bluuebunny/crossref_metadata_2025 huggingface dataset:
Policy Analysis and Optimization Method of Chinese Animation Industry Management Based on Policy Tools
Authors: Cao Meng
Published: April 2022

On the basis of theoretical research, a two-dimensional analysis framework of China’s animation industry policy text is constructed from the dimension of policy tools and the dimension of value chain. According to the two-dimensional analysis framework constructed in this study, the content analysis of the Chinese animation industry policy samples selected in this study is carried out, and the characteristics of China’s animation industry policy in the use of policy tools and the application of policy tools at various stages of the value chain are obtained. Then, the shortcomings of China’s animation industry policy are discussed. Finally, to promote the reform and improvement of the policy step by step and promote the long-term and healthy development of China’s animation industry, this study proposes corresponding strategies for the optimization of China’s animation industry policy. This study takes the theory of policy tools as a theoretical perspective and deeply analyzes the possible shortcomings of China’s animation industry policy texts. Based on the perspective of policy tools, corresponding strategies are proposed for the optimization of China’s animation industry policy, and the proposed optimization strategies are evaluated through fuzzy mathematical theory. The results obtained are excellent, and it can be concluded that an optimization strategy is proposed to improve the animation industry structure.
MOOC Dropout Prediction Based on Multidimensional Time-Series Data
Authors: Shou Zhaoyu , Chen Pan , Wen Hui , Liu Jinghua , Zhang Huibing
Published: April 2022

Massive open online courses have attracted millions of learners worldwide with flexible learning options. However, online learning differs from offline education in that the lack of communicative feedback is a drawback that magnifies high dropout rates. The analysis and prediction of student’s online learning process can help teachers find the students with dropout tendencies in time and provide additional help. Previous studies have shown that analyzing learning behaviors at different time scales leads to different prediction results. In addition, noise in the time-series data of student behavior can also interfere with the prediction results. To address these issues, we propose a dropout prediction model that combines a multiscale fully convolutional network and a variational information bottleneck. The model extracts multiscale features of student behavior time-series data by constructing a multiscale full convolutional network and then uses a variational information bottleneck to suppress the effect of noise on the prediction results. This study conducted multiple cross-validation experiments on KDD CUP 2015 data set. The results showed that the proposed method achieved the best performance compared to the baseline method.
The Application of Artificial Intelligence Oriented to the Internet of Things Technology in the Complexity of Digital Crossborder Trade
Authors: Liu Hongqing , Liu Ting
Published: April 2022

With the continuous update of technologies such as cloud computing, big data, AI, and 5G, various IoT services have also been developed. Various IoT terminals have emerged one after another, and the number of access terminals has also risen sharply. With the development and popularization of a new generation of artificial intelligence technology, the Internet of Things mainly connects various objects to each other in an agreed manner through various sensing devices and conducts wireless communication and data exchange to achieve object-to-object communication, positioning, traceability, intelligent identification, and management. The Internet of Things has broad application prospects in many aspects such as intelligent robots, transportation, and intelligent buildings. Accurate node positioning is considered to be the focus of the application of the Internet of Things in geographic and network location services. More and more experts and scholars have begun to worry about the future development of artificial intelligence technology. While artificial intelligence technology provides convenience to the society, it may also cause huge hidden dangers to the public safety of the entire society. Therefore, it is necessary to make decisions about the technical risks of artificial intelligence. Philosophical analysis has certain historical, practical significance, and theoretical value. Internet technology innovation and digital technology innovation continue to emerge. Digital trade has become a typical feature of the current economic structural transformation, and it is also a new driving force for countries to participate in the division of labor in the global value chain. At present, the scale of global digital trade continues to expand, but there are still many restrictions on digital trade rules. Digital trade tariff barriers and nontariff barriers in digital trade such as crossborder data flow restrictions, intellectual property infringements, and personal information protection are bound to have an impact on the global value chain division of labor system. IoT technology-oriented artificial intelligence accelerates the logistics speed and tracking efficiency of digital cross-border trade, improves consumers’ willingness to spend, and promotes the development of the entire e-commerce trade field.
Optimization of Mechanical Properties and Manufacturing Time through Experimental and Statistical Analysis of Process Parameters in Selective Laser Sintering
Authors: Korycki Adrian , Garnier Christian , Nassiet Valerie , Sultan Charles Tarek , Chabert France
Published: April 2022

A statistical analysis of the response of the parameters has been led by a design of experiments to extract the most influential parameters in the selective laser sintering process. The parametric study was carried out by varying five parameters on the SLS machine and by looking at their influence on five groups of responses relating to the physical, mechanical, and thermal properties as well as to the printing duration. The mathematical models of the response surfaces were established by linking the responses to factors and their interactions. The model of regression uses not only the interaction between regressors but also nonlinear logical functions. These statistical models were used to define an optimal set of parameters. The geometry and density of specimens made of polyamide 12 confirm that increasing the distance between successive laser beam passes allows a significant manufacturing time reduction. The mechanical properties depend mostly on laser power and scan count. We conclude that a low laser power applied twice can improve the properties of the printed part. By optimizing the laser parameters, the targeted mechanical properties are obtained with over 33% of production time savings.
Strength Model of Cemented Filling Body Based on a Neural Network Algorithm
Authors: Deng Daiqiang , Liang Yihua , Cao Guodong , Fan Jinkuan
Published: April 2022

