bluuebunny/crossref_metadata_2025 huggingface dataset:
Antibody responses and risk factors associated with impaired immunological outcomes following two doses of BNT162b2 COVID-19 vaccination in patients with chronic pulmonary diseases
Authors: Harboe Zitta Barrella , Hamm Sebastian Rask , Pérez-Alós Laura , Sivapalan Pradeesh , Priemé Helene , Wilcke Torgny , Kjeldgaard Peter , Shaker Saher , Svorre Jordan Alexander , Møller Dina Leth , Heftdal Line Dam , Madsen Johannes Roth , Bayarri-Olmos Rafael , Hansen Cecilie Bo , Pries-Heje Mia Marie , Hasselbalch Rasmus Bo , Fogh Kamille , Armenteros Jose Juan Almagro , Hilsted Linda , Sørensen Erik , Lindegaard Birgitte , Browatzki Andrea , Biering-Sørensen Tor , Frikke-Schmidt Ruth , Ostrowski Sisse Rye , Iversen Kasper Karmark , Bundgaard Henning , Nielsen Susanne Dam , Garred Peter , Jensen Jens-Ulrik Stæhr
Published: July 2022

Introduction

Responses to COVID-19 vaccination in patients with chronic pulmonary diseases are poorly characterised. We aimed to describe humoral responses following two doses of BNT162b2 mRNA COVID-19 vaccine and identify risk factors for impaired responses.

Methods

Prospective cohort study including adults with chronic pulmonary diseases and healthcare personnel as controls (1:1). Blood was sampled at inclusion, 3 weeks, 2 and 6 months after first vaccination. We reported antibody concentrations as geometric means with 95% CI of receptor binding domain (RBD)-IgG and neutralising antibody index of inhibition of ACE-2/RBD interaction (%). A low responder was defined as neutralising index in the lowest quartile (primary outcome) or RBD-IgG <225 AU/mL plus neutralising index <25% (secondary outcome), measured at 2 months. We tested associations using Poisson regression.

Results

We included 593 patients and 593 controls, 75% of all had neutralising index ≥97% at 2 months. For the primary outcome, 34.7% of patients (n=157/453) and 12.9% of controls (n=46/359) were low responders (p<0.0001). For the secondary outcome, 8.6% of patients (n=39/453) and 1.4% of controls (n=5/359) were low responders (p<0.001). Risk factors associated with low responder included increasing age (per decade, adjusted risk ratio (aRR) 1.17, 95% CI 1.03 to 1.32), Charlson Comorbidity Index (per point) (aRR 1.15, 95% CI 1.05 to 1.26), use of prednisolone (aRR 2.08, 95% CI 1.55 to 2.77) and other immunosuppressives (aRR 2.21, 95% CI 1.65 to 2.97).

Discussion

Patients with chronic pulmonary diseases established functional humoral responses to vaccination, however lower than controls. Age, comorbidities and immunosuppression were associated with poor immunological responses.
Local one-dimensional reggeon model of the interaction of several pomerons
Authors: Braun M. A. , Kuzminskii E. M. , Vyazovsky M. I.
Published: July 2022

We consider the one-dimensional local reggeon theory describing the leading pomeron with the conformal spin $$l=0$$

l
=
0

and two subdominant pomerons with $$l=\pm 2$$

l
=
±
2

. The dependence of the propagators of pomerons and the hA amplitude on rapidity are found numerically by integrating the evolution equation.
Entropy of the quantum fluctuations of fermionic instantons in the Universe
Authors: Akıllı Mahmut , Yılmaz Nazmi
Published: July 2022

The aim of this paper is to study the entropy of quantum fluctuations of fermionic instantons. For this purpose, we focus on the spinor-type instanton solution family of the massless pure fermionic Thirring model because it is a well-known quantized toy model in the quantum field theory since 1958. We calculate the Boltzmann–Gibbs–Shannon (BGS) entropy of the Thirring model by the normalized inner scalogram (NIS) and find that the entropy of quantum fluctuations of Thirring fermionic instantons increase slightly just after the big bang. This result leads us to discuss the relation between the increasing entropy in the Universe due to quantum fluctuations of the fermionic Thirring instantons during the creation of fermionic type particles.
DYNAMIC LINKAGES AND INTEGRATION AMONG FIVE EMERGING BRICS MARKETS: PRE- AND POST-BRICS PERIOD ANALYSIS
Authors: SINGH AMIT KUMAR , SHRIVASTAV ROHIT KUMAR , MOHAPATRA AMIYA KUMAR
Published: July 2022

