bluuebunny/crossref_metadata_2025 huggingface dataset:
On a polynomial involving roots of unity and its applications
Authors: Wu Hai-Liang , She Yue-Feng
Published: April 2022

Let [Formula: see text] be a prime. Gauss first introduced the polynomial [Formula: see text] where [Formula: see text] and [Formula: see text] varies over all quadratic residues modulo p and [Formula: see text]. Later Dirichlet investigated this polynomial and used this to solve problems involving the Pell equations. Recently, Sun studied some trigonometric identities involving this polynomial. In this paper, we generalize their results. As applications of our result, we extend Chowla’s result on the congruence concerning the fundamental unit of [Formula: see text] and give an equivalent form of the extended Ankeny et al. conjecture.
Global stability of an age-structured model of smoking and its treatment
Authors: Tan Yuan-Shun , Li Xiao-Xue , Yang Jing , Cheke R. A.
Published: April 2022

Smoking is a serious global public health problem. The ability to quit smoking is closely related to age; in addition, personal determination and education usually play an important role in quitting smoking. In order to capture such characteristics, we developed a novel age-structured smoking dynamical model. By defining the smoking generation number [Formula: see text], the local stability, global stability of the boundary equilibrium and endemic equilibrium are obtained using Lyapunov functions. The uniform persistence and the well-posedness and asymptotic smoothness of the solutions are also studied. Sensitivity analyzes show that the lower the age of onset of smoking and the higher the determination to stop, the greater the likelihood of quitting smoking and numerical studies support the theoretical results.
Dynamics for a hybrid non-autonomous prey–predator system with generalist predator and impulsive conditions on time scales
Authors: Kumar Anil , Malik Muslim , Kang Yun
Published: April 2022

In this paper, we investigate the dynamical behavior for a hybrid non-autonomous predator–prey system with Holling Type II functional response, impulsive effects and generalist predator on time scales, where our proposed model commutes between a continuous-time dynamical system and discrete-time dynamical system. By using comparison theorems, we first study the permanence results of the proposed model. Also, we established the uniformly asymptotic stability for the almost periodic solution of the proposed model. Finally, in the last section, we provide some examples with numerical simulation.
Facial Action Units Detection to Identify Interest Emotion: An Application of Deep Learning
Authors: Belhouchette Kenza
Published: April 2022

For human beings, facial expression is one of the most powerful and natural ways to communicate their emotions and intentions. A human being is able to recognize facial expressions effortlessly, but for a machine, this task is very difficult. Today, facial expression recognition is proving to be one of the most relevant applications in many fields such as human–computer interaction, medicine, security, education, etc. Facial Action Coding System (FACS) is a method that describes face movements. This later became a main description tool used in the studies concerned with facial expression. In this paper, we propose an action units recognition system which reflects interest emotion using deep learning, particularly the Convolutional Neural Network (CNN) architecture “MobileNetV2”. Our choice of this system is motivated by its success in image classification. Our system allows detecting action units which define interest emotion from an input image. In other words, our classifier differentiates interest facial movements from other affective states. This identification is very useful in several areas, particularly in e-learning: For example, knowing whether a distance learner is interested or not in the course certainly influences the quality of learning and reduces the dropout rate. Our proposed approach presented a very satisfactory recognition rate despite the absence of a large database.
Early Cretaceous ocean-island basalt-type magmatism in northern Guangdong: implications for lithospheric thinning in the South China Block
Authors: Jiang Hai , Shao Shang , Jiang Shao-Yong , Yang Shui-Yuan , Zhao Kui-Dong
Published: April 2022

The South China Block underwent a tectonic switch from compression to extension between the Late Jurassic and Early Cretaceous, but the extension process has not been well understood. Here we report that the Meiziwo lamprophyres in northern Guangdong, formed in the Early Cretaceous (
c.
136 Ma;
40
Ar/
39
Ar dating), are characterized by ocean-island basalt (OIB)-like geochemical signatures and Pb enrichment, together with moderately high (
87
Sr/
86
Sr)
i
(0.70642–0.70766), positive

ε

Nd
(
t
) (+4.6 to +5.1) and intermediate (
206
Pb/
204
Pb)
i
(18.579–18.602), (
207
Pb/
204
Pb)
i
(15.727–15.733) and (
208
Pb/
204
Pb)
i
(38.871–38.909). The lamprophyres were derived from an asthenosphere-dominated source, with contributions from recycled oceanic crust and terrigenous sediments. In contrast, the previously reported
c.
154 Ma mafic dyke in eastern Guangdong and the
c.
165–155 Ma A-type granite belt in southern Hunan and nearby regions show arc-like signatures and unradiogenic Nd but moderately radiogenic Pb isotopes, suggesting magma derivation from a lithospheric mantle. Hence, the change from lithospheric to asthenospheric sources may be caused by progressive lithospheric extension and thinning associated with the rollback and tearing of the palaeo-Pacific plate. The 136 Ma Meiziwo lamprophyres, together with the 140 Ma asthenosphere-derived OIB-like mafic rocks nearby, signify the petrological response to regional extension in the South China Block from
c.
140 Ma onward.