As one of the key measures for comprehensive management of goaf in various mines, filling mining has been recognized by practitioners in recent years due to its functions (e.g., resource utilization of solid waste and thorough goaf treatment). The performance of the filling material is the core challenge of filling mining, and it is influenced by the settling speed, conveying characteristics, and filling body strength. To understand the strength characteristics of a cemented filling body composed of medium-fine tailings, in this study, filling material ratio tests under different content of cement, tailings, and water were conducted. A backpropagation (BP) neural network topology structure was established in this study. The strength after different curing times was used as the output variable to analyze the impact of the cement, tailings, and water content on the filling body. A 3-Hn-3 structural model was employed. When the number of hidden layers Hn was 7, the model achieved the best learning and training effect. The results show that the predicted value, which is close to the measured value (fitting accuracy of 92.43–99.92%; average error of 0.0792–7.5682%), satisfies the engineering requirements. The neural network model can be employed to predict the filling body’s strength and provide a good reference to analyze the change law in the filling body’s strength.
Environmental Collaboration under Various Contracts in the O2O Closed‐Loop Supply Chain
Authors: Bai Yang , Meng Xueying
Published: April 2022

This study considers the dynamic pricing and green advertising incentive decisions in the O2O closed‐loop supply chain. Through green advertising efforts, the manufacturer and the retailer push up the consumers’ environmental consciousness and the return rate of the end‐of‐use products. Based on the manufacture directing Stackelberg game theory, this research looks into three different green advertising decisions (noncooperative, unilateral cooperative, and bilateral cooperative) under wholesale price contract, revenue‐sharing contract, and financial support contract in the O2O CLSC. The results show that the bilateral cooperative incentive strategy always makes both the manufacturer and the retailer invest more in green advertising efforts and increase profits. Without affecting sales, implementing the bilateral cooperative incentive mechanism under the financial support contract is the best strategy to mitigate channel conflict, promote Pareto improvement, and ultimately achieve sustainable development goals.
Naringin Alleviates H2O2-Inhibited Osteogenic Differentiation of Human Adipose-Derived Stromal Cells via Wnt/β-Catenin Signaling
Authors: Yang Xufang , Dong Jianjiang , Hao Yankun , Qi Yucheng , Liang Jun , Yan Lei , Wang Wenting
Published: April 2022

Osteoporosis is an age-related systemic bone disease that places a heavy burden on patients and society. In this study, we aimed to investigate the effects of naringin (NAR) on the osteogenic differentiation of human adipose-derived stromal cells (ADSCs). The results demonstrated that NAR pretreatment effectively abated H2O2-induced cell death and ROS accumulation in ADSCs undergoing osteogenic differentiation (ADSCs-OD). In addition, we also observed that the impaired extracellular matrix mineralization and ALP activity in H2O2-stimulated ADSCs-OD were notably rescued by NAR pretreatment. Moreover, the effects of H2O2 exposure on Wnt/β-catenin signaling in ADSCs-OD were largely reversed by NAR pretreatment. Collectively, our findings indicated that NAR could protect ADSCs-OD against H2O2-inhibited osteogenic differentiation.
Fuzzy Reliability Analysis of the COVID‐19 Mortality Rate Using a New Modified Kies Kumaraswamy Model
Authors: Riad Fathy H. , Alruwaili Bader , Almetwally Ehab M. , Hussam Eslam
Published: April 2022

In this paper, we developed a novel superior distribution, demonstrated and derived its mathematical features, and assessed its fuzzy reliability function. The novel distribution has numerous advantages, including the fact that its CDf and PDf have a closed shape, making it particularly relevant in many domains of data science. We used both conventional and Bayesian approaches to make various sorts of estimations. A simulation research was carried out to investigate the performance of the classical and Bayesian estimators. Finally, we fitted a COVID‐19 mortality real data set to the suggested distribution in order to compare its efficiency to that of its rivals.
Multistate Reliability Analysis of Recessive Fault of Mechanical Meta-Action Unit Based on Markov Process
Authors: Ge Hongyu , Liu Yuan , Zhang Chuanwei , Yang Manzhi
Published: April 2022