This study aims to explore dynamic linkages and integration among emerging markets of BRICS, especially comparing the pre- and post-BRICS formation period behaviors and further comment upon the portfolio diversification opportunities available for global investors. Weekly closing indices of BRICS stock markets for the period 2000–2020 have been taken. Considering BRICS formation year, total period is divided into two sub-periods, pre- and post-BRICS periods. Short-run relationship has been measured through Granger causality, VAR, IRF and VDC. For long-run co-movement, Johansen co-integration is applied. To explain asymmetrical response of the market, E-GARCH Model is applied. Both Granger causality and VAR model confirm presence of short-run inter-linkages among BRICS during post-BRICS period. Johansen co-integration test also establishes more co-integrating equations during post-BRICS. E-GARCH result indicates a strong presence of asymmetry effect in the volatility of BRICS stock returns during post-BRICS and concludes the presence of leverage effect in all the BRICS markets. By integrating the findings with relevant literature, authors propose a framework that establishes BRICS formation, trade agreements and collaboration with each other has resulted into a strong relationship among BRICS nations during post-BRICS period and hints a little opportunity to global investors for portfolio diversification in short-run but no opportunity in the long-run.
Unsupervised Identification of Abnormal Nodes and Edges in Graphs
Authors: Senaratne Asara , Christen Peter , Williams Graham , Omran Pouya G.
Published: July 2022

Much of today’s data are represented as graphs, ranging from social networks to bibliographic citations. Nodes in such graphs correspond to records that generally represent entities, while edges represent relationships between these entities. Both nodes and edges in a graph can have attributes that characterize the entities and their relationships. Relationships are either explicitly known (like friends in a social network), or they are inferred using link prediction (such as two babies are siblings because they have the same mother). Any graph representing real-world data likely contains nodes and edges that are abnormal, and identifying these can be important for outlier detection in applications ranging from crime and fraud detection to viral marketing. We propose a novel approach to the unsupervised detection of abnormal nodes and edges in graphs. We first characterize nodes and edges using a set of features, and then employ a one-class classifier to identify abnormal nodes and edges. We extract patterns of features from these abnormal nodes and edges, and apply clustering to identify groups of patterns with similar characteristics. We finally visualize these abnormal patterns to show co-occurrences of features and relationships between those features that mostly influence the abnormality of nodes and edges. We evaluate our approach on datasets from diverse domains, including historical birth certificates, COVID patient records, e-mails, books, and movies. This evaluation demonstrates that our approach is well suited to identify both abnormal nodes and edges in graphs in an unsupervised way, and it can outperform several baseline anomaly detection techniques.
The dynamic shaping of local cortical circuitry by sex and age, and its relation to pattern comparison processing speed
Authors: Christova Peka , James Lisa M. , Georgopoulos Apostolos P.
Published: July 2022

Sex and age significantly affect shaping of local cortical interactions that are more limited in women and older brains. The net result is an increase in local spatial steepness of interactions, leading to a reduction of overlap among local ensembles and, hence, a more efficient information processing and an increase in the number of independent local cortical “processors.” Remarkably, cognitive processing speed was positively associated with local spatial steepness, in keeping with the reasoning earlier.
Effect of Oxytocin Combined with Different Volume of Water Sac in High-Risk Term Pregnancies
Authors: Mi Hanna , Sun Na
Published: July 2022

Objective. The study estimated the impacts of water sac of different capacities combined with oxytocin (OXT) on pregnant women with high-risk term pregnancies. Methods. Women with high-risk term pregnancies who received OXT were enrolled to perform labor induction using 30  mL (group A), 80 mL (group B), and 150  mL (group C), followed by the comparisons regarding to the success rate of labor induction, cesarean section rate, duration of induced labor to labor, duration of the first stage of labor, postpartum blood loss, the incidence of adverse reactions, and the assessment of cervical ripening using Bishop Score. Besides, neonatal weight, Apgar score, as well as psychological status, and satisfaction of patients were compared among these groups. Results. As compared with group A, the success rate of induced labor was higher in groups B and C with lower cesarean section rate and shorter duration of induced labor to labor, but the duration of the first stage of labor in group B was the shortest among the three groups. The amount of postpartum hemorrhage decreased stepwise from groups A to B to C. In addition, groups A and B showed a reduced incidence of adverse reactions than group C, but the highest level of cervical ripening and highest patient satisfaction was revealed in group C and group B, respectively. Furthermore, the highest patient satisfaction was found in group B. Conclusion. The usage of an 80  mL water sac combined with OXT  in high-risk term pregnancy has ideal induction effects, which can guarantee maternal cervical maturity and shorten the time of the first stage of labor.
Digital Forensics as Advanced Ransomware Pre-Attack Detection Algorithm for Endpoint Data Protection
Authors: Du Jian , Raza Sajid Hussain , Ahmad Mudassar , Alam Iqbal , Dar Saadat Hanif , Habib Muhammad Asif
Published: July 2022

Ransomware is a malicious software that takes files hostage and demands ransomware to release them. It targets individuals, corporations, organizations, and public services such as hospitals and police stations. It is a growing industry that affected more than three million users from 2019 to 2020. The ransom payments totaled 25 billion-plus dollars in the year 2019. The latest version of ransomware was developed using undetectable and nonanalysis techniques. This paper represents an intelligent KNN and density-based machine learning algorithm to detect ransomware pre-attacks on an endpoint system. The data preprocessing and feature engineering techniques are augmented with the KNN algorithm for finding the solution. This helps the anti-malware developer, vendors, endpoint security provider companies, or researchers work on malware detection using advanced machine learning algorithms to develop the more effective ransomware defensive solutions to detect and prevent ransomware pre-attack execution. The proposed KNN and density-based algorithm will predict ransomware detection with higher accuracy than other machine learning algorithms. The anti-malware and anti-ransomware solution provider companies can use this algorithm to improve their existing ransomware detection solutions for endpoint users.
A Recognition Method of Crop Diseases and Insect Pests Based on Transfer Learning and Convolution Neural Network
Authors: Liu Dongmei , Yang Hangxu , Gong Yongjian , Chen Qing
Published: July 2022

