameters of PTC, and CLNM. Methods. 1335 patients who had pathologically confirmed unifocal PTC were enrolled in this retrospective cohort study. Univariate and multivariate logistic analyses were performed to predict CLNM in PTC patients. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance. Results. Univariate analysis showed that gender, age, maximum tumor diameter and volume, and cross‐sectional and longitudinal aspect ratio were related to CLNM (P < 0.05). Multivariate logistic analysis showed that gender, age, maximum tumor diameter, and volume were independent correlative factors, and the cross‐sectional aspect ratio had significant difference for PTC2 to predict CLNM. The area under the curve (AUC) of the maximum tumor diameter and volume was 0.738 and 0.733, respectively. Maximum tumor diameter and volume and the cross‐sectional and longitudinal aspect ratio were statistically significant following analysis of variance (P < 0.05). Conclusions. Younger age, male, and larger tumor were high risk factors for CLNM in patients with unifocal PTC. The cross‐sectional aspect ratio had a more effective predictive value for CLNM in patients with larger thyroid tumors.
Research on the Characteristic Model of Learners in Modern Distance Music Classroom Based on Big Data
Authors: Wang Yushan , Liu Lianhong
Published: May 2022

This paper makes in-depth research on data mining, especially association rule mining, improves the FP-tree algorithm in both the algorithm itself and the data source, and finds out a mining algorithm suitable for learner characteristics. Association rule algorithm for actor feature model mining. By establishing the characteristic model of learners in modern distance music classroom, simulation experiments are carried out on FP-tree and three improved algorithms. This paper improves the FP-tree algorithm. Firstly, we improve the algorithm itself; aiming at the problem of too many frequent itemsets, an improved key item extraction algorithm KEFP-growth based on FP-growth is proposed, which ignores the frequent itemsets that are not concerned in the analysis. Then, improvements were made in terms of data sources. In view of the problem that the data source is too large, the mining efficiency is low, and the FP-tree cannot be loaded in memory, this paper proposes a data projection algorithm, which adopts the idea of divide and conquer, divides the frequent 1-itemsets of the database into database subsets of each frequent 1-itemsets, and then mines the database subsets separately and then merges them. Finally, the KEFP-growth algorithm and the projection algorithm are combined, which can not only eliminate the mining of meaningless frequent items but also divide the data when there is a large amount of data. This paper also compares the performance of the three improved algorithms and the original FP-tree algorithm through experiments. The experiments show that the combination of the KEFP-growth algorithm and the database projection algorithm is the most suitable one for the learner feature mining of the adaptive learning system. (1) The KEFP-growth algorithm reduces the number of frequent items output by the original FP-tree algorithm by about 50%, and the mining time is reduced by 50%. (2) The data projection algorithm is more suitable for data mining with less support. When the support is 10%, the mining time of the data projection algorithm is reduced by 80% compared with the FP-tree algorithm. (3) When the support degree is 10%, the running time of the hybrid algorithm is reduced by 10% compared with the KEFP-growth algorithm and the data projection algorithm.
An Occupancy-Based Adaptive Signal Control for a Congested Signalized Intersection in the Low CV Penetration Environment
Authors: Zhang Yi , Zhang Jianhua , Tan Baihong , He Shuxian , Peng Liqun , Qiu Tony Z.
Published: May 2022

