bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved Fake News Detection Model by Applying a Recursive Feature Elimination Approach for Credibility Assessment and Uncertainty
Authors: Agarwal I. Y. , Rana D. P.
Published: August 2022

World Health Organization (W.H.O) has coined the word “Infodemic” to refer to the dissemination of fake news during this pandemic, which is considered to be as harmful as the virus itself. Verifying the information available on the internet is a prerequisite to ensuring the ecosystem is maintained which is the driving force behind this work. The primary goal of this study is to address the problem of time-consuming automatically voluminous fake news detection of certain data and consider the uncertainty of data from causal relations using a rich feature set. This research produces significant feature reduction for reduced time and improved accuracy by filtering out significant features using recursive feature selection (RFE). The retained features by the RFE algorithm are also compared to a standard statistical measure of Pearson’s correlation to ensure no information loss while reducing features. The suggested methodology has also defined appropriate class output assurance levels and accurate prediction ambiguity for the fake identification jobs. Comparative analysis for existing methods for feature selection is performed. The result of experimentation testifies the improvement of a 6% increase in precision and a 97% reduction in execution time.
Comparative analysis on the three popular causality modeling methodologies
Authors: Shi Xueyang , Cheng Bing
Published: August 2022

The idea of causality has lasted for over thousands of years. Unlike the idea of statistical correlation and regression, performing causal modeling and prediction is an even more challenging job. Under the intervention framework of causality, causal modeling is gaining popularity given the advances of big data and computational ability in recent years. In different scientific research areas, there exist three powerful causal modeling methodologies, namely, the potential outcomes method in statistics, the instrumental variables method in economics and Judea Pearl’s causal diagram method (do-calculus) in computer science and artificial intelligence. In this paper, by linear causal modeling assumption, we prove that the above three causal methodologies are equivalent. That is, given a causal problem, all of the three modeling methods will generate the same causal relationship conclusion, despite that they own different causal inference processes. During the past one-and-half years, the global economy suffers severe impacts from the COVID-19 pandemic. To fight the deadly pandemic, various social distancing measures and actions, taken by the countries, are effective in curbing the impact of the pandemic over the population. However, such social distancing policy has an adverse effect over the global economy growth; if more stringent measures were taken, then there would be suffering in the forms of much slower economic growth and higher unemployment. In this paper, we study the causal relationships between social distancing, fatality rate and economy growth. This work provides a useful tool for the governments to keep balance between controlling the pandemic and maintaining economic growth.
A Nonlinear Optimal Control Method for Attitude Stabilization of Micro-Satellites
Authors: Rigatos G. , Abbaszadeh M. , Busawon K. , Dala L.
Published: August 2022

Attitude control and stabilization of micro-satellites is a nontrivial problem due to the highly nonlinear and multivariable structure of the satellites’ state-space model. In this paper, a novel nonlinear optimal (H-infinity) control approach is developed for this control problem. The dynamic model of the satellite’s attitude dynamics undergoes first approximate linearization around a temporary operating point which is updated at each iteration of the control algorithm. The linearization process relies on first-order Taylor series expansion and on the computation of the Jacobian matrices of the state-space model of the satellite’s attitude dynamics. For the approximately linearized description of the satellite’s attitude a stabilizing H-infinity feedback controller is designed. To compute the controller’s feedback gains, an algebraic Riccati equation is solved at each time-step of the control method. The stability properties of the control scheme are proven through Lyapunov analysis. It is also demonstrated that the control method retains the advantages of linear optimal control that is fast and accurate tracking of the reference setpoints under moderate variations of the control inputs.
An Empirical Study on IPO Model Construction of Undergraduate Education Quality Evaluation in China from the Statistical Pattern Recognition Approach In NLP
Authors: Chen Zhaojun , Bao Yanhai , Zhu Tongxun
Published: August 2022

Based on the analysis of 1,497 samples from the survey of national undergraduate educational administrators, the IPO model of undergraduate education quality evaluation from the perspective of managers can be effectively verified. The quality of higher education needs the accountability of higher education and evaluation of student learning outcomes. The empirical studies show the effects on the various dimensions of quality provisions which were not the same. The main findings are that the input of undergraduate education and teaching can not only directly and positively predict the output of undergraduate education and teaching, but also can positively predict the output of undergraduate education and teaching through the partial mediating effect of the process of undergraduate education and teaching. The research suggests that under the given material conditions, it is essential to enhance the “soft input” of undergraduate education and teaching, to strengthen the process construction of undergraduate education and teaching, to enhance the process quality control in the implementation of humanistic care, to pay attention to the development effect of teachers and students and the development effect of school-running characteristics, and to promote the connotative development of colleges and universities. The study provides a practical framework, model, and guidelines that can be used for undergraduate education institutions to evaluate and enhance the performance to effectively work in society. The quality evaluation of undergraduate education needs to focus from the quality of students' learning outcomes to the comprehensive consideration of “input-process-outcome”.
Joint Intent Detection Model for Task-oriented Human-Computer Dialogue System using Asynchronous Training
Authors: Wu Yirui , Li Hao , Zhang Lilai , Dong Chen , Huang Qian , Wan Shaohua
Published: August 2022

