bluuebunny/crossref_metadata_2025 huggingface dataset:
“China–US Plus” Triangular Development Cooperation: Challenges and Opportunities
Authors: Hu Yong
Published: September 2022

The “China–US Plus” model of development cooperation refers to the triangular pattern between China, the largest South–South Cooperation partner, the United States, the largest traditional donor, and recipient nations in the developing world. Since the beginning of the 21st century, China and the United States have strengthened policy coordination on international development, and carried out several pilot projects including the “China–US Plus Afghanistan” triangular cooperation on human resources, “China–US Plus Timor-Leste” triangular cooperation on agriculture and the “China–US Plus Africa” triangular cooperation on public health which have already yielded early harvests. However since 2018, the “China–US Plus” cooperation has encountered challenges from both the setbacks in the field of international development and the United States’ increasingly tougher policies toward China. At a time when the COVID-19 pandemic has severely undermined global development achievements, it is of great significance for China and the United States to resume and strengthen triangular development cooperation. By urging Washington to restart the bilateral dialogue on development cooperation, encouraging Chinese and US civil society organizations to initiate non-traditional “China–US Plus” cooperation and strengthening coordination and connection with aid recipients, Beijing can play a constructive role to put the “China–US Plus” cooperation back on track as early as possible.
Subcubic Equivalences between Graph Centrality Problems, APSP, and Diameter
Authors: Abboud Amir , Grandoni Fabrizio , Vassilevska Williams Virginia
Published: September 2022

Measuring the importance of a node in a network is a major goal in the analysis of social networks, biological systems, transportation networks, and so forth. Different
centrality
measures have been proposed to capture the notion of node importance. For example, the
center
of a graph is a node that minimizes the maximum distance to any other node (the latter distance is the
radius
of the graph). The
median
of a graph is a node that minimizes the sum of the distances to all other nodes. Informally, the
betweenness centrality
of a node
w
measures the fraction of shortest paths that have
w
as an intermediate node. Finally, the
reach centrality
of a node
w
is the smallest distance
r
such that any
s
-
t
shortest path passing through
w
has either
s
or
t
in the ball of radius
r
around
w
.

The fastest known algorithms to compute the center and the median of a graph and to compute the betweenness or reach centrality even of a single node take roughly cubic time in the number
n
of nodes in the input graph. It is open whether these problems admit truly subcubic algorithms, i.e., algorithms with running time Õ(n
3-δ
) for some constant δ > 0.

1

We relate the complexity of the mentioned centrality problems to two classical problems for which no truly subcubic algorithm is known, namely All Pairs Shortest Paths (APSP) and Diameter. We show that Radius, Median, and Betweenness Centrality are
equivalent under subcubic reductions
to APSP, i.e., that a truly subcubic algorithm for any of these problems implies a truly subcubic algorithm for all of them. We then show that Reach Centrality is equivalent to Diameter under subcubic reductions. The same holds for the problem of approximating Betweenness Centrality within any finite factor. Thus, the latter two centrality problems could potentially be solved in truly subcubic time, even if APSP required essentially cubic time.

On the positive side, our reductions for Reach Centrality imply an improved Õ(Mn
ω
)-time algorithm for this problem in case of non-negative integer weights upper bounded by
M
, where ω is a fast matrix multiplication exponent.
Optimizing Substance Use Treatment Selection Using Reinforcement Learning
Authors: Baucum Matt , Khojandi Anahita , Myers Carole , Kessler Larry
Published: September 2022

Substance use disorder (SUD) exacts a substantial economic and social cost in the United States, and it is crucial for SUD treatment providers to match patients with feasible, effective, and affordable treatment plans. The availability of large SUD patient datasets allows for machine learning techniques to predict patient-level SUD outcomes, yet there has been almost no research on whether machine learning can be used to
optimize
or
personalize
which treatment plans SUD patients receive. We use contextual bandits (a reinforcement learning technique) to optimally map patients to SUD treatment plans, based on dozens of patient-level and geographic covariates. We also use near-optimal policies to incorporate treatments’ time-intensiveness and cost into our recommendations, to aid treatment providers and policymakers in allocating treatment resources. Our personalized treatment recommendation policies are estimated to yield higher remission rates than observed in our original dataset, and they suggest clinical insights to inform future research on data-driven SUD treatment matching.
Code Generation for Neural Networks Based on Fixed-point Arithmetic
Authors: Benmaghnia Hanane , Martel Matthieu , Seladji Yassamine
Published: September 2022

Over the past few years, neural networks have started penetrating safety critical systems to make decisions as, for example, in robots, rockets, and autonomous driving cars. Neural networks based on floating-point arithmetic are very time and memory consuming, which are not compatible with embedded systems known to have limited resources. They are also very sensitive to the precision in which they have been trained, so changing this precision generally degrades the quality of their answers. To deal with that, we introduce a new technique to generate a fixed-point code for a trained neural network. This technique is based on fixed-point arithmetic with mixed-precision. This arithmetic is based on integer operations only, which are compatible with small memory devices. The obtained neural network has the same behavior as the initial one (based on the floating-point arithmetic) up to an error threshold defined by the user. The experimental results show the efficiency of our tool
SyFix
in terms of memory saved and the accuracy of the computations.
[Retracted] Investigation on the Effect of Hemodialysis Combined with MDT Multimode Intervention on Renal Fibrosis Degree and Renal Function Improvement in Uremia Patients
Authors: Fan Jingjing , Lu Peng , Zhang Lei , Zhang Yu , Yuan Bo , Zhu Yitang
Published: September 2022

