bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of thermal radiation on two-dimensional unsteady third-grade fluid flow over a permeable stretching Riga plate
Authors: Nadeem Sohail , Ishtiaq Bushra , Abbas Nadeem
Published: September 2022

In many fields, there are various applications of non-Newtonian fluids. Various complicated fluids (polymer melts, clay coatings and oil) belong to the category of non-Newtonian fluids. The third-grade fluid is one of the most important non-Newtonian fluid models. This paper has the primary object of heat transfer mechanism and boundary layer third-grade fluid flow under the effects of thermal radiation. The time-dependent two-dimensional flow is considered to flow above a permeable stretchable vertical Riga plate. For numerical solutions, the setup of ordinary differential equations (ODEs) is acquired by converting nonlinear governing equations through relevant similarity transformations. The nonlinear setup of ODEs is numerically solved with the aid of a suitable software such as MATLAB via its bvp4c technology. Graphs are sketched to discuss the various flow parameters’ significance for the expression of velocity and temperature fields. Tabulated values of surface drag force and heat transfer rate corresponding to the numerous pertinent parameters are described. The current analysis of the concerned flow mechanism concludes that the fluid parameters descend the temperature distribution but amplify the profile of the fluid velocity. The radiation parameter escalates the temperature field.
Fixed-time stochastic synchronization of Kuramoto oscillators subjected to noisy diffusion process
Authors: Wu Jie , Liu Mingqi , Ma Ru-ru , Wang Xiaofeng
Published: September 2022

This investigation concerns the phase agreement (PA) for identical Kuramoto oscillators and frequency synchronization (FS) for nonidentical oscillators within fixed-time (FxT) in noisy environments. Comparing with the conventional FxT controllers, this investigation picks the smooth control protocol for stripping the signum function out, which could improve the synchronizing performance of networked systems. Furthermore, due to the ubiquity of stochastic perturbation, this investigation takes the noise into consideration. Additionally, the synchronizing criteria are deduced for realizing the FxT phase-FS in probability, and meanwhile the time estimation (TE) of realizing synchronization is given. At last, simulation results not only validate the correction of theoretical analysis, but also indicate that PA is more robust to stochastic disturbance, whereas FS of oscillators is relatively sensitive.
Computing the Wide-Band Earth Return Impedance of Submarine Cable Considering Complex Paths
Authors: Shen Shuyi , He Dong , He Yingjing , Li Fan , Zhu Keping , Pan Zhuohong
Published: September 2022

Because of the various earth return pathways in practical projects, calculating the wide-band earth return impedance of submarine cable is problematic. Using the dynamic Hertz magnetic potential, the formula for estimating the earth return impedance has been obtained based on the three-layer structure of air, sea water, and seafloor. Furthermore, in the frequency range of 50 [Formula: see text] 10 MHz, the enhanced complex image approach and numerical integration method have been developed to compute the earth return impedance. The suggested iterative complex image method outperforms the classic complex image method in terms of accuracy and computation speed, with a global fitting error of less than 10−6. Finally, the impact of various models on the real and imaginary parts of submarine cable wide-band earth return impedance has been investigated. The results demonstrate that the actual component has a more complicated variation, whereas the imaginary part is clearly affected by the skin effect, with a bigger difference in the high-frequency range. The method presented in this research can be utilized as a supplement to existing methods, as well as a reference methodology for analyzing the influence of over-voltage on submarine cable engineering.
MODELING THE DYNAMICS OF COVID-19 TRANSMISSION IN INDIA: SOCIAL DISTANCING, REGIONAL SPREAD AND HEALTHCARE CAPACITY
Authors: NAG SURYADEEPTO , CHAKRABARTY SIDDHARTHA P.
Published: September 2022

In the new paradigm of health-centric governance, policymakers are in constant need of appropriate metrics to determine suitable policies in a non-arbitrary fashion. To this end, in this paper, a compartmentalized model for the transmission of COVID-19 is developed, with a socially distanced compartment added to the model. The modification allows for administrators to quantify the extent to which voluntary social distancing norms are followed, and address restrictions accordingly. Modifications are also made to incorporate inter-region migration, and suitable metrics are proposed to quantify the impact of migration on the rise of cases. The healthcare capacity is modeled and a method is developed to study the consequences of the saturation of the healthcare system. The model and related measures are used to study the nature of the transmission and spread of COVID-19 in India, and appropriate insights are drawn.
A Network Analysis of Return Connectedness in Financial Stability: Insights into Disease and Economic Policy Uncertainties
Authors: Bui Hung Quang , Phan Nguyet Thi Bich , Huynh Anh Ngoc Quang , Huynh Toan Luu Duc
Published: September 2022