In order to clearly express the reliability dynamic change process of mechanical meta-action units with recessive fault on continuous time series, this paper proposes a reliability analysis method for multistate systems with recessive failures of mechanical meta-action units based on the fusion of vibration signal analysis and Markov process. By analyzing the vibration signal, five major recessive fault types of mechanical meta-action units are determined. By analyzing the experimental data and based on the average time of the first occurrence of five major recessive faults, a performance level state representation model of mechanical meta-action unit based on fault importance weight is established. Then, based on the repairable characteristics of the mechanical element action unit, the two-way state transition model and state probability differential equation of mechanical meta-action units are established, and the state probability of each state is obtained. Next, under the condition of determining the initial state, the change process curves of the instantaneous availability, instantaneous average performance, and instantaneous average performance deficit of the mechanical meta-action unit are obtained by solving the reliability index calculation formula. Finally, this paper takes the worm rotation meta-action unit as an example to verify the law and probability of the state transition of the mechanical meta-action unit, and the performance level change accompanying the state transition process, by analyzing the failure modes and causes of recessive faults, the corresponding reliability control measures are formulated, and the control effects before and after reliability control are analyzed. The research results show that this method can effectively improve the accuracy of the state probability when calculating the state probability of each state, and compared with the discrete Markov model, when studying the reliability of complex multistate systems, this method can dynamically describe the change process of state probability, instantaneous availability, instantaneous average performance, and instantaneous average performance deficit in real time under the condition of continuous time-dependent variables. It has certain guiding significance for the reliability analysis of mechanical element action units in the long-term range.
Biochemical Characterization of Different Chemical Components of Parthenium hysterophorus and Their Therapeutic Potential against HIV‐1 RT and Microbial Growth
Authors: Jaiswal Jyotsna , Doharey Pawan Kumar , Singh Reetika , Tiwari Priyanka , Singh Nitika , Kumar Abhishek , Gupta Vivek Kumar , Siddiqui Arif Jamal , Sharma Bechan
Published: April 2022

Parthenium hysterophorus possesses certain allelochemicals responsible for their medicinal effects. The presence of oils, polyphenols, alkaloids, terpenes, pseudoguaianolides, and histamines in P. hysterophorus has been shown to exhibit medicinal properties. However, the systematic biomedical properties of this plant are still unexplored. The extracts of leaves, stem, and flower of P. hysterophorus, both at low and high temperatures (equivalent to boiling points of different solvents) were prepared. The extracts prepared in hexane, ethylacetate, methanol, and water were analyzed spectrophotometrically and colorimetrically and resolved on TLC for the presence of phytochemicals. The analyses of the free radical quenching potential of plant extracts were done by DPPH assay. The total antioxidant capacity was determined by phosphomolybdate assay and the ferric reducing antioxidant power (FRAP) assay was used to determine the reduction potential of the extracts. The spectrophotometric and qualitative analysis of plant extracts demonstrated the presence of alkaloids, terpenoids, carbohydrates, and cardiac glycosides. The occurrence of more than one Rf values for extracts determined by TLC indicated the presence of more than one phytochemical compound. The P. hysterophorus extracts contained strong antioxidant activity. These extracts exhibited strong antimicrobial activity against Staphylococcus epidermis, Salmonela typhi, Neisseria gonococci or gonococci, Citrobacter, and Shigella flexineri. The evaluation of the antimicrobial potential of P. hysterophorus extracts was done by the disc diffusion method. These extracts also showed significant inhibition against HIV‐1 RT activity. The anti‐HIV‐1 RT activity was done using Roche Kit. The P. hysterophorus extracts displayed the presence of many phytochemicals with strong antioxidant, antimicrobial, and anti‐HIV‐1 RT properties.
Pedestrian Fall Event Detection in Complex Scenes Based on Attention-Guided Neural Network
Authors: Geng Peng , Xie Hui , Shi Houqin , Chen Rui , Tong Ying
Published: April 2022

To address automatic detection of pedestrian fall events and provide feedback in emergency situations, this paper proposes an attention-guided real-time and robust method for pedestrian detection in complex scenes. First, the YOLOv3 network is used to effectively detect pedestrians in the videos. Then, an improved DeepSort algorithm is used to track by detection. After tracking, the authors extract effective features from the tracked bounding box, use the output of the last convolutional layer, and introduce the attention weight factor into the tracking module for final fall event prediction. Finally, the authors use the sliding window for storing feature maps and SVM classifier to redetect fall events. The experimental results on the CityPersons dataset, Montreal fall dataset, and self-built dataset indicate that this approach has good performance in complex scenes. The pedestrian detection rate is 87.05%, the accuracy of fall event detection reaches 98.55%, and the delay is within 120 ms.
The Development Status and Trend of Urban Smart Tourism Based on Internet of Things Technology
Authors: Zhang Yi , Li Qi
Published: April 2022

The rapid economic development and the improvement of people’s living standards have prompted the rapid development of tourism. And in the era of big data, it is inevitable for economic and technological development to apply IoT technology to various industries. The application of Internet of Things technology in the tourism industry conforms to the development of the times, promotes the upgrading of the industry, and also promotes the emergence and development of smart tourism. At present, the development of smart tourism provides convenient and timely services for people’s travel and also improves the management level and reception capacity of tourist destinations. Therefore, this paper conducts an in-depth discussion on the urban smart tourism based on the Internet of Things technology and summarizes the development status of urban tourism. And based on the current development situation to predict the future development trend of urban smart tourism and the future development plan of smart tourism, this paper discusses the current problems of smart tourism in order to promote the healthy and sustainable development of urban smart tourism. The research in this paper has great reference significance and practical guiding significance for the healthy and stable development of smart tourism in the future.
Improved Blockchain Technology for Performance Optimization Model Design of Sports Clubs
Authors: Wang Shuang
Published: April 2022