The effect of hemodialysis combined with MDT intervention on the degree of renal fibrosis and renal function in uremia patients was studied. 118 patients with uremia admitted to the hospital were selected as the research object, and they were divided into two groups according to the random number table method, 59 cases in the control group and 59 cases in the experimental group. The control group was treated with hemodialysis, and the experimental group was treated with MDT multimode intervention on the basis of hemodialysis. The differences in renal fibrosis, renal function, and satisfaction after treatment were compared before treatment and at 1, 3, and 6 months after treatment. The experimental results showed that hemodialysis combined with MDT multimode intervention in uremia patients could reduce renal fibrosis and improve renal function and improve clinical satisfaction evaluation.
In Vitro Cercaricidal and Schistosomicidal Activities of the Raffia Wine and Hydroethanolic Extracts of Pedilanthus tithymaloides Linn (Poit). Stem Barks
Authors: Tienga Nkondo Emilienne , Boukeng Jatsa Hermine , Gipwe Feussom Nestor , Kenfack Mérimé Christian , Membe Femoe Ulrich , Tamdem Guetchueng Stephanie , Kopa Kowa Theodora , Kamtchouing Pierre , Tchuem Tchuente Louis-Albert
Published: September 2022

Schistosomiasis control remains a public health concern, and there is a need to evaluate new strategies for targeting larval and adult stages of the parasite. As Pedilanthus tithymaloides is empirically used to treat schistosomiasis, it becomes essential to know its effective action scientifically. This study assessed the cercaricidal and schistosomicidal activity of P. tithymaloides stem barks raffia wine extract (RwPt) and hydroethanolic extract (HePt). Different concentrations of these extracts were tested against cercariae (31.25–1000 μg/mL) and adult worms (62.5–2000 μg/mL) of Schistosoma mansoni. Niclosamide-olamine 5% (1 μg/mL) and praziquantel (10 μg/mL) were used as pharmacological controls. Cercariae viability was determined every 30 min for 180 min, and adult worms’ motor activity and viability after 24 and 48 h incubation. In addition, cytotoxicity and phytochemical analysis were performed. HePt was lethal to cercariae and adult worms with LC50 of 73.91 μg/mL after 60 min of incubation and 731.17 μg/mL after 48 h of incubation, respectively. Furthermore, a significant reduction of 94.44% in motor activity was observed in surviving worms at the concentration of 2000 μg/mL. RwPt was less effective on S. mansoni cercariae with an LC50 of 617.86 μg/mL after 180 min and on adult worms with a mortality rate of 9.83% at 2000 μg/mL for 48 h incubation. Both extracts showed a weak cytotoxicity profile with an IC50 of 983.50 μg/mL for HePt and more than 1000 μg/mL for RwPt. The LC-MS analysis of HePt allowed the detection of two annotated diterpenoids. Based on the selectivity index, the hydroethanolic extract of P. tithymaloides stem barks disclosed an intense cercaricidal activity and a moderate schistosomicidal effect with low cytotoxicity. These findings may support the potential use of Pedilanthus tithymaloides as a natural product or a source of natural-derived compounds for interrupting schistosomiasis transmission.
Potential Biomarkers and Signaling Pathways Associated with the Pathogenesis of Primary Ameloblastoma: A Systems Biology Approach
Authors: Bayat Zeynab , Mirzaeian Azin , Taherkhani Amir
Published: September 2022

Objective. Ameloblastoma is a benign odontogenic tumor that may lead to ameloblastic carcinoma. This study aimed to determine potential signaling pathways and biological processes, critical genes and their regulating transcription factors (TFs), and miRNAs, as well as protein kinases involved in the etiology of primary ameloblastoma. Methods. The dataset GSE132472 was obtained from the GEO database, and multivariate statistical analyses were applied to identify differentially expressed genes (DEGs) in primary ameloblastoma tissues compared to the corresponding normal gingiva samples. A protein-protein interaction (PPI) map was built using the STRING database. The Cytoscape software identified significant modules and the hub genes within the PPI network. Gene Ontology annotation and signaling pathway analyses were executed by employing the DAVID and Reactome databases, respectively. Significant TFs and miRNAs acting on the hub genes were identified using the iRegulon plugin and MiRWalk 2.0 database, respectively. A protein kinase enrichment analysis was conducted using the online Kinase Enrichment Analysis 2 (KEA2) web server. The approved drugs acting on the hub genes were also found. Results. A total of 1,629 genes were differentially expressed in primary ameloblastoma (Pvalue <0.01 and |Log2FC| > 1). HRAS, CDK1, MAPK3, ERBB2, COL1A1, CYCS, and BRCA1 demonstrated high degree and betweenness centralities in the PPI network. E2F4 was the most significant TF acting on the hub genes. BTK was the protein kinase significantly enriched by the TFs. Cholesterol biosynthesis was considerably involved in primary ameloblastoma. Conclusions. This study provides an intuition into the potential mechanisms involved in the etiology of ameloblastoma.
[Retracted] Model Construction of the Influence of Tourist Destination Image on Tourists’ Word‐of‐Mouth Communication Based on Cognitive and Emotional Environment
Authors: Zhang Su
Published: September 2022