This paper studies how return connectedness exhibits potential linkages among 17 economies over a 20-year period starting in 2001. We obtained three main results by employing the dynamic connectedness approach, which is based on vector auto-regression (VAR), to calculate generalized forecast error decompositions. First, although the financial crisis (2007–2008) experienced a high level of connectedness, the spillover index spiked during the early stages of the COVID-19 outbreak. Second, the “return shock sender” is a community of countries that includes the United States, Australia, and European countries, while Vietnam is immune to financial linkages. Third, we discovered the predictive power of U.S. economic policy uncertainty and disease fear with market volatility for the Vietnamese return connectedness. As a result, our research identifies a range of relevant policies to mitigate spillover risks in the context of financial stability.
Does the Indian economy progress toward a cashless economy?
Authors: Nasira Banu M , Cholakkal Ibrahim
Published: September 2022

The cashless payment system has been considered a critical driver for the country’s economic growth and development. Recently, financial institutions and services have undergone drastic changes due to rapid digitalization. Over the last 10 years, substantial progress has been made toward transforming India into an inclusive digital financial system, cost-effective, convenient, transparent, accountable and secure, bringing underserved and excluded rural people into the economic mainstream. All these efforts led to the transformation of India into a greater cashless economy. Despite the rapid expansion of digital financial transactions in India, cash still accounts for a significant share of payment. India observed a simultaneous growth of the cash economy and digital payments, which created a paradoxical situation. This situation twisted a lesson for policymakers who imagined all cash was shady and that digital must necessarily dent the cash economy. This research paper analyzes the trends and progress of digital payment for transactions in India and the developments to make India a cashless economy. It also examines whether a long-promised cashless society will become a reality, or will currency continue to play a critical role in everyday transactions, as it had for decades?
Novel approach to predicting the spatial distribution of the hydraulic conductivity of a rock mass using convolutional neural networks
Authors: He Mingming , Zhou Jiapei , Li Panfeng , Yang Beibei , Wang Haoteng , Wang Jing
Published: September 2022

Characterizing the spatial distributions of the hydraulic conductivity of a rock mass is important in geoscience and engineering disciplines. In this paper, the architecture of convolutional neural networks (CNNs) is proposed to predict the spatial distributions of hydraulic conductivity based on limited geological factors. The performance of the CNN model is evaluated using the new data of hydraulic conductivity. A comparative study with the empirical method is performed to validate the reliability of the CNN model. The effect of weathering and unloading on the spatial distributions of hydraulic conductivity is studied using the CNN model. The results show that the hydraulic conductivity predicted by the CNN model is within an error range of 5% compared to Lugeon borehole tests. The predictive accuracy of the CNN method is higher than the estimations made using empirical relationships. The spatial distributions of hydraulic conductivity v. depth can be divided into three stages. In the first stage, the hydraulic conductivity is slightly reduced with increasing depth. Increasing in the depth range of 300–600 m (second stage), the hydraulic conductivity is slightly reduced as a function of the lower degree of weathering. In the final stage, the hydraulic conductivity is not changed by weathering and values gradually converge to a constant as depth increases.
Binary Representation via Jointly Personalized Sparse Hashing
Authors: Wang Xiaoqin , Chen Chen , Lan Rushi , Liu Licheng , Liu Zhenbing , Zhou Huiyu , Luo Xiaonan
Published: September 2022

Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode high-dimensional features in the Hamming space with similarity preservation between instances. However, most existing methods learn hash functions in manifold-based approaches. Those methods capture the local geometric structures (i.e., pairwise relationships) of data, and lack satisfactory performance in dealing with real-world scenarios that produce similar features (e.g., color and shape) with different semantic information. To address this challenge, in this work, we propose an effective unsupervised method, namely, Jointly Personalized Sparse Hashing (JPSH), for binary representation learning. To be specific, first, we propose a novel personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space. In addition, we deploy sparse constraints for different personalized subspaces to select important features. We also collect the strengths of the other clusters to build the PSH module with avoiding over-fitting. Then, to simultaneously preserve semantic and pairwise similarities in our proposed JPSH, we incorporate the proposed PSH and manifold-based hash learning into the seamless formulation. As such, JPSH not only distinguishes the instances from different clusters but also preserves local neighborhood structures within the cluster. Finally, an alternating optimization algorithm is adopted to iteratively capture analytical solutions of the JPSH model. We apply the proposed representation learning algorithm JPSH to the similarity search task. Extensive experiments on four benchmark datasets verify that the proposed JPSH outperforms several state-of-the-art unsupervised hashing algorithms.
Deciding Robustness for Lower SQL Isolation Levels
Authors: Ketsman Bas , Koch Christoph , Neven Frank , Vandevoort Brecht
Published: September 2022