Professional sports clubs are an important part of professional sports, and their development has a certain role in promoting the strength of China's competitive sports and the prosperity of the sports industry. However, the development of professional sports clubs in China is still facing many challenges. Among them, the key to their long-term development is to improve their financing ability and solve their financing difficulties in order to improve the performance of professional sports club. At present, research on the financing methods and financing difficulties of professional sports clubs has achieved certain results, but no systematic research on the application of blockchain technology to solve the pain points of professional sports club financing has been established. This study systematically compares the current situation and development problems of financing for professional sports clubs in China from the new perspective of combining blockchain and professional sports club financing. In addition, this article proposes a solution for the application of blockchain technology to optimize the performance of professional sports club, which has important theoretical and practical significance for promoting the development of professional sports clubs.
Combination of Blockchain and AI for Music Intellectual Property Protection
Authors: Li Na
Published: April 2022

In the last two years, due to the pandemic and restrictive measures, the dependence of music creators and artists on the Internet, where they could promote their work, organize live streaming concerts, and talk to the public, has increased and expanded even more and seeks higher revenue from digital music platforms. An important issue that arises from the above statement is protecting the authors’ copyright regarding the uses and sharing in the digital services of their works with protected content. Although circulated in digital information, the protected content is not information but a product of ethical and commercial value. While it has an intangible owner and it owes its existence to the creative idea of its creator, it is not an idea. The imposition of legal and commercial conditions on its movement cannot be associated with any restrictions on the free movement of information, as it is not related to them. In general, the unauthorized exchange of digital music files via peer-to-peer violates copyright law. The exchange of files is unauthorized, as it does not have the relevant permission from the creators and beneficiaries and is therefore illegal. With this in mind, this paper proposes a highly effective way of protecting the copyright of music technology, which is based on the widespread use of artificial intelligence, blockchain, and cryptography technologies. Specifically, an advanced blockchain model based on Hyperledger Fabric is introduced, which, however, uses Quantum Homomorphic Encryption and Quantum Zero-Knowledge Arguments. Music files are implemented as Nonfungible Tokens (NFTs), which activate smart contracts. Finally, an advanced collaborative filtering algorithm provides recommendations for effectiveness in securing the copyrights of music industry creators. A specialized scenario was built to model the proposed system to verify the degree of protection on music intellectual property in developing a security simulation with an innovative consensus-based zero knowledge and the quantum fully homomorphic encryption technique. Experiment results show that this technique can aid in implementing a technologically aware system capable of providing a powerful answer to a current real-world problem.
Research on Efficient Top-
k
Query Based on ARIMA Time Series Model
Authors: Gu Fenfei , Hu Xiande
Published: April 2022

In wireless sensor networks, Top-

k

query is often used to query the first

k

values which can satisfy the user’s criteria. In the process of query, in order to reduce the transmission of redundant data, different filtering windows are set for each sensor node. However, the filter windows often update because of the dynamic change of the data which will cause the huge consumption of the node energy. In order to solve this problem, a new algorithm based on ARIMA is proposed called AAFU (ARIMA approach based on the filter updating) in this paper. With this algorithm which is based on the FILA algorithm and ARIMA model, the base node can use time series model to predict the future data according to the collected historical data. This algorithm can reduce energy consumption that can make the sensor network effectively deal with the node window updating through comparison to decide whether update the filtering windows. The simulation results show that AAFU algorithm is superior FAPU algorithm with ensuring the accuracy of query.
Innate Immunity Stimulation during the COVID-19 Pandemic: Challenge by Parvulan
Authors: Palmieri Beniamino , Manenti Antonio , Galotti Francesca , Vadalà Maria
Published: April 2022

Aim. We report an open spontaneous anecdotical retrospective survey of Corynebacterium parvum administration to 4000 fragile immune-depressed and multimorbid patients treated with a killed C. parvum strain to enhance innate immunity, integrating the adaptative immune response for long-standing antinfectious resistance. Methods. A total of 4000 patients (1900 men and 2100 women) with mild, moderate, or chronic disease, appealing to our Second Opinion Medical Consultation Network, signed an informed consent form and were injected subcutaneously with C. parvum. The treatment was followed up to 6 months, completing the short form of the medical outcome health survey questionnaire (SF-36) directly by the patients or their parents and monitoring their health status regularly via telemedicine (Skype, WhatsApp, mail, etc.) or outpatients visits. Results. The main efficacy endpoints, as assessed by the SF-36 questionnaire are: significant improvements in the mental and physical role functioning score (

p
<
0.02

), better general health; social role performance (

p
<
0.02

), vitality (

p
<
0.03

), and a significant pain reduction (

p
<
0.03

). A quick (48-72 hours) symptoms improvement and/or complete regression of the herpetic eruptions was observed in 1000 affected patients with disappearance or relieve of herpetic neuralgia (reduced in 80% of cases); also full recovery or frequency reduction (30%) of recurrent cystitis and prostatitis in 120 affected patients. Last but not least, a life quality improvement in 100 oncologic patients of overall 200 cases. A significant increase in the lymphocyte count (