Tourists base a significant portion of their decisions on the perception they have of a place, which is formed over a protracted period of time and serves as the foundation for word‐of‐mouth marketing. It is uncommon to talk about how to include travellers’ word‐of‐mouth into tourism research. At the same time, the perception of a travel place has an impact on the entire travel experience as well as posttravel behaviour. This research investigates the impact of tourist destination image on travellers’ word‐of‐mouth communication, and it builds a model of travellers’ tourism awareness word‐of‐mouth communication based on the dual perspectives of cognition and emotion. The study demonstrates that the age of tourists accounted for the biggest proportion—up to 84.3%—among the three parameters influencing word‐of‐mouth communication among tourists. Second, the highest percentage for tourists’ educational attainment is 61.1%; third, the highest percentage for tourists’ occupation is 55.4%, demonstrating that age is the primary factor. The kind and strength of visitors’ emotional reactions to the environment at their destination are determined by their emotions and the surrounding environment. Integration, which is reflected in tourists’ tolerance and proximity to the destination environment, is the cognitive assessment of their perception of the location.
Construction and Quality Control of Subway Wet Loess in Concealed Tunnel Based on Particle Swarm Optimization Algorithm
Authors: Wang Wen , Zhang Xin , Liu Xiaoning
Published: September 2022

To reduce urban pressure, urban rail transit has become an effective way to reduce traffic congestion, mitigate traffic accidents, reduce environmental pollution, and improve commuting efficiency. Subway as the main means of urban public transport travel, in recent years by people’s favor, although the construction industry of rail transit is developing rapidly and the industry scale is expanding, but because the construction of rail transit construction projects is very difficult, especially in the wet loess within the concealed excavation tunnel, but also frequent accidents, and the quality of the project is not easy to guarantee, so the underground railroad wet loess within the concealed excavation tunnel construction technology is poor. Therefore, it is especially important to study the construction technology and project quality management of underground railway concealed tunnel in wet loess. In this paper, based on the in-depth study of the basic principle of quantum particle swarm optimization calculation and the realization of key engineering technologies, the particle swarm optimization algorithm is programed using MATLAB software, and the coding scheme, operation specification, and operation parameters are designed. Then, combined with the particle swarm optimization algorithm and assisted MATLAB software, the main analysis of the construction and quality control of wet loess in concealed tunnels of subway projects was carried out, mainly by systematizing the relationship between the three major elements of subway project schedule, cost, and management and the construction and quality control of wet loess in concealed tunnels of a subway, and concluded that the construction and quality control of wet loess in concealed tunnels of the subway needed schedule. It is concluded that the construction and quality control of subway wet loess tunnel requires stable schedule, adequate cost budget, and management personnel.
Relative Positioning Method for UAVs Based on Multi-Source Information Fusion
Authors: Song He , Hu Shaolin , Guo Qiliang , Jiang Wenqiang
Published: September 2022

To improve the accuracy and reliability of the relative positioning for unmanned aerial vehicles (UAVs), a relative positioning method based on multi-source information fusion is proposed. An integrated positioning scheme is constructed by the Beidou Navigation Satellite System (BDS) receivers, Global Positioning System (GPS) receivers, Vision-Based Navigation System (VisNav), and Inertial Navigation System (INS). The BDS pseudorange relative difference equation, the GPS relative difference equation, the relative line-of-sight vector equation, and the INS measurement equation are established, respectively. The least squared (LS) method is used to realize information fusion, and the Gauss-Newton method is used to iteratively solve the relative position results. Finally, numerical simulation and result analysis are conducted with different sensor configurations. Simulation results show that BDS/INS/GPS/VisNav relative positioning result is obviously better than that of INS/GPS, INS/VisNav, and INS/GPS/VisNav, and the proposed method reduces the relative positioning errors and has a higher accuracy and excellent robustness. The research result is suitable for application scenarios with high navigation accuracy requirement such as AAR and intelligent swarm formation control.
[Retracted] Evaluation Method of Public Physical Training Quality Based on Global Topology Optimization Deep Learning Model
Authors: Yan JingMing
Published: September 2022