How to accurately understand low-resource languages is the core of the task-oriented human-computer dialogue system. Language understanding consists of two sub-tasks, i.e., intent detection and slot filling. Intent detection still faces challenges due to semantic ambiguity and implicit intentions with users’ input. Moreover, separately modeling intent detection and slot filling significantly decrease the correctness and relevance between questions and answers. To address these issues, we propose a joint intent detection method using asynchronous training strategy. The proposed method firstly encodes local text information extracted by CNN and relationship information among words emphasized by attention structure. Later, a joint intent detection model with asynchronous training strategy is proposed by either fusing hidden states of intent detection and slot filling layers, or adopting the key information to fine-tune the whole network, greatly increasing the relevance of intent detection and slot filling subtasks. The accuracy achieved by the proposed method tested on an open-source airline travel dataset and a self-collected electricity service dataset, i.e., ATIS and ECSF, are 97.49% and 89.68%, respectively, which proves the effectiveness of joint learning and asynchronous training.
An Object Localization-based Dense Image Captioning Framework in Hindi
Authors: Mishra Santosh Kumar , Harshit  , Saha Sriparna , Bhattacharyya Pushpak
Published: August 2022

Dense image captioning is a task that requires generating localized captions in natural language for multiple regions of an image. This task leverages its functionalities from both computer vision for recognizing regions in an image and natural language processing for generating captions. Numerous works have been carried out on dense image captioning for resource-rich languages like English; however, resource-poor languages like Hindi are not explored. Hindi is one of India’s official languages and is the third most spoken language in the world. This article proposes a dense image captioning model to describe different segments of an image by generating more than one caption in the Hindi language. For localized image recognition and language modeling, we employ Faster R-CNN and Long Short-Term Memory (LSTM), respectively. Apart from this, we conduct various experiments using gated recurrent units (GRUs) and attention mechanism. By manually translating the well-known Visual Genome dataset from English to Hindi, a dataset has been created for dense image captioning in Hindi. The experiments conducted on the newly constructed Hindi dense image captioning dataset illustrate the efficacy of the proposed method over the state-of-the-art methods.
Obesity Dysregulates the Immune Response to Influenza Infection and Vaccination Through Metabolic and Inflammatory Mechanisms
Authors: Shaikh Saame Raza , MacIver Nancie J. , Beck Melinda A.
Published: August 2022

The COVID-19 pandemic demonstrates that obesity alone, independent of comorbidities, is a significant risk factor for severe outcomes from infection. This susceptibility mirrors a similar pattern with influenza infection; that is, obesity is a unique risk factor for increased morbidity and mortality. Therefore, it is critical to understand how obesity contributes to a reduced ability to respond to respiratory viral infections. Herein, we discuss human and animal studies with influenza infection and vaccination that show obesity impairs immunity. We cover several key mechanisms for the dysfunction. These mechanisms include systemic and cellular level changes that dysregulate immune cell metabolism and function in addition to how obesity promotes deficiencies in metabolites that control the resolution of inflammation and infection. Finally, we discuss major gaps in knowledge, particularly as they pertain to diet and mechanisms, which will drive future efforts to improve outcomes in response to respiratory viral infections in an increasingly obese population.
Altering the Photoluminescence Emission Quenching and Energy Transfer for the Photocatalytic Behaviour of ZnO Nanostructures
Authors: Sahu Keya , Ali Asif , Kar Asit Kumar
Published: August 2022

Synthesis of nanoparticles (NPs) is gaining attention as a cost-effective and environmentally acceptable alternative to remove the pollutant by facile photocatalysis process. Role of hydrothermal treatment on Zinc-oxide (ZnO) nanostructures were investigated using non-ionic surfactant diethanolamine (DEA). Further, in order to investigate the effect of DEA on morphological variation different concentration of DEA was used. The samples were thoroughly characterized by XRD, Rietveld analysis, FESEM and TEM to get insight idea about the ZnO structural and morphological properties. Moreover, XPS spectra reveal the variation of surface oxygen defects as hydrothermal treatment induced more defects to ZnO material. BET measurement reveals the alteration of surface area and pore size of ZnO sample. The surface defect-states (mostly oxygen vacancies) of the catalyst nanoparticles can influence the photocatalytic degradation of MB dye activated by ZnO nanoflowers via a non-radiative energy transfer pathway. A steady-state photoluminescence analysis validated the photoinduced electron transport from ZnO to MB dye. Steady state photoluminesence emission spectra established one to one correlation between the defects and colour emission from ZnO. Spectral overlap between donor (ZnO) to acceptor (MB dye) also enhanced greatly after hydrothermal treatment ascribing more Förster resonance energy transfer (FRET) which accelerates photocatalytic degradation efficiency of methylene blue (MB) dye under UV light irradiation. The defect-engineered ZnO nanoparticles synthesized through facile hydrothermal treatment led to an efficient decolourization of MB dye which was strengthened by FRET based on a correlation of photocatalytic degradation and defect mediated colour emission.
Lightweight Smoke Recognition Based on Deep Convolution and Self-Attention
Authors: Zhao Yang , Wang Yigang , Jung Hoi-Kyung , Jin Yongqiang , Hua Dan , Xu Sen
Published: August 2022