p
<
0.01

), mainly helper and killer lymphocytes, was noted 6 months after Parvulan injection vs. the baseline. The asymptomatic SARS-CoV-2 patients who were incidentally enrolled in our survey were tested at the sixth month for antibodies against SARS-CoV-2, and 14 patients had high levels of SARS-CoV-2 antibodies. The incubating COVID infections of the Parvulan-injected patients even if frail and multimorbid recovered in short term (48-96 hours) with a benign clinical course, without need of drugs administration except for the variants, such as Delta and Omicron, whose infections lasted on average one week and required some antipyretics and low-dose steroids for a few days. Conclusions. Our results confirm that C. parvum is quite safe and effective in supporting immune-compromised patients when epidemic or pandemic events increase the life risk and any kind of infection and complication rate. Further double-blind placebo evidence-based studies are urgently required, and our numerically substantial not sponsored spontaneous observation is exclusively intended to promote further evidence-based double-blind institutional studies.
Comparative Pharmacokinetics of Seven Major Compounds in Normal and Atherosclerosis Mice after Oral Administration of Simiao Yong’an Decoction
Authors: Sun Ke-han , Yang Man-fang , Xu Xin-rui , Li Yang , Gao Zhao , Zhang Qing-yue , Li Hui , Wang Shu-qi , Lou Li-xia , Wu Ai-ming , Jin Qiu-shuo , Wu Sheng-xian , Nie Bo
Published: April 2022

Simiao Yong’an decoction (SMYAD), a classic traditional Chinese medicine formula, has been used to treat atherosclerosis (AS) in clinical in China, but its therapeutic mechanism and pharmacodynamic material basis are not clear. In this study, the AS model was caused by a high-fat diet and perivascular carotid collar placement (PCCP), and SMYAD was orally administered to the model and normal mice. A rapid, sensitive, selective, and reliable method using ultrahigh-performance liquid chromatography (UHPLC) system combined with a Q Exactive HF-X mass spectrometer (UHPLC-Q Exactive HF-X MS) was established and validated for the simultaneous determination of seven compounds, including harpagide, chlorogenic acid, swertiamarin, sweroside, angoroside C, liquiritin, and isoliquiritigenin in the plasma of normal and AS mice. The specificity, linearity, precision, accuracy, recovery, and stability of the method were all within the acceptable criteria. The results showed that some pharmacokinetic behaviors of harpagide, chlorogenic acid, and isoliquiritigenin were significantly different among the two groups of mice. The specific parameter changes were harpagide (AUC0–t and AUC0–∞ were 11075.09 ± 2132.38 and 16221.95 ± 5622.42 ng·mL−1·h, respectively; CLz/F was 2.45 ± 0.87 L/h/mg), chlorogenic acid (t1/2 was 21.59 ± 9.16 h; AUC0–∞ was 2637.51 ± 322.54 ng·mL−1·h; CLz/F was 13.49 ± 1.81 L/h/mg) and isoliquiritigenin (AUC0–t and AUC0–∞ were 502.25 ± 165.65 and 653.68 ± 251.34 ng·mL−1·h, respectively; CLz/F was 62.16 ± 23.35 L/h/mg) were altered under the pathological status of AS. These differences might be partly ascribed to the changes in gastrointestinal microbiota, nonspecific drug transporters, and cytochrome P450 activity under the AS state, providing research ideas and experimental basis for pharmacological effects and pharmacodynamic material basis.
Efficacy and Mechanism of Mallotus furetianus Müll. Arg. Extract on Nonalcoholic Fatty Liver Disease
Authors: Lin Daobin , Ding Yi , Cheng Yabo , Chen Yubin , Tang Yunting , Wu Xiaowen , Cheng Yawei
Published: April 2022

Nonalcoholic fatty liver disease (NAFLD) is currently the major cause of chronic liver disease globally. To observe the sedative effect of Mallotus furetianus extract (MFE) on NAFLD and the potential molecular mechanism, a high-fat diet (HFD) was used to induce NAFLD in rats for 8 weeks. Rats were orally given MFE (1.7 g/kg, 2.5 g/kg, and 3.3 g/kg) every day. Serum and liver biochemical indexes were detected. 16S rDNA sequencing was performed to test the changes in the gut microbiota. Mass spectrometry was used to analyze the changes in blood and liver metabolites and to perform a joint analysis of differential flora and differential metabolites. The results showed that MFE alleviated liver injury and decreased hepatic lipids content. ELISA analysis certificated that MFE reduced inflammation levels in rats fed with HFD. Compared to HFD rats with a normal diet, MFE significantly changed the overall structure of the intestinal flora and the composition of the intestinal microbes destroyed by HFD. In addition, MFE changes the metabolic levels of lipids and proteins in HFD rats. In conclusion, MFE effectively treated NAFLD and significantly improved the overall structure and intestinal microbial composition of the intestinal microbiota. The abundance of Bacteroides fragilis and Escherichia coli increased significantly in the partridge tea treatment group.
A Novel Risk Score Model Based on Eleven Extracellular Matrix-Related Genes for Predicting Overall Survival of Glioma Patients
Authors: Li Xiaodong , Wang Yichang , Wu Wei , Xiang Jianyang , Qi Lei , Wang Ning , Wang Maode , Yu Hai
Published: April 2022