The research on crop pest identification technology based on image analysis has important practical significance for effectively controlling the occurrence of crop diseases and insect pests (CDIP), improving crop yield and reducing the pollution of pesticides to the environment. Aiming at the problems existing in crop pest recognition technology and aiming at improving the accuracy and efficiency of crop pest recognition, this study proposes a crop pest image recognition method based on transfer learning and convolution neural network. Firstly, the crop pest image is geometrically operated to expand the crop pest image data set, and then the expanded data set is divided into training set and test set. Then, Alexnet, VGG-16, and ResNet-50 models are pretrained on ImageNet large image data set. Based on the theory of transfer learning, the learned model parameters are transferred to the small sample image data set of crop diseases and insect pests in this paper, so as to train the crop diseases and insect pests recognition model. This method is tested on Image Database for Agricultural Diseases and Pests (IDADP). The experimental results show that the convolution neural network plant leaf image recognition method based on transfer learning has better effect. It can quickly and accurately diagnose crop diseases, reduce the use of pesticides and fertilizers, and improve the yield and quality of crops.
Anomaly Intrusion Detection of Wireless Communication Network-Based on Markov Chain Model
Authors: Zhang Huifang , Lan Wangsen , Zhang Desheng
Published: July 2022

In order to solve the increasingly serious security problems of wireless networks, research on abnormal intrusion detection methods of wireless communication networks based on Markov chain model is proposed. What is usually observed is not the known intrusion behavior but the abnormal phenomenon in the communication process studied, which is completed by detecting the change of system behavior or usage. In this paper, the Markov chain model is used to detect the abnormal intrusion of wireless communication networks. Through the analysis and selection of parameters, the experimental results are ideal, and a variety of judgment methods are compared and analyzed. First, this method can easily distinguish between normal and abnormal data, which reduces the time by about 50% compared with the previous method; Second, the detection result of analysis method 2 is better than that of analysis method 1, and the accuracy is about 20%. The new method proposed in this paper has the characteristics of simple calculation, low algorithm complexity, and easy online detection. This method overcomes the disadvantage that the single-step Markov chain analysis and detection method cannot be strictly established in the nature of the Markov chain, has lower algorithm complexity than the multistep Markov chain analysis and detection method, and is simpler than the parameter calculation of hidden Markov chain model.
Evaluation and Analysis of Influencing Factors of National Traditional Sports Tourism on National Cultural Ecology Based on Big Data Discrete Differential Algorithm
Authors: Li Yunong , Liu Shaohua
Published: July 2022

The development of sports tourism is an important way of national cultural development, and its development will inevitably have a certain impact on national cultural ecology. Based on the characteristics of national cultural ecology, this paper adopts the big data discrete differential algorithm to evaluate and analyze the influencing factors of sports tourism development on national cultural ecology. The experimental results show that the discrete differential algorithm based on support vector machine can transform and optimize the solution objectives and solve them effectively. Through data analysis, it can be seen that the development of sports tourism can improve the economic development of ethnic areas, promote the development of regional facilities and environment, and increase cultural exchanges between different ethnic groups. At the same time, the development of sports tourism has strengthened the development of local national cultural behavior level and spiritual form level and made people reexamine and understand their own national culture. However, in the development of sports tourism, the material form of national cultural ecology has been impacted by the opening of the environment. If the local awareness of the protection of national cultural ecology is not improved, the material form of national culture may be irreversibly damaged.
Violin Teaching Improvement Strategy in the Context of Intelligent Internet of Things
Authors: Zhang Yifeng
Published: July 2022

With the progress of science and technology, the construction of intelligent teaching is also developing and making progress. Intelligent teaching is a kind of teaching mode with the help of emerging information technology such as the Internet of things, cloud computing, and big data to build a diversified classroom. The construction of intelligent teaching is one of the important parts in the field of education informatization, and it is also an active field. The research in this area is beneficial to China’s growth of information education. Online education has progressed significantly in the context of the intelligent Internet of Things. The focus of this paper is on violin online education. In the course of the research, it was discovered that violin teachers have little control over their students’ after-school practice results, and that parents of violin students have little time to supervise their children’s practice at any given moment. To solve this problem, the mixed speech composed of speech and violin accompaniment is selected as the research object. Audio signal analysis and neural network algorithm are used for analysis and research. The time domain and frequency domain characteristics of various audio signals are analyzed, including sparsity of speech signals and repeatability of music signals. The logarithmic power spectrum of audio signal is selected as the characteristic parameter, the sample is preprocessed and the feature is extracted, and the sound source separation model based on deep neural network is realized. The PESQ index was used to evaluate the separation results after using the audio source separation model to separate the mixed speech consisted of speech and violin accompaniment. By comparing the PESQ evaluation results of 40, 50 and 60 iterations, the model converges when the number of iterations is 50. The comparison between the model and L-MMSE algorithm shows that the performance of the model is better in speech source separation. The system will serve for the automatic evaluation of violin teachers’ homework and the remote practice supervision of teachers and parents.
Development of Rogers RT/Duroid 5880 Substrate-Based MIMO Antenna Array for Automotive Radar Applications
Authors: Subitha D. , Velmurugan S. , Lakshmi M. Vanitha , Poonkuzhali P. , Yuvaraja T. , Alemayehu Samson
Published: July 2022