Deep convolutional networks have better smoke recognition performance. However, a lightweight network model and high recognition accuracy cannot be balanced when deployed on hardware with limited computing resources such as edge computing. Based on this background, we propose a novel smoke recognition network that combines convolutional networks (CNN) and self-attention. The core ideas of this framework are as follows: (1) Combine the depthwise convolution and asymmetric convolution of large convolution kernels to construct a lightweight CNN model, and realize multiscale extraction of feature information with slight model complexity. (2) Combined with the self-attention in transformer, a skip-connection branch is designed, which improves the feature extraction capability of the backbone network through parallel processing and fusion of feature map information. (3) Fusion multicomponent discrete cosine transform (DCT) is used to compress channel information and expand the ability of global average pooling (GAP) to aggregate feature maps. The proposed DCT-GAP improves the accuracy of the network without adding additional computational costs. Experimental results show that the proposed CSANet achieves an average accuracy of over 98.3% with 238 M FLOPs and 5.8 M parameters on the homemade smoke dataset, outperforming state-of-the-art competitors.
KDELR3 Is a Prognostic Biomarker Related to the Immune Infiltration and Chemoresistance of Anticancer Drugs in Uveal Melanoma
Authors: Zhang Jingnan , Zhang Juan , Guan Jing , Yu Lisha , Yan Shilong
Published: August 2022

Uveal melanoma (UM) is an intraocular malignancy in adults in which approximately 50% of patients develop metastatic diseases and have a poor clinical outcome. Immunotherapies are quickly becoming a need, and recent research has produced some amazing achievements in this area. In the current investigation, an attempt was made to evaluate the prognostic usefulness of KDELR3 in UM, particularly its connection with tumor-infiltrating lymphocytes (TILs). The expression patterns of mRNAs and related clinical data of 80 UM patients were obtained from The Cancer Genome Atlas (TCGA). By using RT-PCR, we were able to investigate whether or not UM cells and D78 cells expressed KDELR3. The Kaplan-Meier approach, as well as univariate and multivariate tests, was utilized in order to investigate the potential predictive significance of KDELR3 expression. The associations between KDELR3 and TILs and immunological checkpoints were analyzed in order to evaluate the effect that KDELR3 may have on UM immunotherapy. On the basis of the differential expression of KDELR3, a distribution of the half-maximal inhibitory concentration (IC50) of various targeted medicines was observed. In this study, we found that the expression of KDELR3 was distinctly increased in most types of tumors. In addition, KDELR3 was highly expressed in UM cells. Moreover, patients with high KDELR3 expression exhibited a shorter overall survival and disease-free survival than those with low KDELR3 expression. Multivariate analyses confirmed that KDELR3 expression was an independent prognostic factor for overall survival and disease-free survival in patients with UM. Furthermore, KDELR3 expression was demonstrated to be positively correlated with macrophage M1, T cell CD8, T cell follicular helper, dendritic cell resting, and T cell CD4 memory activated. Meanwhile, the expression of KDELR3 was related to several immune checkpoints. The IC50 of AP-24534, BHG712, bleomycin, camptothecin, cisplatin, cytarabine, GSK1070916, and tipifarnib was higher in the KDELR3 high-expression group. In conclusion, KDELR3 may be applied as a potential diagnostic and prognostic biomarker for UM patients.
Remote-Sensing Inversion Method for Evapotranspiration by Fusing Knowledge and Multisource Data
Authors: Wang Jingui , Cheng Dongjuan , Wu Lihui , Yu Xueyuan
Published: August 2022

Evapotranspiration (ET) is the main process parameter of the land surface heat and water balance. Evapotranspiration remote-sensing inversion can be divided into two types of methods, process-driven and data-driven, according to the model power. This paper presents a comprehensive and systematic review of the research progress of data-driven ET remote-sensing inversion methods and their products; reviews the basic principles, advantages, and disadvantages of related methods/products from three perspectives: empirical regression, machine learning, and data fusion; and finally indicates the development direction of data-driven ET remote-sensing inversion research.
How Does Built Environment Affect Metro Trip Time of Elderly? Evidence from Smart Card Data in Nanjing
Authors: Shi Zhuangbin , Liu Yang , He Mingwei , Liu Qiyang
Published: August 2022