Gliomas are the most common lethal primary brain tumors with variable survival outcomes for patients. The extracellular matrix (ECM) is linked with clinical prognosis of glioma patients, but it is not commonly used as a clinical indicator. Herein, we investigated changes in ECM-related genes (ECMRGs) via analyzing the transcriptional data of 938 gliomas from TCGA and CGGA datasets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, a 11-ECMRG signature that is strongly linked with overall survival (OS) in glioma patients was identified. This signature was characterized by high-risk and low-risk score patterns. We found that the patients in the high-risk group are significantly linked with malignant molecular features and worse outcomes. Univariate and multivariate Cox regression analyses suggested that the signature is an independent indicator for glioma prognosis. The prediction accuracy of the signature was verified through time-dependent receiver operating characteristic (ROC) curves and calibration plots. Further bioinformatics analyses implied that the ECMRG signature is strongly associated with the activation of multiple oncogenic and metabolic pathways and immunosuppressive tumor microenvironment in gliomas. In addition, we confirmed that the high-risk score is an indicator for a therapy-resistant phenotype. In addition to bioinformatics analyses, we functionally verified the oncogenic role of bone morphogenetic protein 1 (BMP1) in gliomas in vitro.
Dual-Channel Pricing of Online Retail Based on Evolutionary Game Theory and Coevolutionary Algorithm
Authors: Song Yejiao , Ji Chengjun
Published: April 2022

For the offline market entry strategy and product pricing of online retailers in the retailer competitive supply chain structure, a supply chain game model composed of a single manufacturer, a single traditional retailer, and a single online retailer is constructed and an improved coevolutionary game algorithm (ICGA) is proposed to solve the model. In addition, a dynamic optimization model based on dissatisfaction is established to dynamically adjust the coevolutionary game algorithm. The research shows that when online retailers open offline channels, with the increase of offline market share of online retailers, the profits of traditional retailers decrease and manufacturers increase, while the profits of online retailers increase first and then decrease; there is a maximum point. The profits of online retailers, manufacturers, and supply chains will be optimized due to the opening of offline channels. When the offline market share of online retailers is lower than a specific threshold, the new offline channels of online retailers will also make the profits of traditional retailers greater. At this time, the profits of all members of the supply chain will be optimized. Finally, the final optimization stability strategy can make the offline retail and online retail achieve the best operation state, and the overall theoretical rate of supply chain has been significantly improved, which proves the effectiveness and superiority of the proposed model and algorithm.
Network Pharmacological Analysis and Experimental Study of the Antipharyngitis Mechanism of the Chaiqin Qingning Capsule
Authors: Wang Can , Gao Hongjin , Huang Lianzhan , Wang Zhen , Ding Xuansheng
Published: April 2022

Objective. The study aimed to explore the active composition and mechanism of the Chaiqin Qingning capsule (CQQN) against pharyngitis based on the network pharmacology and through using a pharyngitis rat model. Methods. The active ingredients and targets of CQQN were queried using the TCMSP database. Disease‐related target genes were queried in the DrugBank, GeneCards, OMIM, and DisGeNEt databases using “pharyngitis” as the search term. The STRING database was used to establish a protein‐protein interaction (PPI) network. GO function enrichment and KEGG pathway enrichment analyses were performed to identify active components and key targets. Cytoscape software (version 3.7.2) was used to construct an active component/target gene/enrichment pathway network. AutoDock software was used to select the best binding target for molecular docking. The effect of CQQN was verified in the pharyngitis rats. Results. Network pharmacology showed 30 active compounds in CQQN with 240 targets, including 54 for the treatment of pharyngitis. Potential active ingredients included quercetin, kaempferol, stigmasterol, saikosaponin D, and isorhamnetin. The key targets involved were AKT1, TNF, IL‐6, and IL‐1β. Signaling pathways included virus infection, TNF, IL‐17, and cancer pathways. The molecular docking results showed that the critical components in CQQN had good potential for binding to key target genes. Animal experiments showed that CQQN could significantly reduce the expression of TNF‐α, IL‐1β, IL‐6, and IL‐17 in the serum of rats with pharyngitis (P < 0.05). Hematoxylin and eosin staining showed that the inflammatory state of pharyngeal tissue in rats was significantly reduced compared to that in the model group. Conclusion. CQQN can improve pharyngitis by regulating the TNF and IL‐17 signaling pathways. The study makes a positive exploration and provides a new idea for a more comprehensive and in‐depth excavation of CQQN with an intervention effect on pharyngitis and other upper respiratory diseases in the future.
Effect of Agricultural Crop Wastes as Partial Replacement of Cement in Concrete Production
Authors: Zerihun Behailu , Yehualaw Mitiku Damtie , Vo Duy-Hai
Published: April 2022