Adaptive signal control (ASC) is a well-researched topic that offers an efficient way for traffic management. It possesses a powerful ability to accommodate complex and constantly changing urban transportation networks. With the development of vehicular communication, CV-based ASC shows remarkable advantages compared with the traditional ASC system. Though the existing CV-based ASC strategies were proposed in the past few years, however, there are still issues to overcome. Most of the studies on CV-based ASC are based on the assumption of high CV penetration rate, which often result in poor performance when applied to low CV penetration environments. Besides, the lack of consideration for mixed traffic flow, which is in terms of both the vehicle types and CV penetration of different types of vehicles. To solve these issues, this paper developed an Occupancy-Based ASC strategy for a congested signalized intersection to optimize signal timing and reduce total passenger delay in the low CV penetration environment. Focused on the issues existing in the low CV penetration environment, a Maximum Likelihood Estimation (MLE) model was proposed to estimate vehicle arrivals, and two traffic models, MicroDM and MacroDM, were developed to model the mixed traffic flow and estimate passenger delay. With the purpose of offering fair treatment to passengers approaching the intersection, we proposed an Occupancy-Based Adaptive Signal Control strategy. By transforming the complex signal control problem into a mixed-integer linear programming problem, we found the optimal solution for minimizing total passenger delay. We then evaluated the proposed Occupancy-Based ASC strategy using simulation case studies. The results show that changing traffic status could be captured and estimated with the real-time CV trajectory data as input. Applying the Occupancy-Based ASC control strategy, phases with HOVs or more vehicles will be allocated more travel time. In particular, optimization results show that the proposed Occupancy-Based ASC strategy effectively balances passenger travel demands during peak volume periods.
Mechanisms of Mitochondrial Malfunction in Alzheimer’s Disease: New Therapeutic Hope
Authors: Nabi Showkat Ul , Khan Andleeb , Siddiqui Ehraz Mehmood , Rehman Muneeb U. , Alshahrani Saeed , Arafah Azher , Mehan Sidharth , Alsaffar Rana M. , Alexiou Athanasios , Shen Bairong
Published: May 2022

Mitochondria play a critical role in neuron viability or death as it regulates energy metabolism and cell death pathways. They are essential for cellular energy metabolism, reactive oxygen species production, apoptosis, Ca++ homeostasis, aging, and regeneration. Mitophagy and mitochondrial dynamics are thus essential processes in the quality control of mitochondria. Improvements in several fundamental features of mitochondrial biology in susceptible neurons of AD brains and the putative underlying mechanisms of such changes have made significant progress. AD’s etiology has been reported by mitochondrial malfunction and oxidative damage. According to several recent articles, a continual fusion and fission balance of mitochondria is vital in their normal function maintenance. As a result, the shape and function of mitochondria are inextricably linked. This study examines evidence suggesting that mitochondrial dysfunction plays a significant early impact on AD pathology. Furthermore, the dynamics and roles of mitochondria are discussed with the link between mitochondrial malfunction and autophagy in AD has also been explored. In addition, recent research on mitochondrial dynamics and mitophagy in AD is also discussed in this review. It also goes into how these flaws affect mitochondrial quality control. Furthermore, advanced therapy techniques and lifestyle adjustments that lead to improved management of the dynamics have been demonstrated, hence improving the conditions that contribute to mitochondrial dysfunction in AD.
Numerical Investigation of Carbon Fiber Reinforced Polymer Confined Concrete‐Filled Steel Tube Columns under Eccentric Load
Authors: Degefa Zewdu Brhanu , Wondimu Aure Temesgen
Published: May 2022

Recently, Fiber Reinforced Polymer (FRP) materials have emerged as a viable alternative to confined columns due to their high ultimate tensile strength to weight ratio and corrosion resistance under harsh and corrosive environments. Many previous studies were focused on the confining capability of FRP on concentric axial loads. This study presents a nonlinear finite element (FE) investigation of the effects of the thickness of Carbon Fiber Reinforced Polymer (CFRP), the thickness of steel tube, cross‐sectional shape, and slenderness effect of an FRP confined concrete‐filled steel tube (FCCFST) column under eccentric load. The FE model was validated by comparing the results with experimental data available in the literature, and good agreement was found. From the FE results, it was found that the steel tube and CFRP confinement improved the load resistance capacity by about 34% to 39%, and the axial shortening of the column at the peak load, from 136% to 57%, in rectangular and circular cross‐sections, respectively. The efficiencies of steel tube and CFRP confinement first increase with an increasing eccentricity of the axial load and then start to decrease as the failure mode of the column changes to stability.
Effects of Polyethylene Terephthalate Fibre Reinforcement on Mechanical Properties of Concrete
Authors: Nkomo NZ , Masu LM , Nziu PK
Published: May 2022