While serializability always guarantees application correctness, lower isolation levels can be chosen to improve transaction throughput at the risk of introducing certain anomalies. A set of transactions is robust against a given isolation level if every possible interleaving of the transactions under the specified isolation level is serializable. Robustness therefore always guarantees application correctness with the performance benefit of the lower isolation level. While the robustness problem has received considerable attention in the literature, only sufficient conditions have been obtained. The most notable exception is the seminal work by Fekete where he obtained a characterization for deciding robustness against SNAPSHOT ISOLATION. In this article, we address the robustness problem for the lower SQL isolation levels READ UNCOMMITTED and READ COMMITTED, which are defined in terms of the forbidden dirty write and dirty read patterns. The first main contribution of this article is that we characterize robustness against both isolation levels in terms of the absence of counter-example schedules of a specific form (split and multi-split schedules) and by the absence of cycles in interference graphs that satisfy various properties. A critical difference with Fekete’s work, is that the properties of cycles obtained in this article have to take the relative ordering of operations within transactions into account as READ UNCOMMITTED and READ COMMITTED do not satisfy the atomic visibility requirement. A particular consequence is that the latter renders the robustness problem against READ COMMITTED coNP-complete. The second main contribution of this article is the coNP-hardness proof. For READ UNCOMMITTED, we obtain LOGSPACE-completeness.
The Immunobiology and Pathogenesis of Celiac Disease
Authors: Iversen Rasmus , Sollid Ludvig M.
Published: September 2022

Among human leukocyte antigen (HLA)-associated disorders, celiac disease has an immunopathogenesis that is particularly well understood. The condition is characterized by hypersensitivity to cereal gluten proteins, and the disease lesion is localized in the gut. Still, the diagnosis can be made by detection of highly disease-specific autoantibodies to transglutaminase 2 in the blood. We now have mechanistic insights into how the disease-predisposing HLA-DQ molecules, via presentation of posttranslationally modified gluten peptides, are connected to the generation of these autoantibodies. This review presents our current understanding of the immunobiology of this common disorder that is positioned in the border zone between food hypersensitivity and autoimmunity.
Corrosion behavior of Cu in accelerated hydrogen peroxide-based disinfectants
Authors: Nakhaie Davood , Clifford Amanda M. , Asselin Edouard
Published: September 2022

Cu ion release due to corrosion plays a critical role in the antimicrobial efficacy of Cu. There are, however, concerns regarding surface passivation, and thus, decreased Cu ion release after routine cleaning with hospital-grade disinfectants. In this study, immersion corrosion rate measurements up to 7 days coupled with SEM and XPS were employed to evaluate the corrosion behavior of pure Cu in 0.5% H2O2 and two commercially available hospital-grade H2O2-based disinfectants. While passivation of Cu occurs in H2O2 alone, results show high corrosion rates in commercial disinfectants, meaning that Cu ion release occurs readily during cleaning with these agents.
The Prognostic Value of Domain-Specific Cognitive Abilities Assessed by Chinese Version of Oxford Cognitive Screen on Determining ADLs Recovery in Patients with Post-Stroke Cognitive Impairment
Authors: Lin Miaoran , Ren Jinxin , Wu Jingsong , Huang Jia , Tao Jing , Chen Lidian , Liu Zhizhen
Published: September 2022

Background. Poststroke cognitive impairment (PSCI) has been increasingly recognized in patients. However, it remains unclear whether ADLs recovery is more susceptible to domain-specific cognitive abilities after a stroke. Therefore, the study was designed to investigate the cognitive functions of patients with PSCI at admission by using the Chinese (Putonghua) Version of the Oxford Cognitive Screen (OCS-P) as well as to identify the prognostic value of domain-specific cognitive abilities on the recovery of ADLs when discharged. Methods. A total of 153 hospitalized stroke patients were included in this prospective study. Cognitive function was assessed by OCS-P when participants were admitted to the hospital. The ADLs were measured at admission and discharge, and recovery was estimated by the improvement between admission and discharge. A diagnostic model using logistic regression was constructed to identify the prognostic value of domain-specific cognitive abilities for ADLs. The efficacy and accuracy of the diagnostic model were assessed by receiver operating characteristic (ROC) and Hosmer-Lemeshow’s goodness of fit test. The diagnostic model was validated by 10-fold cross-validation and presented as a nomogram. Results. The score of OCS-P was 60(49.75, 69). The most frequently impaired cognitive domain was number writing (60.8%), followed by verbal memory (52.9%). Multivariate logistic regression showed executive dysfunction was a risk prognostic factor of ADLs recovery (

P
<
0.001

, OR = 3.176 [95% CI, 1.218∼8.278]). The ROC curve of the diagnostic model was 0.839, with a good diagnostic efficacy. Hosmer–Lemeshow test showed diagnostic model had good calibration ability (χ2 = 8.939.3,

P
=
0.347
>
0.05

). The average error rate after adjustment of 10-fold cross-validation was 20.93%, within the acceptable range. Conclusions. Post-stroke patients generally suffered from multidimensional cognitive impairments. Executive dysfunction screened with OCS-P at clinical admission was a reliable and accessible predictive factor ADLs recovery in patients with PSCI. Early targeted rehabilitation programs are suggested to make them as earlier as possible, especially for those having executive dysfunction while hospitalized.
[Retracted] Application of Uncertainty Thought Environment in Judicial Adjudication Based on Cognitive Psychology
Authors: Li Yongchao
Published: September 2022