In this paper, a novel 2 × 2 multiple-input multiple-output (MIMO) antenna array with four patch elements is designed. The proposed antenna is the first dual band, operating at two prominent working frequencies: 24 (24.286-25.111) GHz and 77 GHz (75.348–79.688), of automotive radars. This structure is composed of two antenna modules colocated on a single substrate, whereas each module is made up of a corporate fed planar array of two elements. This attractive feature enables us to utilize the antenna in two different ways; either both modules serve as the transmitting/receiving antenna of a monostatic radar or one module serves as a transmitter and the other one as the receiver of a bistatic radar. Most of the existing autonomous radar applications operating at 24 GHz are going to become obsolete, and all countries have plans of shifting towards the 77 GHz band. Hence, our design is very attractive as it operates with the required performance in both the bands with another added feature of the MIMO structure. The placement of antenna elements is also optimized in terms of inter- and intraelement separation of greater than λ/2 so as to ensure high diversity gain of 9.6 dBi. Moreover, the proposed antenna structure with only two antenna elements is able to achieve a high gain of around 11.8 dBi and 11.3 dBi at the dual operating modes of 24 GHz and 77 GHz, respectively. In addition to the above-mentioned benefits, this design also addresses mutual coupling reduction that is a common problem in MIMO structures by using complementary split ring resonator (CSRR) structures. State-of-the-art comparison with the recent literature shows that the proposed antenna has less number of antenna elements, an adequate gain, an excellent VSWR value, and high isolation.
[Retracted] Sevoflurane Inhibits lncRNA HOTAIR‐Modulated Stability of HK2 mRNA in a m6A‐Dependent Manner to Dampen Aerobic Glycolysis and Proliferation in Lung Cancer
Authors: Sun Xuefeng , Li Qiyong , Yang Ling
Published: July 2022

Previous studies have shown that sevoflurane has an inhibitory effect on tumor cells. So far, the effect of sevoflurane on hepatocellular carcinoma needs to be confirmed by more studies. HOX transcript antisense intergenic RNA (HOTAIR), a long noncoding RNA (lncRNA), has been shown to enhance cancer cell proliferation and medication resistance. The inherent importance and biological function of HOTAIR in the course of lung cancer (LC) is, however, poorly unclear. HOTAIR was shown to be highly elevated in LC cells in this investigation. Impairment of function trials with sevoflurane indicated that it has anticancer effects on LC cell growth, apoptosis, and aerobic glycolysis. In a mechanistic manner, HOTAIR was related to HK2 mRNA and promoted expression and constancy. Additional research revealed that HOTAIR coupled with hexokinase 2 (HK2) mRNA and favorably controlled its stabilization in a traditional‐component way. By HK2, the LC enhancement role was mediated. In summary, our data show that HOTAIR promotes the synthesis and proliferation of LC glycogen by increasing the transcription of HK2, and HOTAIR is likely to be a potential treatment for LC patients.
Efficacy and Prognosis of Ultrasound-Guided Percutaneous Catheter Drainage in Patients with Liver Abscess Complicated with Septic Shock
Authors: Li Shoulin , Luo Jiye , Wang Zheng , Lv Chunyang , Wang Yang
Published: July 2022

Objective. Currently, the therapeutic effect and outcomes of US-PCD in patients with liver abscess and septic shock remain unclear. This study is aimed at investigating the effects of ultrasound-guided percutaneous catheter drainage (US-PCD) on the prognosis of patients with liver abscess complicated with septic shock. Method. We retrieved and assessed the data of 120 patients with liver abscess complicated with septic shock diagnosed at our hospital from January 2019 to March 2021. The patients underwent US-PCD in the observation group or conventional surgical incision and drainage in the control group. After treatment, we determined the levels of liver function indicators alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), and alkaline phosphatase (ALP) as well as the levels of inflammatory cytokines IL-6, IL-8, and TNF-α in serum using ELISA on postoperative days 0, 2, 4, and 6. The postoperative body temperature recovery, peripheral white blood cell count recovery, extubation, postoperative length of stay, and complications were recorded, with a 12-month follow-up to calculate their survival rate. Results. After treatment, the ALT, AST, TBIL, DBIL, and ALP levels and inflammatory factor levels in the two groups were gradually reduced over time and returned to the normal range with a better recovery trend in the observation group. US-PCD was associated with better postoperative body temperature recovery, peripheral white blood cell count recovery, shorter extubation time, hospital stay, lower postoperative rate, and a higher survival rate. Conclusion. US-PCD may be effective in treating liver abscess and can significantly improve the prognosis of patients.
Acute Cholecystitis in a Gastric Bypass Patient Complicated by Takotsubo Cardiomyopathy
Authors: Jawaid Muhammad , El-Sherif Tarek , George Aaron , Bonatti Hugo J. R.
Published: July 2022