Understanding the determinants of elderly people’s public transport usage patterns can offer new insights into elderly mobility issues and provide policy implications for planning toward an aging-friendly and sustainable city. However, few studies have examined the impact of the built environment on the trip time of the elderly using big data. Moreover, the elderly’s trip time has been mostly investigated by the multivariate linear regression model (MLR), ignoring the non-linear association between explanatory variables and trip time. Using smart card data from Nanjing in 2019, this study employs a gradient boosting regression trees (GBRT) model to probe into the correlations between the built environment and the elderly’s trip time. The results show that significant non-linear relationships exist between trip time and the selected explanatory variables, which cannot be captured by the MLR model. It suggests that relevant policy implementations should be carried out in conjunction with the elderly’s travel environment by regarding their threshold effects. Besides, interaction effects of spatial attributes on trip time are identified in our study. For example, elderly people living in the exurban area are more likely to take long-distance metro travel for their physical exercise. These findings demonstrate that planners and policymakers should not only consider one single built-environment factor, but also the interactions of various factors to enhance elderly mobility.
Treating Acute Severe Eosinophilic Asthma with IL-5 Inhibitors in ICU
Authors: Barbarot Nicolas , Nourry Emmanuelle , Massart Nicolas , Legay François , Debarre Matthieu , Fillatre Pierre , Magalhaes Eric , Mari Arnaud , Wallois Julien , Briens Eric , Jouneau Stéphane
Published: August 2022

Introduction. About 10% of the 300 million people worldwide who suffer from asthma have a severe disease that is uncontrolled despite treatment with inhaled corticosteroids and long-acting beta agonists. The eosinophilic inflammation pathway in the respiratory tract and blood is involved and interleukin-5 (IL-5) has recently been identified as a major promotor of this pathway. The anti-IL-5 antibodies reduce the incidence of exacerbation and allowed steroid sparing in severe asthma patients but only two case reports have been published on their use in critical care. Case Presentation. This report describes the extraordinary clinical improvement of a young patient with steroid-refractory eosinophilic acute severe asthma who required mechanical ventilation, VV-ECMO followed by treatment with mepolizumab. The salient point in this case is the use of an anti-IL-5 monoclonal antibody for a critically ill patient whose condition was deteriorating despite mechanical ventilation and VV-ECMO. The usual steroid treatment failed to control the increase in blood eosinophils or his bronchial inflammation and constriction. Conclusion. Anti-IL-5 antibodies are now a standard treatment for severe eosinophilic asthma that can also be useful in an emergency to treat steroid-refractory eosinophilic acute severe asthma.
Data Sharing and Online Political Education Based on Edge Computing Network Optimization
Authors: Zeng Yihua , Luo Mingwan
Published: August 2022

The rapid development of Internet of Things services and the rapid growth of new broadband services have promoted the ever-increasing computing demands of mobile devices. As an emerging mobile computing technology, mobile edge computing is an important key technology to improve computing services for mobile devices. Mobile edge computing technology provides an important way for users to provide computing services with low latency and high computing performance. At the same time, it also provides technical means to develop online education. Online education called E-learning is an education model that uses the Internet as a medium. Through the Internet, students can conduct educational activities thousands of miles away from the teacher. In addition, students can learn at any time by referring to and downloading network code resources, thereby breaking the space and time constraints. The development of informatization of university education is an important part of the development of informatization, and it is the adaptation and combination of the application of information technology in the education field. At present, the informatization of universities at home and abroad has ushered in the era of digital campus construction, and most of its focus is on the reorganization of functional departments and the integration of independent systems. By analyzing the needs of data sharing and exchange, this article plans to use existing mature technologies, such as data warehouse, data synchronization update, and data security, based on DataX to design the data sharing and exchange plan of the public data platform, including data standards, coding standards are designed to achieve uniform rules, formats, and codes to build an online political education platform.
Efficient Hybrid Iterative Method for Signal Detection in Massive MIMO Uplink System over AWGN Channel
Authors: Gebeyehu Zelalem Melak , Singh Ram Sewak , Mishra Satyasis , Rathee Davinder Singh
Published: August 2022

Massive multiple input multiple output (massive MIMO) is a key technology in fifth-generation (5G) and beyond fifth-generation (B5G) networks. It improves performance metrics such as gain, energy efficiency, spectral efficiency, and bit error rate (BER). Because of the large number of users and antennas, sophisticated processing is required to detect the transmitted message signal. One of the challenges in massive MIMO systems is transmitted message signal detection. To respond to these challenges, several detection algorithms have been developed, including minimum mean squared error (MMSE), zero forcing (ZF), matched filter (MF), conjugate-gradient (CG), gauss-seidel (GS), and optimized coordinate descent (OCD). Although the ZF and MMSE algorithms perform well, their computational complexity is high due to direct matrix inversion. When the number of users is much lower than the number of antennas, the MF algorithm performs well. However, as the number of users increases, the performance of the MF algorithm degrades. Although the OCD, CG, and GS algorithms have less computational complexity than the MMSE algorithm, they perform poorly in comparison. To address and resolve the shortcomings of existing methods, an efficient iterative algorithm has been proposed in this manuscript, which is a hybrid method possessing the combination of MMSE with the alternating direction method of multipliers (ADMM) technique and Gauss-Seidel method. The initial vector has a large influence on the performance, complexity, and convergence rate of such iterative algorithms. The proposed detector’s initial solution is determined using the diagonal matrix and MMSE with the ADMM technique. The proposed algorithm’s performance and complexity are compared with existing algorithms based on BER and the real number of multiplications, respectively. The numerical results revealed that the proposed algorithm achieves the desired performance with a small number of iterations and a significant reduction in computational complexity. At 8QAM, SNR = 20 dB, 80 × 120 massive MIMO antenna configuration, and n = 2, the percentage performance improvement of the proposed detector from the GS detector is 99.82%. At 32QAM, SNR = 25 dB, 120 × 180 antenna configuration, and n = 5, performance improvement of the proposed detector is 99.89%. At 64QAM, SNR = 28 dB, 80 × 120 antenna configuration, and n = 3, performance improvement of the proposed detector is 99.93%.
3D Indoor Scene Synthesis System Based on Collaborative Retrieval
Authors: Weijun Yu
Published: August 2022