Supplementary material:
Tables S1–S3 and Figures S1–S4 are available at
https://doi.org/10.6084/m9.figshare.c.5953720
NEAR: Neighborhood Edge AggregatoR for Graph Classification
Authors: Kim Cheolhyeong , Moon Haeseong , Hwang Hyung Ju
Published: April 2022

Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformatics, social network analysis, and data mining. Recent GNN algorithms are based on neural message passing, which enables GNNs to integrate local structures and node features recursively. However, past GNN algorithms based on 1-hop neighborhood neural message passing are exposed to a risk of loss of information on local structures and relationships. In this article, we propose Neighborhood Edge AggregatoR (NEAR), a framework that aggregates relations between the nodes in the neighborhood via edges. NEAR, which can be orthogonally combined with Graph Isomorphism Network (GIN), gives integrated information that describes which nodes in the neighborhood are connected. Therefore, NEAR can reflect additional information of a local structure of each node beyond the nodes themselves in 1-hop neighborhood. Experimental results on multiple graph classification tasks show that our algorithm makes a good improvement over other existing 1-hop based GNN-based algorithms.
A Multi-channel Hierarchical Graph Attention Network for Open Event Extraction
Authors: Wan Qizhi , Wan Changxuan , Xiao Keli , Hu Rong , Liu Dexi
Published: April 2022

Event extraction is an essential task in natural language processing. Although extensively studied, existing work shares issues in three aspects, including (1) the limitations of using original syntactic dependency structure, (2) insufficient consideration of the node level and type information in Graph Attention Network (GAT), and (3) insufficient joint exploitation of the node dependency type and part-of-speech (POS) encoding on the graph structure. To address these issues, we propose a novel framework for open event extraction in documents. Specifically, to obtain an enhanced dependency structure with powerful encoding ability, our model is capable of handling an enriched parallel structure with connected ellipsis nodes. Moreover, through a bidirectional dependency parsing graph, it considers the sequence of order structure and associates the ancestor and descendant nodes. Subsequently, we further exploit node information, such as the node level and type, to strengthen the aggregation of node features in our GAT. Finally, based on the coordination of triple-channel features (i.e., semantic, syntactic dependency and POS), the performance of event extraction is significantly improved. Extensive experiments are conducted to validate the effectiveness of our method, and the results confirm its superiority over the state-of-the-art baselines. Furthermore, in-depth analyses are provided to explore the essential factors determining the extraction performance.
Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT
Authors: Khatun Aisha , Rahman Anisur , Islam Md Saiful , Chowdhury Hemayet Ahmed , Tasnim Ayesha
Published: April 2022

Authorship Attribution is the task of creating an appropriate characterization of text that captures the authors’ writing style to identify the original author of a given piece of text. With increased anonymity on the internet, this task has become increasingly crucial in various security and plagiarism detection fields. Despite significant advancements in other languages such as English, Spanish, and Chinese, Bangla lacks comprehensive research in this field due to its complex linguistic feature and sentence structure. Moreover, existing systems are not scalable with the increasing number of authors, and performance drops with the small number of samples per author. In this paper, we propose the use of Average-Stochastic Gradient Descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) architecture and an effective transfer learning approach that addresses the problem of complex linguistic features extraction and scalability for authorship attribution in Bangla Literature (AABL). We analyze the effect of different tokenization, such as word, sub-word, and character level tokenization, and demonstrate the effectiveness of these tokenizations in the proposed model. Moreover, we introduce the publicly available Bangla Authorship Attribution Dataset of 16 authors (BAAD16) containing 17,966 sample texts and 13.4+ million words to solve the standard dataset scarcity problem and release six variations of pre-trained language models for use in any Bangla NLP downstream task. For evaluation, we used our developed BAAD16 dataset as well as other publicly available datasets. Empirically, our proposed model outperformed state-of-the-art models and achieved 99.8% accuracy in the BAAD16 dataset. Furthermore, we showed that the proposed system scales much better with the increasing number of authors, and performance remains steady even with few training samples.
A Survey on Cyber Situation-awareness Systems: Framework, Techniques, and Insights
Authors: Alavizadeh Hooman , Jang-Jaccard Julian , Enoch Simon Yusuf , Al-Sahaf Harith , Welch Ian , Camtepe Seyit A. , Kim Dan Dongseong
Published: April 2022