The uncertainty of judicial decision‐making has a deep and extensive theoretical foundation. Theoretical analysis starts with a reflection on legal rationalism that challenges the legal certainty before delving deeply into the case’s facts and the entire legal system. In light of this, this paper explores a novel approach to enhance the reasoning mechanism of trial documents from the viewpoint of modern cognitive psychology, concentrating on the parties’ and the public’s cognitive processes to justice. It is suggested to use an inert hierarchical multilabel classification algorithm. In order to predict the category of invisible examples, the extended multilabel training set is first searched for adjacent samples of invisible examples, and the classification weight and confidence of each category are then determined in accordance with these adjacent samples. The group of invisible examples is then anticipated. Experimental comparison demonstrates that this algorithm outperforms other prediction techniques; the macro accuracy, macro recall, and macro F1 of this method are, respectively, 0.896, 0.871, and 0.814. It has some advantages in many multilabel evaluation indexes when compared to other multilabel algorithms.
Influence Mechanism of Marketization Process and Financial Technology Development on Enterprise Investment Preference Based on Threshold Model
Authors: Li Yongkui , Liu Xiaokang
Published: September 2022

This paper explores the influence mechanism of the marketization process and financial technology on corporate investment preference using panel data from 2013 to 2019 for China’s A‐share listed companies. The study finds that both the marketization process and FinTech can facilitate increased R&D innovation and foreign equity investment by companies but discourage investment in their fixed assets, and FinTech has a “U‐shaped effect” on corporate R&D and innovation. At the same time, the marketization process and FinTech show different threshold effects on corporate investment preferences with government subsidies as the threshold variable. It is found that market‐based processes in small‐scale enterprises are better able to promote investment in innovative R&D by companies—the marketization process for large enterprises has a greater impact on outward equity investment in the heterogeneity analysis. From the perspective of the nature of property rights, it is found that the marketization process and FinTech have a greater impact on non‐state enterprises. As a result, in an era of rapid FinTech development, this paper enriches the relevant research on the impact of the marketization process on corporate investment preferences, which has important practical significance for policy formulation and corporate future development.
Multiple Mechanistic Action of Brevinin-1FL Peptide against Oxidative Stress Effects in an Acute Inflammatory Model of Carrageenan-Induced Damage
Authors: Chai Jinwei , Liu Junfang , Tian Maolin , Liao Hang , Wu Jiena , Xie Jianpeng , Lai Shian , Mo Guoxiang , Chen Xin , Xu Xueqing
Published: September 2022

Amphibian skin is acknowledged to contain an antioxidant system composed of various gene-encoded antioxidant peptides, which exert significant effects on host defense. Nevertheless, recognition of such peptides is in its infancy so far. Here, we reported the antioxidant properties and underlying mechanism of a new antioxidant peptide, brevinin-1FL, identified from Fejervarya limnocharis frog skin. The cDNA sequence encoding brevinin-1FL was successfully cloned from the total cDNA of F. limnocharis and showed to contain 222 bp. The deduced mature peptide sequence of brevinin-1FL was FWERCSRWLLN. Functional analysis revealed that brevinin-1FL could concentration-dependently scavenge ABTS+, DPPH, NO, and hydroxyl radicals and alleviate iron oxidation. Besides, brevinin-1FL was found to show neuroprotective activity by reducing contents of MDA and ROS plus mitochondrial membrane potential, increasing endogenous antioxidant enzyme activity, and suppressing H2O2-induced death, apoptosis, and cycle arrest in PC12 cells which were associated with its regulation of AKT/MAPK/NF-κB signal pathways. Moreover, brevinin-1FL relieved paw edema, decreased the levels of TNF-α, IL-1β, IL-6, MPO, and malondialdehyde (MDA), and restored catalase (CAT) and superoxide dismutase (SOD) activity plus glutathione (GSH) contents in the mouse injected by carrageenan. Together, these findings indicate that brevinin-1FL as an antioxidant has potent therapeutic potential for the diseases induced by oxidative damage. Meanwhile, this study will help us further comprehend the biological functions of amphibian skin and the mechanism by which antioxidants protect cells from oxidative stress.
[Retracted] Effectiveness Assessment of College Ideological and Political Courses Using BP Neural Networks in Network Environment
Authors: Huang Chudiao , Zhou Xianglian
Published: September 2022