The main goal of this review paper was to study the physical and chemical properties of agricultural crop wastes and their potentials to partially replace in production of mortar and concrete. Bamboo leaves’ ashes (BLA), banana leaves’ ashes (BNLA), corncob leaves’ ashes (CCA), groundnut ashes (GNA), rice husk ashes (RHA), and sugarcane bagasse ashes (SCBA) were selected to be reviewed. The chemical composition of these agricultural wastes revealed that the they are basically composed of alumino‐siliceous materials conforming the requirement of pozzolanic materials. Compared with cement, they have finer particle size and higher surface area. The effect of partial replacement of cement by BLA, BNLA, CCA, GNA, RHA, and SCBA on the properties of fresh concrete (consistency, workability, and setting time), mechanical properties (compressive and tensile strength), microstructure (scanning electron microscopy, derivative thermogravimetry, and X‐ray diffraction), and durability (ultrapulse velocity, water absorption, and sulphate attack) of concrete was recapped. The inclusion of BLA, CCA, RHA, and SCBA as partial replacement of cement reduced the workability of concrete due to their finer particle size. The mechanical performance of concrete showed enhancement with partial replacement of cement by BLA, RHA, and SCBA due to the involvement of amorphous silica in secondary reaction to produce densified tetrahedral gel. Consequently, uniform and denser morphology was formed, and durability was enhanced as densification of hydrated gel increased. In general, the review exposed that agricultural crop wastes satisfy pozzolanic material properties and have strong potential to replace cement up to 10% without compromising the performance of concrete.
Distal Humerus Fracture Malunion in Adults: A Case Report and a Review of the Literature
Authors: Stavrakakis Ioannis M. , Ntontis Zisis , Kastritsi Olga , Chaniotakis Constantinos , Alpantaki Kalliopi , Kastanis Grigorios
Published: April 2022

Introduction. Neglected distal humerus fractures are rare injuries, which the orthopaedic surgeon will not deal many times in his career. We present a case of a young patient with such an injury, who was treated with a corrective osteotomy and fixation, resulting in a very good outcome. This case report highlights the importance of surgical intervention for distal humerus fracture malunion. A narrative review of the literature regarding this topic is presented as well. Case Report. A 42-year-old female patient presented to our department nine weeks after a displaced distal humerus fracture, which was treated conservatively in another institution. Ulnar nerve neuropathy, pain, and severe stiffness of the elbow were her main symptoms. Open correction of the deformity, anatomical reduction of the articular surface, and realignment of the metaphyseal level of the fracture were performed. Six months postoperation, a painless flexion-extension arc of 110° and a normal rotation of the forearm was achieved. Conclusion. Distal humerus fracture malunion is a challenge. The operation needed for this purpose is much more demanding, and postsurgical complications are more likely to occur as opposed to the treatment of acute fractures. If a proper surgery is performed though, a good clinical outcome can be expected.
LCZ696 Ameliorates Isoproterenol-Induced Acute Heart Failure in Rats by Activating the Nrf2 Signaling Pathway
Authors: Hou Min , Lu Linxin , Wu Xiaobo , Liu Hongxuan
Published: April 2022

Objective. LCZ696 (sacubitril/valsartan) is an angiotensin II (Ang II) type 1 receptor-neprilysin inhibitor, with effects of immunosuppression, anti-inflammation, antiapoptosis, and antioxidation. The present study was aimed at determining whether LCZ696 has a protective effect against isoproterenol-induced acute heart failure (AHF) in rats. Methods. SD rats were randomly divided into four groups: control group, HF group, LCZ696 group, and enalapril group. The cardiac function of rats was evaluated using echocardiographic parameters, heart weight (HW), serum levels of cardiac troponin I (cTnI), and lactate dehydrogenase (LDH). HE is staining, which was used to determine the pathological damage of rat myocardial tissue. Also, we measured oxidative stress markers including reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD), and catalase (CAT). Finally, the expression of Nrf2 signaling pathway-related proteins was determined using Western blot. Results. Compared with the HF group, LCZ696 could significantly improve cardiac function and myocardial injury in rats and reduce AHF-induced oxidative stress. In addition, the results of Western blot confirmed that LCZ696 could upregulate the expression of Nrf2 and HO-1 while decreasing Keap1 expression. Conclusion. LCZ696 ameliorates isoproterenol-induced AHF in rats by alleviating oxidative stress injury and activating the Nrf2 signaling pathway.
Research on Feature Extraction and Chinese Translation Method of Internet-of-Things English Terminology
Authors: Li Huasu
Published: April 2022

Feature extraction and Chinese translation of Internet-of-Things English terms are the basis of many natural language processing. Its main purpose is to extract rich semantic information from unstructured texts to allow computers to further calculate and process them to meet different types of NLP-based tasks. However, most of the current methods use simple neural network models to count the word frequency or probability of words in the text, and it is difficult to accurately understand and translate IoT English terms. In response to this problem, this study proposes a neural network for feature extraction and Chinese translation of IoT English terms based on LSTM, which can not only correctly extract and translate IoT English vocabulary but also realize the feature correspondence between English and Chinese. The neural network proposed in this study has been tested and trained on multiple datasets, and it basically fulfills the requirements of feature translation and Chinese translation of Internet-of-Things terms in English and has great potential in the follow-up work.
Effect of Viscosity on Stopping Power for a Charged Particle Moving above
Two-Dimensional Electron Gas
Authors: Chen Lei , Wang Yu , Jia Yuesong , Yang Xianjun , Li Chunzhi , Yi Lin , Jiang Wei , Zhang Ya , Hoffmann Dieter H.H.
Published: April 2022