Background. Gallbladder disease is a common condition after gastric bypass surgery. Even after weight loss, many bariatric patients continue to suffer from comorbid conditions. Takotsubo cardiomyopathy is a rare condition that mimics acute cardiac ischemia but seems to be caused by a catecholamine storm triggered by intense stress. Case Report. A 62-year-old female presented with acute right upper quadrant (RUQ) pain to the ER. She had a history of laparoscopic gastric bypass 5 years ago and had been noncompliant for 2 years. This noncompliance included missing follow-up appointments, gaining weight which caused poorly controlled DM, and not taking her vitamin supplements. Upon presentation, her WBC was elevated, her LFTs were normal, and imaging showed acute calculous cholecystitis. She was admitted and started on antibiotics with plans for laparoscopic cholecystectomy. The next day, she developed acute chest pain, and troponins were elevated with ST changes on EKG. Echocardiography showed a ballooned left ventricle indicative for Takotsubo cardiomyopathy. Symptomatic treatment including antibiotics, betablocker, and thiamine infusion was initiated. At three-month follow-up, ejection fraction had improved from <20% to >50%. The patient underwent interval laparoscopic cholecystectomy, which was technically very challenging due to severe ongoing acute and chronic cholecystitis. There were no cardiac issues, but the patient developed an abscess in the gallbladder fossa, which was successfully treated with oral antibiotics. Conclusions. Takotsubo cardiomyopathy complicating acute cholecystitis has thus far not been reported. Our patient had a history of gastric bypass and was noncompliant with vitamin supplementation. Thiamine deficiency may have contributed to the cardiac condition (wet beriberi).
Post-Quantum Secure Identity-Based Encryption Scheme using Random Integer Lattices for IoT-enabled AI Applications
Authors: Dharminder Dharminder , Das Ashok Kumar , Saha Sourav , Bera Basudeb , Vasilakos Athanasios V.
Published: July 2022

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, where the IoT smart devices send securely the sensing data to their nearby gateway nodes(s) with the help of IBE and the gateway node(s) secure aggregate the data from the smart devices by decrypting the messages using the proposed IBE decryption. Later, the gateway nodes will securely send the aggregated data to the cloud server(s) and the Big data analytics is performed on the authenticated data using the Artificial Intelligence (AI)/Machine Learning (ML) algorithms for accurate and better predictions.
[Retracted] TNFAIP6 Promotes Gastric Carcinoma Cell Invasion via Upregulating PTX3 and Activating the Wnt/β‐Catenin Signaling Pathway
Authors: Cui Hongquan , Zhang Ling , Chen Bin , Zhang Fengchun , Xu Haiyan , Ma Guixiang , Cao Longnv , Wang Miao
Published: July 2022

Tumor metastasis is a fundamental cause of the poor prognosis of gastric carcinoma (GC). In order to study the problems affecting metastasis and recurrence of gastric cancer, the paper expose that TNF alpha induced protein 6 (TNFAIP6) is aberrantly overexpressed in GC, and patients with high‐TNFAIP6 levels exhibited inferior overall survival. Mechanistically, overexpression of TNFAIP6 raised β‐catenin ectopic nuclear distribution and activated the Wnt/β‐catenin signal pathway. The experimental results show that TNFAIP6 facilitates the aggressive potential of GC cells through modulating PTX3 expression.
Reconstruction of a Synchronous Scrambler Based on Average Check Conformity
Authors: Tan Jiyuan , Zhang Limin , Zhong Zhaogen
Published: July 2022

Channel coding technology is indispensable in digital communication systems. In noncooperative contexts, the identification of the channel codes of the synchronous scrambler is essential. In this paper, a new algorithm that directly uses a soft decision sequence for blind reconstruction of the synchronous scrambler is proposed. First, considering imbalanced signal sources and the principle of scrambling and descrambling the synchronous scrambler, the error-containing equation of the synchronous scrambler is established. Second, the average check conformity is introduced to complete the check relationship detection. Then, based on the statistical characteristics of the average check conformity, the corresponding discrimination threshold is established, and the reconstruction of the feedback polynomial of the synchronous scrambler is completed by traversing the possible primitive polynomials. Finally, the verification equation is determined by the method of subsection optimization, which greatly reduces the number of initial states that need to be traversed; this is critical for scramblers with high-order feedback polynomials (i.e., when the order of the feedback polynomial is greater than 15). Simulations show that the algorithm can effectively reconstruct the synchronous scrambler under imbalanced signal sources. Moreover, the proposed algorithm offers improved performance with about 1–2 dB gain at low signal-to-noise ratios compared with existing methods, and its computational complexity is reasonable.
Evaluation Method of Ideological and Political Classroom Teaching Quality Based on Analytic Hierarchy Process
Authors: Cheng Peng
Published: July 2022