In order to solve the research problem of collaborative retrieval 3D indoor scene synthesis system, an automatic synthesis method of 3D indoor scene is proposed. In the research fields of computer graphics and computer vision, the modeling of various 3D scenes has always been a hot spot and a very valuable research topic for Chinese and foreign scholars. GMM is used to fit the position of the object on the support surface, and the position distribution model is obtained. Firstly, a file is created to store the normalized position of the counted object on the support surface. The number of coordinates represents the number of times the object appears in the scene library. Then, the Gaussian mixture model is used to fit the content of the file to obtain the location distribution model. For the initial indoor scene and the description of small objects to be added to the scene given by the user, select small objects with the help of the material library. Shapenetcorcv2 is the core part of the database, with a total of 55 categories and more than 50000 model instances. The automatic placement of these small objects is realized by using the scene synthesis algorithm, which shows the continuous enrichment of the evolution process of simple scenes to solve various problems of 3D indoor scene modeling. This method not only reflects the design requirements of users but also avoids cumbersome manual operation.
Potential Mechanisms of Shu Gan Jie Yu Capsule in the Treatment of Mild to Moderate Depression Based on Systemic Pharmacology and Current Evidence
Authors: Li Taiping , Qiu Tian , Zeng Yanyan , Kang Bing , Tang Xianglong , Yang Ning , Xiao Hong
Published: August 2022

Background. Shu Gan Jie Yu (SGJY) capsule has a good effect on relieving depressive symptoms in China. However, the mechanism of action is still unclear. Therefore, systemic pharmacology and molecular docking approaches were used to clarify its corresponding antidepressant mechanisms. Methods. Traditional Chinese Medicine Database and Analysis Platform (TCMSP), the Encyclopedia of Traditional Chinese Medicine (ETCM), and Swiss Target Prediction servers were used to screen and predict the bioactive components of the SGJY capsule and their antidepressive targets. Mild to moderate depression (MMD) related genes were obtained from GeneCards and DisGeNET databases. A network of bioactive components-therapeutic targets of the SGJY capsule was established by STRING 11.5 and Cytoscape 3.9.0 software. Gene function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by utilizing Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform. Active components were taken to dock with the hypothetical proteins by iGEMDOCK and SwissDock, and the docking details were visually displayed by UCSF Chimera software. Then, the related research literature of the SGJY capsule was reviewed, summarized, sorted, and analyzed, including experimental evidence and clinical experience. Results. Seven active components and 45 intersection targets were included in the study. PPI network had genuinely uncovered the potential therapeutic targets, such as AKT1, HSP90AA1, ESR1, EGFR, and PTGS2. KEGG pathway analysis showed that the mechanism of the SGJY capsule on MMD was mainly involved in the PI3K-Akt signaling pathway. Conclusions. In this study, we have successfully predicted the biochemically active constituents, potential therapeutic targets, and comprehensively predicted the related drug-gene interaction of the SGJY capsule for treating MMD and provided a basis for subsequent experiments.
Teaching Method Design of Tennis Baseline Stroke Technique Relying on Neural Network Learning Algorithm
Authors: Sheng Xia , Zhou Shijie
Published: August 2022

Tennis is an important sporting event. In China, the number of people who study and participate in tennis can be said to be quite large, but there are not many people who insist on playing tennis in the end. Some coaches and tennis groups have come up with a number of different teaching styles in order to keep tennis players going. Its main features are as follows: To cultivate students’ interest in learning, the sequence of strokes in tennis is used for teaching. Although tennis has no special requirements for the physical condition of the athlete, tennis is a relatively difficult sport to master. What is lacking at present is more effective and simpler teaching methods, so training should be carried out in a targeted manner in the tennis baseline stroke technique. In order to make the tennis baseline stroke teaching more intuitive and easy to learn, this paper applies the neural network learning algorithm to the research and analysis of the tennis baseline stroke technique and proposes the core technology of the tennis baseline stroke technique. On this basis, the key techniques of tennis baseline stroke technique are discussed. Based on this, a basic model based on deep convolutional neural network is proposed. The experimental results show that the forehand scores of the three throwing methods in the experimental group are 6.47 and 7.28, respectively, which is better than the control group. The three throwing methods are self-throwing, close throwing, and long throwing. It shows that the teaching method in this paper can greatly improve the students’ tennis baseline hitting skills.
Ve‐Degree, Ev‐Degree, and Degree‐Based Topological Indices of Fenofibrate
Authors: Delen Sadik , Khan Riaz Hussain , Kamran Muhammad , Salamat Nadeem , Baig A. Q. , Naci Cangul Ismail , Pandit M. K.
Published: August 2022