In the quality evaluation of public sports training, the selected indicators are not comprehensive, resulting in some errors in the results of quality evaluation. Therefore, this paper designs a public sports training quality evaluation method based on the deep learning model of global topology optimization. Determine the basic principles of public sports training quality evaluation, determine the human coordinate points of public sports training by determining the basic section and basic axis of human training, and extract the data of public sports training quality evaluation. On this basis, quantify the public sports training quality evaluation index, construct the evaluation matrix, calculate the weight of the evaluation index, and determine the importance of the public sports training quality evaluation index. Preprocess the public sports training quality evaluation index set, search the optimal fitness value in the global topology optimization depth learning model, introduce the global topology optimization depth learning model, input the evaluation index and output the evaluation quality results, and realize the quality evaluation of public sports training. The experimental results show that the evaluation method in this paper can improve the accuracy of the evaluation results and is feasible.
Development and Validation of a Nomogram Model for Predicting the Risk of Readmission in Patients with Heart Failure with Reduced Ejection Fraction within 1 Year
Authors: Hu Yue , Wang Xiaotong , Xiao Shengjue , Huan Chunyan , Wu Huimin , Xu Tao , Guo Minjia , Zhu Hong , Pan Defeng
Published: September 2022

The high incidence of readmission for patients with reduced ejection fraction heart failure (HFrEF) can seriously affect the prognosis. In this study, we aimed to build a simple predictive model to predict the risk of heart failure (HF) readmission in patients with HFrEF within one year of discharge from the hospital. This retrospective study enrolled patients with HFrEF evaluated in the Heart Failure Center of the Affiliated Hospital of Xuzhou Medical University from January 2018 to December 2020. The patients were allocated into the readmission or nonreadmission group, according to whether HF readmission occurred within 1 year of hospital discharge. Subsequently, all patients were randomly divided into training and validation sets in a 7 : 3 ratio. A nomogram was established according to the results of univariate and multivariate logistic regression analysis. Finally, the area under the receiver operating characteristic curve (AUC-ROC), calibration plot, and decision curve analysis (DCA) were used to validate the nomogram. Independent risk factors for HF readmission of patients with HFrEF within 1 year of hospital discharge were as follows: age, body mass index, systolic blood pressure, diabetes mellitus, left ventricular ejection fraction, and angiotensin receptor-neprilysin inhibitors. The AUC-ROC of the training and validation sets were 0.833 (95% confidence interval (CI): 0.793-0.866) and 0.794 (95% CI: 0.727-0.852), respectively, which have an excellent distinguishing ability. The predicted and observed values of the calibration curve also showed good consistency. DCA also confirmed that the nomogram had good clinical value. In conclusion, we constructed an accurate and straightforward nomogram model for predicting the 1-year HF readmission risk in patients with HFrEF. This nomogram can guide early clinical intervention and improve patient prognosis.
Characterization of the Emerging Enteropathogen Escherichia Albertii Isolated from Urine Samples of Patients Attending Sapporo Area Hospitals, Japan
Authors: Fujioka Miyuki , Yoshioka Sho , Ito Masahiko , Ahsan Chowdhury Rafiqul
Published: September 2022

Recently, Escherichia albertii has been identified as a causative agent of diarrhea in humans and is often misidentified as diarrheagenic Escherichia coli (DEC), a lactose-nondegrading bacterium. In this study, we performed biochemical characterization, gene possession status, drug susceptibility testing, and sequencing analysis of the strains detected in urine samples. One urea-degrading strain was detected in terms of biochemical characteristics, but was found to be nonurea-degrading by another method, leading to conflicting results. All target strains possessed the E. albertii-specific gene, the DEC common gene eae, and the E. coli 16S rRNA gene. In the drug susceptibility test, all urine-derived strains were sensitive to tetracycline (TC), whereas the JCM 17328 strain was resistant to TC, suggesting that TC is effective against urine-derived E. albertii strains. In 16S rRNA sequencing analysis, the E. albertii strains were ranked at the top of homology, but not in the top one, making it difficult to differentiate them from other strains. In summary, if a suspected lactose-nondegrading E. coli strain was isolated from a urine sample, it could be differentiated from E. albertii by the presence of E. albertii-specific genes.
Recognition Method for Face Local Features Based on Fuzzy Algorithm and Intelligent Data Analysis
Authors: Li Xianwei
Published: September 2022

Traditional recognition methods for face local features have a low recognition rate, so the recognition method for face local features based on the fuzzy algorithm and intelligent data analysis was designed. Firstly, the wavelet denoising method was used to reduce the noise of face images, and adaptive template matching was performed on the obtained images. Then, the face information was encoded, and the face features were identified to locate the face features. On this basis, the principal components of the face were analyzed to obtain the global features of the face. Finally, through the candidate set of facial local feature recognition, the extraction of face local features, and the fusion of face local features, the recognition of local face features were realized. The experimental results show that the average recognition rate of this method is 88.84% in a noise environment and 97.3% in noise‐free environment. It can accurately recognize the face local features, can meet the needs of recognition of the face local features, and has certain practical application significance.
Psychological Adjustment and Emotional Health Care Strategies in the Teaching Process of College Music Teachers Based on Big Data Analysis
Authors: Ma Songqing , Liu Yuhe
Published: September 2022