We will not be able to provide educators with the assistance they require to implement the network IPECU and boost the effectiveness of the network IPE until we have established a trustworthy and effective assessment system. By first identifying the problems with the current ideological course assessment system, this study builds an indicator system for evaluating IPECU’s effectiveness. An evaluation model of IPECU effectiveness based on the BPNN is designed and built using this as the basis. In this research, GA with adaptive mutation is used to optimise the initial weights and thresholds of the BPNN. As a result, the training termination conditions are satisfied by the weights and thresholds for the BPNN’s assessment of the instructional quality more quickly, increasing the prediction accuracy and convergence speed. The simulation and comparative analyses presented in this study use MATLAB to verify the effectiveness of the method’s evaluation. Experiments show that this algorithm’s prediction accuracy can be as high as 95%, which is comparable to GA and the traditional BPNN algorithm. This method could provide some technical assistance for the network IPECU validity assessment.
Low-Dose Apatinib Improves the Prognosis of Patients with Recurrent High-Grade Gliomas
Authors: Zhang Ming , Gao Liying , Liu Xiaofeng , Dong Fang , Su Qun , Zhang Yanping , Li Fengju , Wang Hulan , Han Pengbing
Published: September 2022

Objective. To evaluate the efficacy, safety, and prognostic value of low-dose apatinib in combination with temozolomide in the treatment of primary or recurrent high-grade gliomas (HGGs). Methods. A retrospective analysis of patients with postoperative and recurrent HGGs treated in our hospital from April 1, 2018, to April 30, 2020. Patients should be treated by combination therapy (surgery + radiotherapy + chemotherapy). Patients who received apatinib combined with temozolomide chemotherapy were allocated to the research group (RG), while patients who received temozolomide chemotherapy alone were allocated to the control group (CG). The efficacy and toxic side effects were compared between the two groups. Results. There were 67 qualified patients retrieved, including 37 cases in the RG and 30 cases in the CG. There were no significant differences in objective remission rate (ORR) or disease control rate (DCR) between the control group and the study group (

P
>
0.05

). However, the overall improvement of clinical efficacy in the observation group was better than that in the control group (

P
<
0.05

). There was no significant difference in the incidence of adverse effects between the two groups (

P
>
0.05

). There were no significant differences in overall survival (OS) or progression-free survival (PFS) between the two groups (

P
>
0.05

). Conclusion. Low-dose apatinib combined with temozolomide and radiotherapy for HGGs is effective in improving efficacy, relieving brain edema, reducing the use of glucocorticoid drugs, and improving patients’ quality of life. It has mild adverse effects and is well tolerated by patients.
Feature Extraction Method Based on Sparse Autoencoder for Air Traffic Management System Security Situation Awareness
Authors: Wu Zhijun , Bai Zhuoning , Zhang Lizhe , Wang Kenian
Published: September 2022

In wide-area distributed scenarios, it is particularly important to carry out information security situational awareness for the air traffic management (ATM) system with integrated air-ground structure. The operation data of the communication, navigation and surveillance (CNS) equipment of ATM system have the characteristics of multi-dimension, complexity, and strong correlation. In the process of situation awareness feature extraction, there are problems such as poor model accuracy, weak feature expression ability, and low classification performance. A feature association algorithm is designed to solve the above problems. Based on this algorithm, a deep-related sparse autoencoder (DRSAE) model based on improved sparse autoencoder is established. In DRSAE model, L1 regularization and Kullback–Leibler divergence (KLD) sparsity terms are used to penalize the parameters of the encoder network, and the quantity of hidden layers is increased to allow the model to optimize the global encoder network by iteratively training a single encoder. Moreover, the proposed DRSAE model and other feature extraction models such as principal component analysis (PCA), autoencoder (AE), and sparse autoencoder (SAE) are compared and evaluated by using the support vector machine (SVM) classifier. Compared with other feature extraction models, it is found that the proposed DRSAE model has good robustness in feature extraction of ATM system, and the obtained features have strong expression ability, which enhances the classification performance of the model and is convenient for situation awareness.
The Role of NLRP3 Inflammasome in Diabetic Cardiomyopathy and Its Therapeutic Implications
Authors: Ding Kai , Song Chao , Hu Hengjing , Yin Kai , Huang Hong , Tang Huifang
Published: September 2022

Diabetic cardiomyopathy (DCM) is a serious complication of diabetes mellitus (DM). However, the precise molecular mechanisms remain largely unclear, and it is still a challenging disease to diagnose and treat. The nucleotide-binding oligomerization domain and leucine-rich repeat pyrin 3 domain (NLRP3) inflammasome is a critical part of the innate immune system in the host to defend against endogenous danger and pathogenic microbial infections. Dysregulated NLRP3 inflammasome activation results in the overproduction of cytokines, primarily IL-1β and IL-18, and eventually, inflammatory cell death-pyroptosis. A series of studies have indicated that NLRP3 inflammasome activation participates in the development of DCM, and that corresponding interventions could mitigate disease progression. Accordingly, this narrative review is aimed at briefly summarizing the cell-specific role of the NLRP3 inflammasome in DCM and provides novel insights into developing DCM therapeutic strategies targeting the NLRP3 inflammasome.
An Unknown Protocol Identification Method for Industrial Internet
Authors: Zhu Xinghui , Jiang Ziheng , Zhang Qiyuan , Zhao Shuangrui , Zhang Zhiwei
Published: September 2022