The molecular topology of a graph is described by topological indices, which are numerical measures. In theoretical chemistry, topological indices are numerical quantities that are used to represent the molecular topology of networks. These topological indices can be used to calculate several physical and chemical properties of chemical compounds, such as boiling point, entropy, heat generation, and vaporization enthalpy. Graph theory comes in handy when looking at the link between certain topological indices of some derived graphs. In the ongoing research, we determine ve‐degree, ev‐degree, and degree‐based (D‐based) topological indices of fenofibrate’s chemical structure. These topological indices are the Zagreb index, general Randić index, modified Zagreb index, and forgotten topological index. These indices are very helpful to study the characterization of the given structure.
Inhibitory Effect of Trihydroxy Isoflavone on Neuronal Apoptosis in Natural Aging Rats
Authors: Zhao Ke , Li Shenghui , Chen Jianzhou , Jin Qinghua
Published: August 2022

Objective. To explore the impact of genistein (Gen) on the apoptosis of neuronal cells in naturally aged rats and its mechanism. Methods. Fifty SD male rats were allocated into five groups at random, including youth group (3M group), natural aging group (24M group), and Gen low-, medium-, and high-dose groups. Starting from 18 months of age, Gen 10, 30, and 60 mg-kg-1 were administered via gavage to the Gen low-, medium-, and high-dose groups, respectively, while the rats in the natural aging group was given saline by gavage until 24 months of age, and the drug was stopped for 1 d per week for 6 months. The protein expression of target genes was examined using western blotting. Results. In contrast to the 3M group, the 24M group rats showed disturbed neuronal cell arrangement and massive cell degeneration. After 6 months of Gen intervention, in contrast to the 24M group, the neural cell pathology in the CA3 area of the hippocampus improved and cell apoptotic decreased observably. In contrast to the 3M group, the protein expression of c-Jun amino-terminal kinase (p-JNK), C/EBP homologous protein (CHOP), inflammatory vesicle 3-associated factor (NLRP3), cysteine protease-1 (Caspase-1), and apoptosis-related punctate protein (ASC) and downstream inflammatory factors in the hippocampus was obviously increased in the 24M group. In contrast to the 24M group, the protein expression of p-JNK, CHOP, NLRP3, Caspase-1, and ASC and downstream inflammatory factors in the hippocampus was observably declined in Gen groups. Conclusion. Gen has a protective effect on hippocampal neurons in aging rat brain tissue via the inhibition of the ERS apoptotic signaling pathway and NLRP3 inflammatory vesicle activation.
Dynamic Scheduling and Optimal Control of Coordination Supply Chain Based on Automatic Allocation of Orders for Multicycle Integrators
Authors: Ma Wenjun
Published: August 2022

Selecting suppliers and allocating orders scientifically and rationally not only have important theoretical value but also have extremely important practical significance to enterprises. This study proposes an improved multicycle integration method that can effectively determine the optimal number of suppliers under the premise of considering the risk of supply interruption and transaction costs that may be caused by various uncertain factors. The supply interruption risk is divided into two categories: “common risk event” and “individual risk event.” On the basis of balancing the transaction cost of the company’s choice of suppliers and the financial loss of supply interruption, quantitative analysis methods are used to determine the optimal number of suppliers for the company. The problem is analyzed, the corresponding model is given, and the influence of each parameter on the optimal number of suppliers is analyzed. From the perspective of stochastic uncertainty in both customer demand and supplier supply capacity, this study analyzes the optimization problem of enterprise purchase quantity allocation under the volume discount environment. The relaxation factor is somewhat adaptive because the particle swarm is continuously updated. Aiming at ordering cost, transaction cost, transportation cost, order delay quantity, and product quality, considering random demand constraints, supplier supply capacity constraints, and multisupplier multiproduct situations, we establish supplier selection in an environment of uncertain demand and supply capacity and dynamic relaxation approximation algorithm for order allocation. The research results show that the dynamic relaxation approximation algorithm can not only determine the optimal supplier but also determine the optimal procurement allocation for each supplier.
Efficacy of Hydroxychloroquine for Improving Pregnancy Outcomes in a Female with Systemic Lupus Erythematosus and Antiphospholipid Syndrome
Authors: Takaki Yumika , Daimon Atsushi , Nunode Misa , Tanaka Tomohito , Fujita Daisuke , Ohmichi Masahide
Published: August 2022