Constructing a scientific evaluation system of curriculum education quality is an important content to improve the effectiveness of curriculum ideological and political education. Starting from the practical significance of exploring the effectiveness evaluation system of curriculum ideological and political education, supported by analytic hierarchy process, this paper constructs the evaluation model of curriculum educational quality through the five dimensions of curriculum design, teaching staff, student cognition, development evaluation, and system design, clarifies the hierarchical relationship between the implementation elements of curriculum ideological and political education from the micro level, and defines the basic responsibilities, in order to provide reference for curriculum teaching reform and educational quality evaluation.
[Retracted] Research on Mental Health Status of College Chinese Learners from the Perspective of Sustainable Development of Ecological Environment
Authors: Liu Yuyan , Jin Xiaoyan
Published: July 2022

Ecologically sustainable development is the environmental basis of sustainable development. In order to realize ecological sustainable development, we need to study the mental health status of college Chinese learners under the principle of prevention. This study analyzes the correlation and cognition between college Chinese learners’ mental health and their mental health status during the learning time through statistical methods and different personalities under various factors. The results show that the learners’ mental health state in learning college Chinese is different, the percentage of different personalities under certain factors is different, the relevance and recognition in the learning process are indispensable, and the effect is often different in the length of the learning time, so it is of great significance to the learners’ mental health state.
Construction and Index Analysis of Whole Chain Linkage Talent Training System Based on Fuzzy AHP Model
Authors: Yi Liu , Yan Sun
Published: July 2022

The construction and analysis of the entire chain linkage talent training system can measure the level of education and at the same time improve talents and drive development for the society. This paper uses the AHP model and CPII model to analyze the construction and index analysis of the entire chain linkage talent training system. The research shows that the weight value of personality literacy is the largest, followed by innovation literacy, emotional intelligence literacy, leadership and management ability literacy, and scientific literacy; in the analysis of the importance of the first-level indicators, it is found that most people think that personality literacy is the most important; in the consistency test of the first-level indicators, it shows that the results have good consistency; in the comment set of the second-level indicators, the highest comment value is expertise, independence and practicality, and diligence. Through the evaluation of the talent training system and its operation quality, it can provide reform ideas for education and management departments and can fully play the role of talents, create a good working atmosphere, and create suitable ways to improve capabilities. Through AHP’s research on the construction of the entire chain linkage talent training system and indicators, it is found that the personality literacy has the largest weight value among the five first-level indicators. The cultivation of leadership, management ability, scientific literacy, etc. can better reflect the characteristics of talents.
Teaching Evaluation Method Based on Fuzzy Support Vector Machine Algorithm
Authors: Wang Zhihong , Xu Tao , Wang Lixia
Published: July 2022

For schools, the quality of teaching and learning is an important guarantee for achieving educational goals. There are very many factors affecting teaching quality, including hard and soft factors. Among them, the soft factors mainly refer to the teaching quality of teachers and the teaching management mechanism of schools. The teaching quality of teachers is the most critical among all factors. Traditional teaching quality evaluation (TQE) mainly adopts the way of manual scoring. This way of TQE lacks data support, and there is no evaluation mechanism based on multiple data sources. Therefore, the TQE results obtained in this way are subjective and inaccurate. Considering that the main subjects of the whole teaching session are teachers, students, and schools, each subject generates massive information in the whole teaching process. In our study, a novel evaluation method based on fuzzy classification algorithm is proposed to be applied in teaching evaluation. Firstly, this paper collects the relevant data generated by the three subjects in the teaching process, respectively. Secondly, after removing the unsuitable data, principal component analysis is used to extract the main features of the applicable data. Finally, a fuzzy support vector machine (FSVM) is used to classify and analyze the feature data in order to derive each teacher’s teaching evaluation results. The final evaluation results in this paper are divided into five grades, i.e., five categories: excellent, good, moderate, passing, and failing. The comparison with other algorithms demonstrates that the teaching evaluation model based on fuzzy comprehensive evaluation algorithm used in this paper has the advantages of high evaluation accuracy and objectivity, and we hope that this study will be useful in the field of teaching evaluation.
Development and Design Case Function Comparison of Panoramic Roaming System of Virtual Museum Based on Pano2VR
Authors: Cao Kai
Published: July 2022