This paper focuses on the problem of protocol identification in the industrial internet and proposes an unknown protocol identification method. We first establish an industrial internet protocol detection model to classify known protocols, unknown protocols, and interference signals and then store the unknown protocols for manual analysis. Based on the Eps-neighborhood idea, we further develop an Eps-neighborhood hit algorithm and propose an identification method to identify unknown protocols, where the supervised learning classification of unknown protocol detection is realized. Finally, extensive experimental results are provided to illustrate our theoretical findings. It indicates that the proposed method has an average screening accuracy of 94.675% and 95.159% for unknown protocols encoded in binary and ASCII, respectively, while the average screening accuracy of known protocols in binary and ASCII encoding is 94.242% and 94.075%.
Design of Image Acquisition System Based on Visual Intelligent Sensor and Video Signal Processing
Authors: Zhou Fei
Published: September 2022

With the rapid development and iteration of CMOS image sensor and other related technologies, in addition to the more advanced technology, the corresponding sensor also integrates the image signal processing system, so it is a leap for the image acquisition system. Based on this, this paper will take the visual intelligent sensor produced and developed by a semiconductor company as the hardware to complete the hardware design of the image acquisition and transmission system. In addition to the core processor of the system, this paper will also use audio and video processing chips and image data processing chips for collaborative analysis, so as to ensure the processing capacity and processing quality of the image acquisition system at the hardware design level. Compared with the traditional system, at the corresponding system software level, in order to further improve the quality of video signal processing, this paper creatively proposes a switch weighted median filter denoising algorithm, which has a significant impact on the processing of random impulse noise in video and the overall image filtering. At the same time, the algorithm also protects the edge details of the video image, thus improving the authenticity of the image and video. Based on this, this paper has built a verification platform based on supporting hardware and corresponding algorithms. Through hardware debugging and system operation analysis, when the operating frequency of the visual intelligent sensor is 100 MHz, the set acquisition depth is 10 bits, and the corresponding system collects data at a rate of 251 fps per second. In terms of the corresponding performance, the iteration number of the algorithm proposed in this paper is about 40% more than that of the traditional algorithm in unit time. In terms of the corresponding algorithm performance, the algorithm proposed in this paper is about 30% higher than the traditional algorithm. In terms of the efficiency of the corresponding image and video acquisition system, the algorithm proposed in this paper is about 44% higher than the traditional algorithm. Based on this, the system proposed in this paper has obvious advantages over the traditional algorithm.
DSC2 Suppresses the Metastasis of Gastric Cancer through Inhibiting the BRD4/Snail Signaling Pathway and the Transcriptional Activity of β-Catenin
Authors: Sun Chao , Wang Lei , Du Dan-dan , Ji Jian-bo , Yang Xiao-xia , Yu Bing-fang , Shang Peng-fei , Guo Xiu-Li
Published: September 2022

Downregulated DSC2 involved in the metastasis of cancers. Unfortunately, its role on the development of gastric cancer (GC) and the potential mechanisms remain unclear. Bioinformatics analysis, Western blot, qRT-PCR, and immunohistochemistry were performed to detect the DSC2 levels of human GC and normal stomach tissues. The role of DSC2 and the downstream signaling in gastric carcinogenesis were explored by using GC specimens, GC cells with different DSC2 expression, inhibitors, and mouse metastasis models. We found that the level of DSC2 decreased significantly in GC tissues and cells. Recovered DSC2 inhibited the invasion and migration of GC cells both in culture and in xenografts. Mechanistically, DSC2 could not only decrease Snail level and nuclear BRD4 level by forming DSC2/BRD4, but also inhibit nuclear translocation of β-catenin. We concluded that DSC2 inhibited the metastasis of GC, and the underlying mechanisms were closely related to the regulation on nuclear translocation of BRD4 and β-catenin. Our results suggest that DSC2 may serve as a novel therapeutic target for GC.
[Retracted] Challenges and Optimization Paths of Guzheng Professional Education in Colleges under Big Data Era
Authors: Cheng Li , Hu Liang
Published: September 2022