In music teaching in high schools, teachers should not only impart corresponding music knowledge to students but also focus on cultivating students’ comprehensive quality and quality. In the process of music teaching in universities, we should focus on students’ psychological adjustment and emotional health care. In order to quantify the application effect of big data technology in music teaching reform, this paper takes piano teaching in universities as an example to conduct research and mainly analyzes the physiological and psychological prediction effects involved in the teaching process. This paper introduces three typical big data technologies to predict and analyze the data involved in the existing piano teaching in universities. The analysis results show that the prediction effect based on the fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by big data technology shows good consistency and continuity, which indicates that the prediction method based on big data technology is suitable for psychological adjustment and emotional health care in music teaching in universities.
Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model
Authors: Alharbe Nawaf R. , Munshi Raafat M. , Khayyat Manal M. , Khayyat Mashael M. , Abdalaha Hamza Saadia Hassan , Aljohani Abeer A.
Published: September 2022

Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate. Automated recognition of EC can aid physicians in improving diagnostic performance and accuracy. However, the classification of EC is challenging due to identical endoscopic features, like mucosal erosion, hyperemia, and roughness. The recent developments of deep learning (DL) and computer-aided diagnosis (CAD) models have been useful for designing accurate EC classification models. In this aspect, this study develops an atom search optimization with a deep transfer learning-driven EC classification (ASODTL-ECC) model. The presented ASODTL-ECC model mainly examines the medical images for the existence of EC in a timely and accurate manner. To do so, the presented ASODTL-ECC model employs Gaussian filtering (GF) as a preprocessing stage to enhance image quality. In addition, the deep convolution neural network- (DCNN-) based residual network (ResNet) model is applied as a feature extraction approach. Besides, ASO with an extreme learning machine (ELM) model is utilized for identifying the presence of EC, showing the novelty of the work. The performance of the ASODTL-ECC model is assessed and compared with existing models under several medical images. The experimental results pointed out the improved performance of the ASODTL-ECC model over recent approaches.
Application of Cloud Computing Technology in Computer Secure Storage
Authors: Cao Fenglian , Zhang Lihong , Naik Darshana A. , Gonzáles José Luis Arias , Verma Neha , Jain Amit , Jain Rituraj , Sharma Ashutosh
Published: September 2022

To investigate the use of cloud computing technologies in safe computer storage, firstly, it is proposed to complete the central control function by building a cloud computing data center, collect multiple platforms and network safety technologies, and then connect computers in unlike sites to confirm computer information security. Then, based on the implementation advantages of cloud computing technique in computer network safe storage, specific applications are analyzed. Finally, a cloud computing secure modeling and analysis idea based on multiqueue and multiserver is proposed. The proposed cloud security approach ensures that both data and applications are easily accessible to authorized users. One always has a consistent way to access your cloud data and applications, allowing you to address any potential security issues as soon as they arise. It has greatly improved computer security storage convenience while also greatly improving computer network storage security. After verification, with the cloud computing technology platform to carry out relevant businesses at any time, the operation effectiveness has been meaningfully enhanced by 80%. At the same time, it promotes the construction of information sharing and gives full performance to the benefits of hardware, accelerates the process of resource integration, and provides information support for the formulation of enterprise strategic plans. Combined with the actual situation, the current study discusses the development and application direction of cloud computing, so as to add new impetus to the economic growth of enterprises.
A Novel Path Planning and Node Selection Method Using Reinforcement Learning in NTN IoT Networks
Authors: Yang Siming , Shan Zheng , Cao Jiang , Gao Yuan , Guo Yang , Wang Ping , Wang Jing , Wang Xiaonan
Published: September 2022

With the rapid deployment of 5G networks in recent years, the characteristics of high bandwidth, low latency, and low energy consumption of 5G networks have enabled the rapid development of IoT (Internet of things) technology. However, 5G networks cannot provide high-quality wireless coverage for many IoT devices in border areas and hotspots with a high signal density that lack fixed infrastructure. Therefore, this paper uses the UAV (unmanned aerial vehicle) to carry the communication platform to build the NTN (nonterrestrial network) to provide wireless coverage for terrestrial fixed and mobile IoT devices. Meanwhile, since the NTN needs to provide wireless coverage for many IoT devices, we use deep reinforcement learning to provide path planning for the UAV communication platform to improve the efficiency of wireless coverage. We build a simulation environment to evaluate the performance of the NTN network for wireless coverage of IoT devices in urban hotspot areas. Experimental results show that the method proposed in this paper can provide higher downlink rates for more IoT devices than NB-IoT (narrowband Internet of things).
Long Term Oral Administration of Oregano Essence Effectively Relieves Polycystic Ovarian Rats through Endocrine and Inflammatory Balance
Authors: Ghorbani Ranjbary Ali , Mehrzad Jalil , Talebkhan Garoussi Massoud , Zohdi Javad
Published: September 2022