Cracked concrete is a problem due to several factors such as poor maintenance, insufficient reinforcement, or steel corrosion leading to crack propagation. There is a need to increase the load-bearing capacity of concrete slabs and increase their life span. The use of waste polyethylene terephthalate fibres in concrete can dramatically alleviate the problem of crack propagation and failure sustainably. Furthermore, the utilization of waste plastic in this manner is environmentally friendly. This study presents the experimental investigation into the mechanical strength properties of concrete with respect to the effect of various mass fractions of polyethylene terephthalate fibre. The polyethylene terephthalate fibres were added at mass fraction of 0.5%, 1.0%, 1.5%, and 2.0%. An experimental investigation was carried out to explore the effect of varying fibre mass fractions on the slump value, rebound number, split tensile strength, flexural strength, and compressive strength. An increase in flexural strength, rebound number and compressive strength was noted with an increase in fibre mass fraction. However, a decrease in split tensile strength was noted. The addition of 0.5% fibre gave the highest compressive and flexural strength of 29.32 N/mm2 and 28 N/mm2, respectively. However, the addition of fibre lowered the split tensile strength beyond the control specimen at all fibre mass fractions. The experimental results of this study indicate that the addition of polyethylene terephthalate fibre enhances the mechanical strength of concrete at low fibre mass fraction percentages. The PET fibre reinforced concrete is suitable for use in paving and ceiling slabs at a fibre addition of 0.5% for optimum workability and mechanical strength.
A Design Method of Virtual Folk Museum Roaming System Based on Visual Interaction Technology
Authors: Li Yifei
Published: May 2022

The effective display of museums can greatly improve the attention of museums. The traditional museum display methods are old and old-fashioned. Aiming at the lack of vivid and immersive display in folklore museums, based on augmented reality technology and artificial intelligence, a virtual folklore museum roaming system based on visual interaction technology is proposed for 3D visualization of cultural heritage. The method applies the 6D pose estimation algorithm of objects to the display of museum objects. 6D pose estimation of objects refers to detecting objects present in an image and estimating their 3D position and orientation relative to the observer. In the field of augmented reality, 6D pose estimation is used to measure the pose of objects in the real environment and add virtual objects to it with the correct pose, thereby realizing augmented reality applications on the mobile side. The experimental results show that the 6D pose estimation method proposed in this paper has an accuracy rate of 93.3% and a running speed of 30 frames per second, which meets the needs of augmented reality applications and can be used in augmented reality museum display systems to provide them with vivid interactive content. Breaking through the time and space limitations of cultural heritage objects, it brings an immersive visiting experience to the audience.
Computational Approach for Detection of Diabetes from Ocular Scans
Authors: Khan Asif Irshad , Kshirsagar Pravin R. , Manoharan Hariprasath , Alsolami Fawaz , Almalawi Abdulmohsen , Abushark Yoosef B. , Alam Mottahir , Chamato Fekadu Ashine
Published: May 2022

The estimated 30 million children and adults are suffering with diabetes across the world. A person with diabetes can recognize several symptoms, and it can also be tested using retina image as diabetes also affects the human eye. The doctor is usually able to detect retinal changes quickly and can help prevent vision loss. Therefore, regular eye examinations are very important. Diabetes is a chronic disease that affects various parts of the human body including the retina. It can also be considered as major cause for blindness in developed countries. This paper deals with classification of retinal image into diabetes or not with the help of deep learning algorithms and architecture. Hence, deep learning is beneficial for classification of medical images specifically such a complex image of human retina. A large number of image data are considered throughout the project on which classification is performed by using binary classifier. On applying certain deep learning algorithms, model results into the training accuracy of 96.68% and validation accuracy of 66.82%. Diabetic retinopathy can be considered as an effective and efficient method for diabetes detection.
Real-Time Prediction of Cross-Border e-Commerce Spike Performance Based on Neural Network and Decision Tree
Authors: Li Na , Gong Chongyi , Lv Dongqin
Published: May 2022

During the spike in the activity of the cross-border e-commerce company, due to the limitation of time and data, the historical activity performance data and some data of this activity are used. Due to the limitation of data and the specificity of the prediction task, the original data are modeled and predicted by using a BP neural network model after a series of processing. This paper proposes a prediction model based on a decision tree and BP neural network model, through this real-time prediction model to predict the performance of the company’s spike activity every minute but also can play an early warning role, which is more helpful to the company’s decision-making. In fact, the company also used this model to detect a trend of lower performance during the May spike and then improved the performance and sell-out rate through email marketing and increased discounts to avoid inventory backlog.