As a treasure among Chinese national musical instruments, guzheng is an important part of traditional Chinese music. As the art of national music goes to the world, the art of guzheng has been widely promoted. As the best form to carry forward the art of guzheng, the teaching of guzheng majors in colleges is significant in teaching and continuously improves guzheng art accomplishment. Oral teaching and step‐by‐step music theory and technique teaching are typical ways of teaching musical instrument performance in colleges. However, under big data, Chinese education is undergoing a profound change, and the combination of big data and education has become a new contemporary education method. This work studies the guzheng professional education in colleges under big data. First, this work aims at the existing outstanding issues of guzheng teaching in colleges and studies the challenges and optimization paths of guzheng professional education in colleges under big data. Second, this work proposes a multiscale residual attention fusion network (MSRAFNET) to evaluate the teaching quality of guzheng majors in colleges in the era of big data. The feature extraction of the network model is mainly completed by the residual module, which is composed of several multiscale residual learning units. Adding an attention mechanism to the multiscale residual learning unit can enhance the feature extraction of key information by the network and reduce the interference of redundant information, which is more conducive to the learning of data features. It adopts the design of GAP and Dropout to reduce spatial parameters in network training, and the effect of antioverfitting is better. Third, this work systematically evaluates the optimization path of Guzheng education and MSRAFNET, and the systematic experiments verify the superiority of the designed method.
Recurrence and Chaos of Local Dendrite Maps
Authors: Hattab Hawete
Published: September 2022

Let X be a local dendrite, and let f be a continuous self‐mapping of X. Let E(X) represent the subset of endpoints of X. Let AP(f) denote the subset of almost periodic points of f, R(f) be the subset of recurrent points of f, and P(f) be the subset of periodic points of f. In this work, it is shown that if and only if E(X) is countable. Also, we show that if E(X) is countable, then R(f) = X (respectively, ) if and only if either , and f is a homeomorphism topologically conjugate to an irrational rotation, or P(f) = X (respectively, ). In this setting, we derive that if E(X) is countable, then, on local dendrites , transitivity = chaos.
Multilingual Speech Emotion Research Based on Data Mining
Authors: Yu Yanting
Published: September 2022

In order to improve the effect of speech emotion research, this paper combines data mining technology to construct a speech emotion research model and establishes a state space model of speech emotion risk problem. Moreover, this paper uses fuzzy variables to describe the external uncertain environmental factors and intuitively shows the relationship between each technology, that is, clearly shows how actions and external environmental factors affect their action results, which is helpful in understanding the voice emotional risk problem from the root. In addition, this paper uses the basic knowledge of credibility theory to transform the model and finally transform the model into an optimal control problem. Through the speech emotion simulation experiment, it can be seen that the multilingual speech emotion analysis model based on data mining proposed in this paper has a good effect in speech emotion analysis.
Behavior Data Analysis of English Learners Based on Discrete Dynamic System Modeling Method
Authors: Chen Hongli , Gao Yuzheng
Published: September 2022

With the rapid development of computer science, there are more and more kinds of discrete dynamic systems. Computer integrated system CIMS, network communication database administrator system, and human behavior analysis system are all discrete dynamic systems. At present, many researchers have studied by adding human behavior data to discrete dynamic systems. This paper aims to study the behavior data of English learners by using the discrete dynamic modeling technology of complex systems and the discrete dynamic system modeling method of Petri nets. By adding the behavior data of learners to the discrete dynamic system of fuzzy Petri nets, the system is diagnosed and optimized. The experimental results show that the complex discrete dynamic system in this paper has achieved good experimental results according to the performance indicators selected in theory. Based on the combination of the above technologies and systems, the fuzzy Petri net discrete dynamic system studied in this paper improves the processing speed of English learners' behavior data.
Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm
Authors: Jia Xi’ning , Xiang Chengmi , Wang Jin , Gao Xue , Ye Yunrui
Published: September 2022

In order to solve the problem of optimal control of the sewage treatment process based on a multiobjective evolutionary algorithm, an intelligent optimal control of sewage treatment based on a multiobjective evolutionary algorithm is proposed in this paper. In this paper, the decomposition based multiobjective evolutionary algorithm (MOEA/D) is improved, and it is expected that the uniformly distributed approximate Pareto frontier can be obtained with fewer evolution times. For each new solution generated by the MOEA/D algorithm, the improved algorithm in this paper finds the most suitable subproblem for the new solution from all subproblems and replaces the population in its neighborhood. On the basis of the original subproblem, it carries out secondary optimization to improve the utilization rate of the children and then finds the approximate Pareto frontier in the optimization problem with fewer iterations. The experimental results show that AE, PE, and EC Based on SS–MOEA/D optimal control method are reduced by 6.91%, 1.54%, and 5.58%, respectively. Conclusion. The algorithm significantly reduces the number of steps to find the Pareto frontier, significantly improves the performance of the MOEA/D algorithm, and achieves the optimization goal in the optimization of the sewage treatment process.
Association between Serum Cotinine Levels and Bone Mineral Density: An Analysis of the National Health and Nutrition Examination Survey (NHANES)
Authors: Fang Jian-Guo , Wang Duo-Jun , Yang Hao-Yu , Zhang Hui , Tong Jin-Yu , Lin Zai-Jun
Published: September 2022