The development of modern information technology gives birth to virtual museums. The exhibition of physical museums is evolving into online virtual exhibition, allowing users to have an immersive experience of museums without leaving home. Through content analysis, this study analyzes the status quo of online virtual museums in China and discovers two major problems: the exhibits are mainly brief introductory graphs and texts, and the exhibition module is independent of the panoramic roaming module. To solve the problems, Pano2VR was used to develop and design a virtual museum panorama roaming system for Hubei Museum. After realizing the map navigation of the museum, photos of the museum were taken on the site and prepared into a panorama. Then, the panorama roaming of the virtual museum was achieved based on Pano2VR. Finally, the results were displayed on the virtual web page of the museum, and real interactive virtual exhibitions were conducted on the website platform. The proposed system enhances the research value of the cultural relics of the Hubei Museum and increases the interaction with users.
[Retracted] Investigating Celastrol’s Anti‐DCM Targets and Mechanisms via Network Pharmacology and Experimental Validation
Authors: Xi Rui , Wan Yongxin , Yang Lihong , Zhang Jingying , Yang Liu , Yang Shuai , Chai Rui , Mu Fengchen , Sun Qiting , Yan Rui , Wu Zhifang , Li Sijin
Published: July 2022

Background and Purpose. DCM (diabetic cardiomyopathy), which may lead to significant complications including cardiovascular lesions, arrhythmia, and even heart failure, has a beginning element now known to be myocardial energy rebuilding. There are limited research on Celastrol’s ability to guard against this in the United States and elsewhere. Since it has not been known, whether Celastrol could reverse the early energy remodeling process, thus, it was hypothesized that triptolide Celastrol is suitable for the reversal of early myocardial energy remodeling in DCM. And our aim is to predict the targets and underlying mechanism of Celastrol in reversing the early energy remodeling for DCM. Methods. Data from TCMSP and GEO databases were utilized to identify targets for Celastrol on DCM. The relationship between the major targets and conventional glycolipid metabolism was obtained with Spearman correlation analysis. Experiments on animals were conducted utilizing healthy control (HC), low‐dose Celastrol interventions (CL), and no intervention groups (NC), all of which had 8 SD rats in each group. To study alterations in signaling molecules, RT‐PCR was performed. Results. There were 76 common targets and 5 major targets for Celastrol‐DCM. Celastrol have been found to regulate AGE‐RAGE, TNF, MAPK, TOLL‐like receptors, insulin resistance, and other signaling pathways, and they are closely linked to adipocytokines, fatty acid metabolism, glycolipid biosynthesis, and glycosylphosphati‐dylinositol biosynthesis on DCM. These five major targets have been found to regulate these pathways. Experiments on rats indicated that P38 MAPK was considerably elevated in the cardiac tissue from rats in the CL and NC groups compared to the HC group, and the difference was statistically significant (P < 0.01). Significant differences were seen between the CL and NC groups in P38 MAPK levels, with a statistical significance level of less than 0.05. Conclusion. Celastrol may play a role in reversing energy remodeling, anti‐inflammation, and oxidative stress via modulating p38 protein expression in the MAPK pathway, which have been shown in the treatment of DCM.
Effect of Thymoquinone on Renal Damage Induced by Hyperlipidemia in LDL Receptor‐Deficient (LDL‐R-/-) Mice
Authors: Li Wanjing , Zhang Hujin , Zhang Lei , Zhang Taipeng , Ding Hui
Published: July 2022

Hyperlipidemia is a well‐established risk factor for kidney injury, which can lead to chronic kidney disease (CKD). Thymoquinone (TQ) is one of the most active ingredients in Nigella sativa seeds. It has various beneficial properties, including antioxidant and anti‐inflammatory activities. TQ also exerts positive effects on doxorubicin‐ (DOX‐) induced nephropathy and ischemia‐reperfusion‐induced kidney injury in rats. Therefore, in this study, we investigated the possible protective effects of TQ against kidney injury in low‐density lipoprotein receptor‐deficient (LDL‐R-/-) mice. Eight‐week‐old male LDL‐R-/- mice were randomly divided into the following three groups: normal diet (ND group), high‐fat diet (HFD group), and HFD combined with TQ (HFD+TQ group). The mice were fed the same diet for eight weeks. After eight weeks, we performed serological analysis of the mice in all three groups. We histologically analyzed the kidney tissue and also investigated the expression of proinflammatory cytokines in the kidney tissue. Metabolic characteristics, including total cholesterol (TC), low‐density lipoprotein‐cholesterol (LDL‐C), and creatinine (CRE) levels, were lower in the LDL‐R-/- HFD+TQ mice than in the HFD mice. Periodic acid‐Schiff (PAS) and Masson’s trichrome staining revealed excessive lipid deposition and collagen accumulation in the kidneys of the LDL‐R-/- HFD mice, which were significantly reduced in the LDL‐R-/- HFD+TQ mice. Furthermore, macrophages and levels of proinflammatory cytokines were lower in the kidney tissues of the LDL‐R-/- HFD+TQ mice than in those of the LDL‐R-/- HFD mice. Moreover, profibrosis‐ and oxidative stress‐related protein expression was lower in the kidney tissues of the LDL‐R-/- HFD+TQ mice than in those of the LDL‐R-/- HFD mice. These results indicate that TQ may be a potential therapeutic agent for kidney damage caused by hyperlipidemia.
Image Segmentation of Retinal Blood Vessels Based on Dual-Attention Multiscale Feature Fusion
Authors: Gao Jixun , Huang Quanzhen , Gao Zhendong , Chen Suxia
Published: July 2022