The use of heparin and low-dose aspirin is the current conventional treatment for pregnant females with antiphospholipid syndrome (APS). However, there is no additional treatment recommended for cases where the standard treatment cannot prevent obstetric complications such as fetal loss and placental insufficiency. Recently, the addition of a novel antimalarial, hydroxychloroquine (HCQ), to the conventional treatment has shown the potential to prevent obstetric complications. Herein, we report a case in which the addition of HCQ improved adverse pregnancy outcomes in a pregnant female with systemic lupus erythematosus and aspirin-heparin-resistant APS.
Aspirin Exerts Its Antitumor Effect in Esophageal Squamous Cell Carcinoma by Downregulating the Expression of ATAD2 and KIF4A
Authors: Zhang Man , Ren Zhenzhen , Wang Xianzeng , Liu Cong , Zheng Zhaoyang , Zhao Junwei , Liu Hongchun
Published: August 2022

Objective. To investigate the expression of ATPase family AAA domain-containing protein 2 (ATAD2) and kinesin family member 4A (KIF4A) in esophageal squamous cell carcinoma (ESCC) tissues and their association with clinicopathological features and to explore the role of ATAD2 in regulating KIF4A expression and biological functions in ESCC cells and the effect of aspirin on their expression. Methods. The mRNA and protein expression of ATAD2 and KIF4A in the tissues of patients with ESCC were measured by RT-qPCR and immunohistochemistry, and the correlation between the expression of mRNA and clinicopathological characteristics was analyzed. Western blot and RT-qPCR were used to detect the interference efficiency and KIF4A expression after si-ATAD2 transfection in EC109 and KYSE30 cells. CCK-8 and Transwell assay were performed to investigate the effects of ATAD2 and aspirin on proliferation, migration, and invasion of ESCC cells. The effect of aspirin on the expression of ATAD2 and KIF4A in ESCC cells was measured by RT-qPCR and Western blot. Results. The expression of ATAD2 and KIF4A was upregulated in ESCC tissues, and both were correlated with the differentiation grades and lymph node metastasis. Knockdown of ATAD2 in ESCC cells significantly inhibited cell proliferation, migration, and invasion. Compared to the negative control group, the proliferation, migration, and invasion ability of ESCC cells in the aspirin-treated groups were decreased, and the expression of ATAD2 and KIF4A in ESCC cells was decreased after treating with aspirin for 48 h. Conclusion. The expression levels of ATAD2 and KIF4A are elevated in ESCC. ATAD2 promotes proliferation, migration, and invasion of ESCC cells by regulating KIF4A. Aspirin can inhibit the malignant behavior of ESCC cells by downregulating ATAD2 and KIF4A.
Correlation between Pregnancy Outcome and Placental Pathology in COVID-19 Pregnant Women
Authors: Al-Rawaf Sara A. , Mousa Enas T. , Kareem Noora M.
Published: August 2022

Background. Vertical transmission of several viruses during pregnancy has been shown to cause adverse fetal outcomes. The question about the possibility of a similar outcome in association with SARS-CoV-2 has been raised in recently published articles. Indeed, the rate of transmission through the placenta to the fetus reported in women with COVID-19 has been shown to form a minority. The aim of this study was to explore the possible histopathological changes in the placenta of pregnant women with COVID-19 after delivery and those changes in the umbilical cord. Methods. A case-control study including a total of 50 full-term pregnant women with COVID-19 and 60 control pregnant females. Histopathological evaluation of placental tissues and umbilical cords were reported. Results. The main findings in the umbilical cord were increased thickness of vessels, thrombus formation, endothelins, and narrow lumen; except for the increased thickness of blood vessels, these findings were more frequently seen in women with COVID-19, in comparison with control women in a significant manner (

p
<
0.05

). Increased thickness of blood vessels was more significantly observed in the control group compared to the COVID-19 group (

p
<
0.01

). Findings of the placenta included avascular villi, fibrin, thrombosis, and meconium macrophage in various combinations. Except for fibrin as the sole findings, all other findings including combinations were more frequently encountered in the study group in comparison to the control group (

p
<
0.05

). Conclusion. Pregnant women with COVID-19 have significant pathological alterations in the placenta and umbilical cord. These findings reflect the capability of SARS-CoV-2 in causing immunological reactions to the placenta, either directly or indirectly, and these pathologies may be linked to the higher rate of adverse neonatal outcomes and maternal admission to the intensive care unit.
Image Segmentation Algorithm of Colorimetric Sensor Array Based on Fuzzy C-Means Clustering
Authors: Bo Yu , Shi Peng , Wang Can , Qin Fang , Wei Huimei
Published: August 2022