Purpose. To investigate the relationship between serum cotinine and lumbar bone mineral density (BMD) among 7905 participants aged 30 years and over. Method. A total of 3945 men and 3960 women from the National Health and Nutrition Examination Survey 2011–2018 were included in this cross-sectional analysis. Independent variable was serum cotinine, which is a biomarker of cigarette exposure. The outcome variable was lumbar BMD. We investigated the associations of serum cotinine levels and lumbar BMD using multivariable linear regression models. Results. Serum cotinine concentration was negatively associated with lumbar BMD after adjustment of relevant covariables (β = −0.039, 95% CI: −0.078 to −0.014,

P
=
0.005

). However, in the subgroup analysis stratified by gender, this negative association remained only in women (β = −0.072, 95% CI: −0.132 to −0.012,

P
=
0.019

). Conclusion. Our study suggested that elevated serum cotinine level correlated with decreased lumbar BMD, especially in women. This finding indicated that reducing cigarette exposure and maintaining serum cotinine at a low level may be beneficial to bone health for adults.
Development of a Clinical Prognostic Model for Metabolism‐Related Genes in Squamous Lung Cancer and Correlation Analysis of Immune Microenvironment
Authors: Zhuang Zifan , Gao Chundi
Published: September 2022

Background. The incidence of squamous lung cancer (LUSC) has substantially increased. Systematic studies of metabolic genomic patterns are fundamental for the treatment and prediction of LUSC. Because cancer metabolism and immune cell metabolism have been studied in depth, metabolism and the state and function of immune cells have become key factors in tumor development. This also indicates that metabolic genes and the tumor immune microenvironment (TME) are crucial in tumor treatment. This study is aimed at dissecting the connection between TME and LUSC digestion‐related qualities. Methods. The information used in this study was obtained from The Cancer Genome Atlas dataset. Metabolism‐related genes in patients with LUSC were screened, and relevant clinical data were collated. Next, genes associated with prognosis were screened using univariate COX regression and LASSO regression analyses. Finally, a timer database study was conducted to analyze the molecular mechanisms of immune cell infiltration of LUSC prognosis‐related metabolic genes at the immune cell level. Results. Nine metabolism‐related genes were identified: ADCY7, ALDH3B1, CHIA, CYP2C18, ENTPD6, GGCT, HPRT1, PLA2G1B, and PTGIS. A clinical prediction model for LUSC based on metabolism‐related genes was constructed. In addition, 22 subpopulations of tumor‐infiltrating immune cells (TIIC) in the TME were analyzed using the CIBERSORT algorithm. Finally, we used the TIMER database to analyze the immune infiltration of LUSC and the relationship between metabolism‐related genes and immune cells. Conclusion. Our study identified metabolic genes associated with the prognosis of LUSC, which are important markers for its diagnosis, clinically relevant assessments, and prognosis. The relationship between metabolic genes with prognostic impact and immune infiltration was also analyzed, and a metabolic gene‐based clinical prediction model was identified, providing a new perspective for LUSC treatment.
Artificial Intelligence and Big Data-Based Injury Risk Assessment System for Sports Training
Authors: Zhang Yuhua
Published: September 2022

Sports training is an important part of daily life, and various injuries are prone to occur during the training process. If they are not dealt promptly, they are bound to affect daily life. Although our nationals are becoming more and more aware of participating in physical exercise, they are performing numerous sports activities at the time of unexpected events, and sports injuries are becoming more and more frequent. To realize the evaluation and automatic prediction of sports training injury risk factors, a sports training injury risk evaluation algorithm using big data analysis is proposed. Establish a training injury risk analysis model, analyze the relevant parameters of training injury risk assessment through statistical and quantitative analyses, extract the entropy characteristics of training injury risk big data, optimize the decision-making and assessment process of injury risk through stable result assessment and fuzzy decision-making, and establish an expert system analysis model of sports training injury risk assessment. The hierarchical analysis method is applied to evaluate the training injury risk, and the adaptive fuzzy control is optimized to realize the optimal design of training injury risk assessment. Results show that this method has good adaptive characteristics and high certainty.
Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity
Authors: Araveeporn Autcha
Published: September 2022

Linear and quadratic discriminant analysis are two fundamental classification methods used in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids (MVE), and t-distribution methods are used to estimate the parameter of independent variables on the multivariate normal distribution in order to classify binary dependent variables. The MM and ML methods are popular and effective methods that approximate the distribution parameter and use observed data. However, the MVE and t-distribution methods focus on the resampling algorithm, a reliable tool for high resistance. This paper starts by explaining the concepts of linear and quadratic discriminant analysis and then presents the four other methods used to create the decision boundary. Our simulation study generated the independent variables by setting the coefficient correlation via multivariate normal distribution or multicollinearity, often through basic logistic regression used to construct the binary dependent variable. For application to Pima Indian diabetic dataset, we expressed the classification of diabetes as the dependent variable and used a dataset of eight independent variables. This paper aimed to determine the highest average percentage of accuracy. Our results showed that the MM and ML methods successfully used large independent variables for linear discriminant analysis (LDA). However, the t-distribution method of quadratic discriminant analysis (QDA) performed better when using small independent variables.