Cyberspace is full of uncertainty in terms of advanced and sophisticated cyber threats that are equipped with novel approaches to learn the system and propagate themselves, such as AI-powered threats. To debilitate these types of threats, a modern and intelligent Cyber Situation Awareness (SA) system needs to be developed that has the ability of monitoring and capturing various types of threats, analyzing, and devising a plan to avoid further attacks. This article provides a comprehensive study on the current state-of-the-art in the cyber SA to discuss the following aspects of SA: key design principles, framework, classifications, data collection, analysis of the techniques, and evaluation methods. Last, we highlight misconceptions, insights, and limitations of this study and suggest some future work directions to address the limitations.
Low-dose morphine reduces tolerance to central hypovolemia in healthy adults without affecting muscle sympathetic outflow
Authors: Watso Joseph C. , Belval Luke N. , Cimino Frank A. , Orth Bonnie D. , Hendrix Joseph M. , Huang Mu , Johnson Elias , Foster Josh , Hinojosa-Laborde Carmen , Crandall Craig G.
Published: April 2022

In this randomized, crossover, placebo-controlled trial, we found that tolerance to simulated hemorrhage was lower after low-dose morphine administration. Such reductions in hemorrhagic tolerance were observed without differences in MSNA burst frequency responses between morphine and placebo trials. These data, the first to be obtained in conscious humans, demonstrate that low-dose morphine reduces hemorrhagic tolerance. Thus, morphine is not an ideal analgesic for a hemorrhaging individual in the prehospital setting.
Pregnancy-specific uterine vascular reactivity: a data-driven computational model of shear-dependent, myogenic, and mechanical radial artery features
Authors: Allerkamp Hanna H. , Pole Teagan , Boukham Ali , James Joanna L. , Clark Alys R.
Published: April 2022

To our knowledge, this is the first data-driven computational model of autoregulation of uterine radial arteries, likely rate limiters of maternal blood flow to the placenta. The study demonstrates that uterine radial arteries behave differently from systemic vessels (higher compliance, shear-mediated constriction) and change their properties in pregnancy (higher myogenic tone, higher compliance, tolerance of higher flow rates). This pregnancy-specific mathematical model of vascular reactivity allows interrogation of the functional significance of incomplete vascular adaption in pathology.
An Extended Target Tracking Algorithm Based on Gaussian Surface Fitting
Authors: Han Yulan , Xu Han , Hu Gangdun , Han Chongzhao
Published: April 2022

Although the existing algorithms have done a lot of progress in extended target tracking (ETT), most algorithms are limited by the assumption of the target shape, and there are problems with parameter calculations. To solve the problems above, an extended target tracking algorithm based on level set and Gaussian Surface fitting (GSF), is proposed. Different from the existing extended target tracking algorithms, the proposed algorithm uses a level set to implicitly describe the evolution of the estimated shape, eliminates the parameterization process, and uses the GSF to construct the measurement source spatial distribution function (MSSDF) of the extended target. The proposed algorithm can obtain the estimated shape of the extended target without knowing the prior shape and size of the target. The simulation results verify the effectiveness and reliability of the proposed algorithm.
Expression of CX3CL1 and CCL28 in Spinal Metastases of Lung Adenocarcinoma and Their Correlation with Clinical Features and Prognosis
Authors: Li Shuguang , Liu Shuai , Xun Jianjun , Zhao Feifei
Published: April 2022