Polycystic ovarian syndrome (PCOS) is alarmingly rising and sustainable therapy/prevention is needed. Here, we evaluated the therapeutic effects of oregano or Origanum vulgare (O. vulgare) essence (OE) on the PCOS rat model system. Vaginal smears monitored the estrous cycle of 40 virgin adult rats, and they received 2 mg estradiol valerate (EV)/0.2 ml corn oil intramuscularly to induce PCOS. At 60 days post-EV injection, all rats were evaluated for follicular development/cysts. The EV-induced PCOS rats were orally administered 250 and 500 mg/kgBW/day of OE for 30 days. OE was also further assessed for its predominant components along with hormonal, histological, and inflammatory-related gene expressions in the ovaries. The main components of the OE were predominantly pulegone (36.3), L-menthone (31.3%), far less piperitone (7.8%), isopiperitone (6.4%), isomenthol (3.6%), humulene epoxide II (2.2%), α-pinene (1.7%), and thymol (1.5%). Hormonal, histological, and inflammatory-related gene expression results showed >4-fold and 1.5-fold increase in FSH and progesterone; ∼50%, 85%, 45%, 55%, and 30% decreased in LH, estradiol, estrogen, testosterone, and AMH; and dose-dependently decreased in mRNA expression of IL-6, IL-1α, NF-kB, TNF-α, and IL-1β by 25–65%, 55–75%, 15–40%, 30–55%, and 35–55%, respectively, and thus decreased the severity of PCOS, boosted endocrine balance, restored functional follicles and corpus luteum, and thus ovulation in PCOS rats. Overall, in the disrupted PCOS rats, OE oral treatment effectively relieved estradiol-induced PCOS rats via: (1) its endocrine balancing on GnRH, FSH, and LH and (2) its anti-inflammatory and antioxidant properties on ovary caused by OE’s useful compounds like pulegone, thymol, and L-menthone. Though many aspects of the effects remain to be tested, such an underlying mechanistic reproductive regulatory effect observed in OE-administered rats further proves its sensible pharmaceutical applications in reproductive medicine and more specifically, PCOS.
Machine Learning-Based Intelligent Image Color Processing for Color Teaching Analysis of Chinese Painting
Authors: Qiao Haiming , Feng Yanshun
Published: September 2022

Color itself is a beautiful and wonderful existence. Chinese traditional colors contain Chinese aesthetic taste and ancient cultural precipitation. It is said that color is a way to understand the world, while traditional Chinese color is integrated into life and caring for the soul [15]. With the continuous development of science and technology, computers are widely used in various fields, and intelligent image color processing technology is an independent theoretical and technical field, but it is also an extremely important technical support for machine perspective graphics processing. In this paper, combined with intelligent image color processing technology, the color teaching of Chinese painting is studied, and based on the wavelet variant, the best blur system parameters are used to obtain high-quality images using BFPSO, PSO, and BFO learning mechanisms to form suitable coding. Through the experiment, the color of Chinese painting is tested and verified by intelligent image color processing technology, and through the experimental results, it can be seen that the accuracy rate and recall rate after intelligent image processing technology are close to 1, indicating that, after optimizing with BFPSO algorithm, the optimal solution is given priority to a certain extent. Therefore, the use of intelligent image color processing technology has further improved the color teaching of Chinese painting.
Transnasal Endoscopic Orbital Decompression Combined with an Enhanced Recovery after Surgery Protocol in Graves Ophthalmopathy
Authors: Xu Hui-Qing , Wang Qian , Hu Li-Li , Li Hong , Peng Feng
Published: September 2022

Objective. Although enhanced recovery after surgery (ERAS) was shown to improve patients’ recovery after surgery and transnasal endoscopic orbital decompression has been associated with lesser risks of postoperative complications compared to other surgical techniques in treating Graves ophthalmopathy (GO), there are currently no clinical studies on the application of ERAS in transnasal endoscopic orbital decompression. This study aimed to investigate the potential effects of combining transnasal endoscopic orbital decompression with ERAS in the treatment of GO. Methods. A retrospective analysis was performed for 5 GO patients (10 eyes) treated with transnasal endoscopic orbital decompression from January 2021 to December 2021 at the Third Affiliated Hospital of Sun Yat-Sen University. All patients underwent ERAS, and the effects of ERAS on the postoperative complications and recovery of patients were evaluated. Results. Ophthalmological examination showed that GO patients had good correction of exophthalmos after surgery combined with ERAS. Specifically, the exophthalmos reduction in subjects was 0.9–2.1 mm, with a mean reduction of 1.23 mm. In addition, a visual acuity improvement of 0.15–0.4, with an average improvement of 0.23, was also observed. Further, the Scale of Quality of Life for Diseases with Visual Impairment (SQOL-DVI) showed that, compared with before surgery, the patients’ QOL was significantly improved 2 weeks after surgery. Before surgery, there were 2 patients with diplopia and blurred vision, and after postoperative adaptive exercise, the symptoms of these 2 patients disappeared after 6 months of follow-up. As for the other 3 patients, they had no diplopia or blurred vision after surgery. Conclusion. This observational study found that transnasal endoscopic orbital decompression might be effective in treating GO, and ERAS might be considered an important adjunct to improving perioperative care and postoperative recovery.
[Retracted] circZC3HAV1 Regulates TBC1D9 to Affect the Biological Behavior of Colorectal Cancer Cells
Authors: Zhang Jianxian , Xue Yan , Gao Hengling , Yu Yunxi , Cheng Huabin , Lv Xukun , Ke Ke
Published: September 2022