Aiming at the problem of insufficient details of retinal blood vessel segmentation in current research methods, this paper proposes a multiscale feature fusion residual network based on dual attention. Specifically, a feature fusion residual module with adaptive calibration weight features is designed, which avoids gradient dispersion and network degradation while effectively extracting image details. The SA module and ECA module are used many times in the backbone feature extraction network to adaptively select the focus position to generate more discriminative feature representations; at the same time, the information of different levels of the network is fused, and long-range and short-range features are used. This method aggregates low-level and high-level feature information, which effectively improves the segmentation performance. The experimental results show that the method in this paper achieves the classification accuracy of 0.9795 and 0.9785 on the STARE and DRIVE datasets, respectively, and the classification performance is better than the current mainstream methods.
Logistics Distribution Route Optimization Based on Genetic Algorithm
Authors: Xin Liu , Xu Peng , Manyi Gu
Published: July 2022

Aiming at the problem of logistic division based on genetic algorithm, it is planned to study the improvement of logistic distribution methods. We first meet the requirements of the genetic algorithm of logistic development, use the division method to divide the delivery area of the gene, and formulate a functional delivery plan, which generally includes weight measurement, measurement time, customer value measurement, instrument measurement time, and the whole process index. We set weight goals and find the best way to improve genetic algorithm delivery. The experimental comparison results show that the optimal method takes less than 2 minutes to find the optimal method, while the normal process takes 4 minutes to find the optimal method, and the longest can reach 5 minutes. The comparison shows that the traditional algorithm takes longer to find the correct way than the algorithm developed this time. Finally, the simple logistic distribution optimization method model and the soft time-limited logistic distribution processing optimization model are calculated and simulated by the genetic testing algorithm and genetic algorithm development. The effectiveness of the improved genetic algorithm in local research and the effectiveness of the logistic transportation allocation solution are determined.
[Retracted] Correlation Analysis of Cytochrome P450 SNPs in Hepatitis B‐Caused Cirrhosis Patients
Authors: Li Qing-Ya , Yang Xiaona , Guo Zhi-Zhong
Published: July 2022

Aim. The aim of the present research was to find the correlation of single nucleotide polymorphisms (SNPs) of cytochrome P450 (CYP450) and hepatitis B‐caused cirrhosis. Methods. Collection of specimens was done from 297 volunteers with confirmed hepatitis B‐caused cirrhosis as well as 120 healthy volunteers in China. Individuals were categorized into three classes, i.e., A, B, and C, on the basis of the Child‐Pugh‐Turcotte (CPT) value of diseased people, while the Child‐Pugh‐Turcotte score was determined by rating the below mentioned 5 parameters, i.e., serum volume of bilirubin as well as albumin, prothrombin time, ascites, and encephalopathy. Twenty‐four SNPs in the CYP450 superfamily including CYP1A2, CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CY3A4 were detected using the SNaPshot assay. Results. CYP1A2‐G2964A, CYP1A2‐C733A, and CYP1A2‐T5347C in all 24 SNP loci attained significance. AA genotype at CYP1A2‐G2964A (P = 0.048) and CC genotype at CYP1A2‐T5347C (P = 0.049) were significantly correlated with hepatitis B‐caused cirrhosis. Moreover, an allele at CYP1A2‐G2964A (P = 0.032) and C allele at CYP1A2‐T5347C (P = 0.016) were associated with hepatitis B‐caused cirrhosis. Furthermore, AC plus AA genotype at CYP1A2‐C733A correlated with a TCM syndrome, “damp abundance due to spleen asthenia syndrome” (P = 0.039) in individuals suffering from hepatitis B‐resulted cirrhosis. Conclusion. The findings of the current study suggested that AA genotype at CYP1A2‐G2964A and CC genotype at CYP1A2‐T5347C may be higher risk in the occurrence of hepatitis B‐caused cirrhosis. Moreover, patients with AC plus AA genotype at CYP1A2‐C733A may be susceptible to appear having “damp abundance due to spleen asthenia syndrome.”
Analysis of Syntactic Complexity and Semantic Coherence of Academic English Writing Based on Particle Swarm Optimization
Authors: Li Lei
Published: July 2022

Writing is an important part of testing language ability, and it is urgent to find some objective indicators to determine and evaluate the surface language structure, which will help language learners’ better master the target language. Complexity and semantic coherence are considered to be an important factor in the teaching of second language writing. In practice, due to the complexity of English writing syntax, such as a large number of high-dimensional nonlinear optimization problems, a new intelligent evaluation method is needed to solve them. At present, particle swarm optimization (PSO) has been widely used in function optimization, neural network training, combinatorial optimization, and other fields. This paper studies the syntactic complexity and semantic coherence of academic English writing based on PSO. The number of phrases is related to writing achievement. When the number of experiments reaches 25, the significant values of syntactic complexity and semantic coherence of data mining algorithm, artificial intelligence algorithm, decision tree algorithm, and PSO algorithm are 0.008, 0.003, 0.002, and 0.013, respectively, which shows that PSO algorithm is the best among them.