In the real world, the boundaries between many objective things are often fuzzy. When classifying things, they are accompanied by ambiguity, which leads to fuzzy cluster analysis. The most typical in fuzzy clustering analysis is the fuzzy C-means clustering algorithm. The fuzzy C-means clustering algorithm obtains the membership degree of each sample point to all the class centers by optimizing the objective function, so as to determine the category of the sample point to achieve the purpose of automatically classifying the sample data. Based on fuzzy C-means clustering, this paper analyzes the image segmentation algorithm of the chroma sensor array. The fuzzy C-means (FCM) algorithm for colorimetric sensor array image segmentation is an unsupervised fuzzy clustering and recalibration process, which is suitable for the existence of blur and uncertainty in colorimetric sensor array images. However, this algorithm has inherent defects; that is, it does not combine the characteristics of the current colorimetric sensor array diversity and instability, does not consider the spatial information of the pixels, and only uses the grayscale information of the image, making it effective for noise. The image segmentation effect is not ideal. Therefore, this paper proposes a new colorimetric sensor array image segmentation algorithm based on fuzzy C-means clustering. Through the image segmentation effect test, the image segmentation algorithm proposed in this paper demonstrates an overall optimal segmentation accuracy of 96.62% in all array point image segmentation, which can effectively and accurately achieve the target extraction of colorimetric sensor array images.
Research on the Application of Public Art Design Based on Digital Technology and Multisensor Fusion Technology in Urban Landscape Construction
Authors: Hu Dong , Hao Xintian , Liu Chao
Published: August 2022

During the more than 100 years of modern urban landscape design and construction, in addition to the continuous deductive changes of design thinking, concepts, methods and means, the construction concept and design thinking have also changed for a long time. In different periods, the degree of involvement and the role and the function of the public art involved in the landscape are different, and the applied technology is always changing. This paper emphatically analyzes the role of public art design of digital technology and multisensor fusion technology in the construction of urban landscape in China in recent years, and reveals that after the digital technology and multisensor fusion technology art design are involved in the construction of urban space landscape, they have shifted from the important power of shaping urban space aesthetics to the development of ecological aesthetics. At the same time, in this process, public art and urban design have developed to a new stage, that is, from the physical space to the construction of the spiritual field, which has become the most vivid visual text of today’s urban landscape, so as to further understand the significance and value of art in urban space.
Machine Learning-Based Classification and Evaluation of Regional Ethnic Traditional Sports Tourism Resources
Authors: Li Cuihan , Lyu Shaojun
Published: August 2022

Traditional sports in ethnic minority regions are a valuable cultural heritage. Regional ethnic traditional sports are not only a sports business but also a tourism resource. The construction of a reasonable regional sports tourism resource classification model is fundamental to the development of sports tourism resources. However, the existing sports tourism resources classification is mostly constructed manually based on the national standard tourism resources classification system. The efficiency and accuracy of the traditional manual classification are poor and cannot reflect the characteristics of regional ethnic traditional sports tourism. In order to solve the above problems, a machine learning-based classification method for regional ethnic traditional sports tourism resources is proposed. Firstly, the relevant concepts and characteristics of traditional sports tourism resources are introduced. Then, taking the development of traditional sports of ethnic minorities in Yunnan Province as the research object, SWOT analysis, literature, interview, questionnaire, and mathematical statistics are used to investigate and analyse the overall status of the development of regional ethnic traditional sports. Secondly, a classification evaluation method based on an optimised back-propagation (BP) neural network is proposed. Finally, the optimised BP neural network model is applied to the classification of traditional sports tourism resources. The experimental results show that the optimised BP model performs well in the classification of traditional sports tourism resources, verifying its effectiveness.
Expressway Lane Change in Fog Environment by Dynamic Strategic Game
Authors: Zhang Xuguang , Gao Jianping , Liao Li , Wu Guoxiong
Published: August 2022

To study the behavior of expressway driver’s lane change in a foggy environment, the driver’s vision is adopted as the index for assessing the lane change behavior. The normalization theory is introduced to analyze the driver’s intention of changing lanes. Using the statistical examination results of the driver’s gaze zones, this paper has analyzed the gaze and glance features of the driver during the change of lanes. Based on the driving condition of changing lanes in foggy environment, the game theory is adopted in the study to elaborate on the strategy equilibrium of the driver’s decision-making in the foggy environment. Moreover, a model is established to analyze the driver’s lane changes under the foggy environment. Through the calibration of parameters and verification of the model, the reliability of the model has been proved. The research findings indicate that the features of the driver’s gaze and glance under circumstances of the fine weather differ from those in the foggy environment, thus laying a theoretical foundation for the safety management of subsequent lane changes in the expressway in foggy environment.
Nonisolated DC to DC Converters for High-Voltage Gain Applications Using the MPPT Approach
Authors: Reddy C. Subba Rami , Narukullapati Bharath Kumar , Uma Maheswara Rao M. , Ravindra Sangu , Venkatesh P. M. , Anil Kumar T. Ch. , Chandra B. Mouli , Berhanu Afework Aemro
Published: August 2022

It is proved and examined in this research paper that the perturb and observe (P and O) technique for isolating the photovoltaic array (PVA) from the power structure may be used to isolate the PV array from the power structure. A single specified voltage setup can remove the maximum power from a PV cell because of the nonlinear properties of the PV cell’s output. As a result, in PVA, the maximum power point tracking (MPPT) algorithm is utilized to increase the yield control range by increasing the maximum power point. In this study, the MPPT computations are carried out with the assistance of a DC-DC boost converter for usage in applications needing high voltage gain at a variety of sun-positioned irradiances and cell temperatures, as demonstrated in the literature.