Background. Colorectal cancer (CRC) is one of the most frequently diagnosed cancers all over the world, which accounts for a large proportion of cancer‐associated deaths. The regulatory function of circular RNAs (circRNAs) has been affirmed in diverse cancers. circ_0082628, named circRNA zinc finger CCCH‐type containing antiviral 1 (circZC3HAV1), has been discovered to be significantly downregulated in CRC tissues. Nevertheless, the function and mechanism of circZC3HAV1 in CRC remain unclear. Purpose. We targeted at studying the specific role and mechanism of circZC3HAV1 in CRC cells. Methods. The expression of the genes was detected by quantitative real‐time polymerase chain reaction (qPCR). The binding relationship among different genes was verified by mechanism assays. Functional assays were carried out to reveal the role of different RNAs in CRC cell malignant behaviors. Results. circZC3HAV1 was significantly downregulated in CRC cells. circZC3HAV1 overexpression hampered CRC cell migratory and invasive abilities. As for the mechanism, circZC3HAV1 competitively bound with microRNA‐146b‐3p (miR‐146b‐3p) to enhance the expression of TBC1 domain family member 9 (TBC1D9). Rescue assays demonstrated circZC3HAV1 sponged miR‐146b‐3p and upregulated TBC1D9 to restrict migration and invasion of CRC cells. Conclusion. circZC3HAV1 could upregulate TBC1D9 via absorbing miR‐146b‐3p, consequently inhibiting migratory and invasive capabilities of CRC cells.
Dynamic Analysis of a VSEIR Model with Vaccination Efficacy and Immune Decline
Authors: Han Xiaotao , Liu Hua , Lin Xiaofen , Wei Yumei , Ming Ma
Published: September 2022

Vaccination is an effective way to prevent the spread of infectious diseases. In this study, we formulate a VSEIR mathematical model to explore the effects of vaccination rate, vaccine efficacy, and immune decline on the COVID-19 transmission. The existence and stability criteria of equilibrium states were determined by analyzing the model. Model analysis was performed. One of the interesting phenomena involved in this issue is that diseases may or may not die out when the basic reproduction number falls below unity (i.e., a backward bifurcation may exist and cause multistability). The disease eventually becomes endemic in the population when the basic reproduction number exceeds one. By comparing different vaccination rates, vaccine efficacy, and infection rate factors, the diseases can be eliminated, not only by vaccines but also by strict protective measures. In addition, we used the COVID-19 number of reported cases in Xiamen in September 2021 to fit the model, and the model and the reported data were well matched.
Mechanical Evaluation on Carbon/Basalt Fiber-Reinforced Hybrid Polymer Matrix Composite
Authors: Alshgari Razan A. , Prasad M. Sarat Chandra , Srivastava Bipin Kumar , Al Ansari Mohammed Saleh , Gupta Parul , Sivakumar A. , Wabaidur Saikh Mohammad , Islam M. Ataul , Diriba Abdi
Published: September 2022

This work is about making hybrid composite materials out of carbon fiber mats and basalt fiber mats that are 40% reinforced with a 60% epoxy polymer matrix. Traditional hand layup has been used for the fabrication process to make five laminates of these two fibers. The mechanical properties of the hybrid composite were evaluated by measuring its tensile strength, flexural strength, impact energy, and hardness. The results showed that adding more carbon fiber layers to the composite made a big difference in its mechanical properties. In sample A, the tensile strength is 280 MPa, the flexural strength is 247 MPa, and the basalt fiber can keep more impact energy of 24 J in sample E, along with the carbon fiber and epoxy matrix. A scanning electron microscope was used to figure out how carbon/basalt fiber composite laminates break down.
Tetrahydrocurcumin Chemosensitizes Breast Cancer to Albumin-Bound Paclitaxel by Enhancing SPARC Expression through Demethylation
Authors: Qiu Dongjie , Li Xiaolong , Liu Yinfeng , Bao Xiaohuan , Ma Li
Published: September 2022

Paclitaxel is an effective chemotherapy drug for breast cancer (BC); however, drug resistance affects long-term clinical applications. In this study, we aimed to explore whether a natural compound, tetrahydrocurcumin (THC), could sensitize BC to albumin-bound paclitaxel (ab-PTX). The in vitro sensitization effect of THC to ab-PTX was evaluated in human BC cell lines, and in vivo chemosensitivity was measured using a xenograft BC tumor model. The expression of secreted protein acidic and rich in cysteine (SPARC), a speculated protein interacting with ab-PTX, was measured. Methylation-specific polymerase chain reaction (MSP) was used to further explore whether demethylation of SPARC by THC contributed to its chemosensitivity capabilities. Higher SPARC expression was correlated with a better prognosis in patients with BC. In vitro analysis showed THC enhanced the inhibitory effect of ab-PTX on BC cells and xenograft tumors and showed significant chemosensitivity. This enhancement mainly relied on upregulating the expression of SPARC through downregulating methylation of the SPARC gene. The demethylating agent, 5-Aza-2