In two-dimensional (2D) electron systems, the viscous flow is dominant
when electron-electron collisions occur more frequently than the impurity or
phonon scattering. In this work, a quantum hydrodynamic model, considering
viscosity, is proposed to investigate the interaction of a charged particle
moving above the two-dimensional viscous electron gas. The stopping power,
perturbed electron gas density, and the spatial distribution of the velocity
vector field have been theoretically analyzed and numerically calculated.
The calculation results show that viscosity affects the spatial distribution
and amplitude of the velocity field. The stopping power, which is an
essential quantity for describing the interactions of ions with the 2D
electron gas, is calculated, indicating that the incident particle will
suffer less energy loss due to the weakening of the dynamic electron
polarization and induced electric field in 2D electron gas with the
viscosity. The values of the stopping power may be more accurate after
considering the effect of viscosity. Our results may open up new
possibilities to control the interaction of ions with 2D electron gas in the
surface of metal or semiconductor heterostructure by variation of the
viscosity.
Conversion of Fructose to 5‐Hydroxymethyl Furfural: Mathematical Solution with Experimental Validation
Authors: Sajid Muhammad , Chowdhury Apu , Bary Ghulam , Guoliang Yin , Ahmad Riaz , Khan Ilyas , Ahmed Waqar , Saleem Khan Muhammad Farooq , Alqahtani Aisha M. , Alam Md. Nur
Published: April 2022

Conversion of fructose to furan aldehydes is a rapidly developing concept considering the emergent scenario of the replacement of fossil‐derived components to biomass‐derived green precursors. 5‐hydroxymethyl furfural (HMF) and levulinic acid (LA) are the two most important bio‐precursors with expanded downstream utilization in modern industries. Their production from biomass‐derived sugars is a complex reaction due to competitive side reactions with a variety of byproducts. Therefore, their simulated optimization is an important tool that can help for process optimization in an economical way. In this article, we have developed a mathematical solution for fructose conversion, HMF production, and levulinic acid (LA) formation in a reactive environment. The accuracy of the developed model is further verified through experiments and found satisfactory with high accuracy. Therefore, the developed model can be used to simulate the reaction environment and product optimization under a given set of conditions.
Robust Spectral Clustering via Low-Rank Sample Representation
Authors: Liang Hao , Guan Hai-Tang , Abhadiomhen Stanley Ebhohimhen , Yan Li
Published: April 2022

Traditional clustering methods neglect the data quality and perform clustering directly on the original data. Therefore, their performance can easily deteriorate since real-world data would usually contain noisy data samples in high-dimensional space. In order to resolve the previously mentioned problem, a new method is proposed, which builds on the approach of low-rank representation. The proposed approach first learns a low-rank coefficient matrix from data by exploiting the data’s self-expressiveness property. Then, a regularization term is introduced to ensure that the representation coefficient of two samples, which are similar in original high-dimensional space, is close to maintaining the samples’ neighborhood structure in the low-dimensional space. As a result, the proposed method obtains a clustering structure directly through the low-rank coefficient matrix to guarantee optimal clustering performance. A wide range of experiments shows that the proposed method is superior to compared state-of-the-art methods.
Application of New Energy Composites in Sports Facilities and Fitness Equipment
Authors: Zhang Jiejian , Mi Yang
Published: April 2022

New energy composite is an advanced material that can replace the traditional single metal material, especially the newly developed or developed material with superior performance. At present, the material is widely used in automobile manufacturing and other fields, but there is a lack of development and application of the material in the field of sports. To solve these problems, this paper puts forward the application of new energy composites in sports facilities and fitness equipment. In this paper, the testing methods of new energy materials, including thermoelectric testing method and energy absorption testing method, can quantitatively evaluate the properties of a kind of new energy composites and point out the direction for the development of materials with high thermoelectric properties and high‐energy absorption and consumption properties. Then, through the preparation experiment of new energy materials, this paper studies the preparation process and self‐shrinkage test of carbon nanomaterials. Finally, it is proposed to use carbon nanomaterials in the sensor design of health monitoring data acquisition system. The experimental results show that when the water ash ratio is low, the self‐shrinkage value of carbon nanomaterial slurry is 74.68% lower than that under the original conditions. In the experimental study of omnidirectional monitoring characteristics, through the repeated tensile tests of rectangular sensor and omnidirectional sensor at different temperatures, it is found that the sensitivity coefficient of the sensor made of carbon nanomaterial is between 55.8 and 60.4, and the maximum fluctuation is only 5. This fully proves that the carbon nanotube omnidirectional sensor has omnidirectional detection ability.