′

-deoxycytidine (5-Aza-Cdr), decreased THC’s chemosensitivity effect, further confirming this molecular mechanism. THC enhanced the inhibitory effect of ab-PTX in BC by downregulating methylation of the SPARC gene. Further, upregulated SPARC increased the efficacy of ab-PTX.
The Dilemma and Countermeasures of Music Education under the Background of Big Data
Authors: Han Jiaye
Published: September 2022

The fundamental subject matter in the area of quality research in education is music education, which is significant. Based on a big data theory, this study proposes a data management platform of music education that can use data analysis techniques to perform analysis and research on the situation facing music education. According to the layered concept through a demand analysis of the music education big data analysis platform, the model divides the platform into a data support layer, data storages and calculation layer, platform function layers, and platform application layer. Based on a platform function layer, the technical route is formulated, and functional modules are designed to solve the issue of quantitative data analysis. The plan uses the data aggregating component to combine music data into a real-time data stream for 3D visualization simulation and then distributes a data stream using messaging middleware. Through a real-time computing framework, the downstream completes a real-time calculation of musical information. Weak and strong real-time needs for music apps can be processed using features. The greatest average relative inaccuracy of the model, as determined by testing a simulation verification module, is 5.1374 percent, and getting the specifics of the visualization work using getViewTaskInfo is a trustworthy technique. It successfully encourages the capacity to overcome the challenges of music education in order to achieve the accuracy standards of high-quality music education.
Temporal and Spatial Cluster Analysis of 2019 Novel Coronavirus Pneumonia in Chongqing, 2020.1∼2020.2
Authors: Dai Xiaoying , Sun Rui
Published: September 2022

In order to explore the spatial and temporal distribution characteristics of COVID-19 in Chongqing from January 22 to February 25, 2010, and provide a series of suggestions for scientific prevention and control of epidemic situation, we will mainly analyze the epidemic situation data of Chongqing Municipal Health Committee members and improve the descriptive analysis. Regional distribution and spatiotemporal scans were analyzed for COVID-19 outbreaks using ArcGIS10.2 and SaTScan9. 5 software. After the analysis, a total of 576 novel coronavirus pneumonia patients were confirmed in Chongqing. The incidence trend increased rapidly from January 22 to January 31, then decreased gradually, and there were no new cases until February 25. The purely spatial scanning results were consistent with spatiotemporal scanning, and a first-level accumulation area was detected by spatiotemporal scanning in the east and northeast of Chongqing from January 22 to February 10. From January 22 to February 25, 2020,COVID-19 occurred in the eastern and northeast regions of Chongqing. It is recommended to strengthen the detection of cluster areas to prevent another outbreak of COVID-19 risk.
Application of Reinforcement Learning in Multiagent Intelligent Decision-Making
Authors: Han Xiaoyu
Published: September 2022

The combination of deep neural networks and reinforcement learning had received more and more attention in recent years, and the attention of reinforcement learning of single agent was slowly getting transferred to multiagent. Regret minimization was a new concept in the theory of gaming. In some game issues that Nash equilibrium was not the optimal solution, the regret minimization had better performance. Herein, we introduce the regret minimization into multiagent reinforcement learning and propose a multiagent regret minimum algorithm. This chapter first introduces the Nash Q-learning algorithm and uses the overall framework of Nash Q-learning to minimize regrets into the multiagent reinforcement learning and then verify the effectiveness of the algorithm in the experiment.
Analysis of Online Education Reviews of Universities Using NLP Techniques and Statistical Methods
Authors: Kang Hong , Zhang Jiangang , Kang Jian
Published: September 2022

In the long-running battle against COVID-19, it is worth further discussing how well online education is accepted by users as a new type of education method which is popularized suddenly. The launch of a large-scale online teaching during the COVID-19 pandemic gave us the opportunity to investigate the opinions and emotions of online teaching users. This paper employs NLP techniques and statistical methods to process 93754 comments collected from the three online teaching platforms designated by the Ministry of Education for universities. This study aims to gain a better understanding of the changing characteristics of students’ opinions and emotions regarding online teaching, as well as to learn what students required and how the platforms functioned during the epidemic period. The findings revealed that (1) users of online teaching platforms were mostly concerned about whether the teaching resources provided by the platforms were rich and of high quality all along. The main influencing factors affecting online teaching experiences during the implementation of large-scale online teaching are hardware device deficiencies such as server congestion and network lag. (2) The majority of users expressed positive attitude towards online education which played a positive role during the outbreak. (3) The epidemic had a significant impact on the emotions of online teaching users. In short, the research themes showed that online teaching users are always concerned with the richness and quality of the platform’s resources. Furthermore, the most important issues for users during the period of large-scale online teaching are whether the network infrastructure is efficient and smooth, as well as the platform’s function perfection. The popularity of online teaching helped to solidify the online teaching space and improve its social image. This study provides a point of reference for the development and improvement of online education and compensates for a lack of relevant studies.