Lung adenocarcinoma is the most common non-small-cell lung cancer. In this paper, we aim to investigate the expression of chemokine ligand 1 (cx3cl1) and chemokine ligand 28 (CCL28) in spinal metastases of lung adenocarcinoma and their correlation with clinical features and prognosis. We analyzed the clinical data of 40 patients with lung adenocarcinoma and spinal metastases who underwent surgery in our hospital from January 2018 to January 2021 retrospectively. The expression levels of cx3cl1 and CCL28 in bone metastases were detected by immunohistochemistry, and the staining results were sorted and classified. Combined with the follow-up results and clinicopathological data, we statistically analyzed the expression of cx3cl1 and CCL28 in spinal bone metastases and their correlation with prognosis. Among the 40 patients with spinal metastasis of lung adenocarcinoma, 7 cases were strongly positive for cx3cl1, 25 cases were moderately positive, and 8 cases were weakly positive and negative. CCL28 was strongly positive in 9 cases, moderately positive in 26 cases, weakly positive and negative in 5 cases. The expression of cx3cl1 was correlated with ECOG score (P = 0.005) and visceral organ metastasis (P = 0.004), but not with age, sex, and the number of bone metastases (P > 0.05). The expression of CCL28 was correlated with ECOG score (P = 0.022) and visceral organ metastasis (P = 0.003), but not with age, sex, and the number of bone metastases (P > 0.05). The OS of patients with strong cx3cl1 positive was significantly shorter than that of patients with medium positive and weak positive (P < 0.001). The survival time was 10, 7, and 4 months, respectively. The OS of patients with strong positive CCL28 was significantly shorter than that of patients with medium positive and weak positive CCL28 (P = 0.004). The survival time was 12, 8, and 4 months, respectively. Univariate analysis showed that ECOG score (P < 0.001), chemotherapy (P = 0.032), visceral organ metastasis (P = 0.002), cx3cl1 expression (P < 0.001), and CCL28 expression (P = 0.004) were the risk factors of OS. Cox regression analysis showed that the expression of cx3cl1 was an independent risk factor for OS in patients with spinal metastasis of lung adenocarcinoma (P = 0.044). Cx3cl1 and CCL28 were highly/strongly positive in spinal metastases of lung adenocarcinoma. The level of cx3cl1 can be used as an index to judge the clinical prognosis of patients with spinal metastasis of lung adenocarcinoma, which can better reflect the prognosis of patients than CCL28.
A Novel Study of Graphs Based on
m
-Polar Cubic Structures
Authors: Muhiuddin G. , Alanazi A. M. , Mahboob A. , Alkhaldi A. H. , Alatwai Wejdan
Published: April 2022

By combining the notions of interval-valued m-polar fuzzy graphs and m-polar fuzzy graphs, the notion of m-polar cubic graphs is first introduced. Then, the degree of a vertex in m-polar cubic graphs and complete m-polar cubic graphs is defined. After that, the concepts of direct product and strong product of m-polar cubic graphs are given. Moreover, weak isomorphism and co-weak isomorphism are defined, and examples are given to prove that weak isomorphism and co-weak isomorphism are not an isomorphism. Finally, the notion of complement m-polar cubic graphs and weak complement m-polar cubic graphs is presented.
Collection System of Air Conditioners Remanufacturing: Development and Optimization under Probabilistic Uncertainty
Authors: Alkahtani Mohammed , Salah Bashir , Ziout Aiman , Alatefi Moath
Published: April 2022

The need for air conditioners (ACs) is increasing every year, particularly in the Kingdom of Saudi Arabia (KSA). The annual sales of ACs clearly indicated either buying new conditioning systems or replacing existing ACs. When air conditioners are replaced for any reason, a large number of used air conditioners are available, which leads to the necessity of remanufacturing these ACs for economic and environmental purposes. The current research focuses on investigating the collection system of used window ACs as a part of the reverse logistics of remanufacturing window ACs under the uncertainty of demand and production rate. To optimize the average inventory and fulfill the need of the ACs remanufacturing unit, this current research explained many operations in the collection system and their relationship to the remanufacturing unit. These objectives were investigated using a combination of simulation, mathematical, and optimization techniques. After developing the model using Arena software, the simulation model for the initial solution was run. The theoretical and mathematical concepts of inventory have been exploited to find an initial solution for the objectives under investigation. The Optquest tool was used for the simulation model of the initial solution to improve the iterative search technique. Finally, a sensitivity analysis was carried out to determine each input parameter’s magnitude and individual impact on the results. The findings suggest that simulation and optimization strategies are effective in improving average inventory and lost demand. As a result, the average inventory has been reduced significantly.
Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction
Authors: Lu Zhiqian , Long Feixiang , He Xiaodong
Published: April 2022

Background and Objective. Effective segmentation of pulmonary nodules can effectively assist in the diagnosis of benign and malignant pulmonary nodules. We aim to explore the effectiveness of classification and segmentation algorithms in diagnosing benign and malignant pulmonary nodules under different CT reconstructions. Methods. The imaging data of 55 patients with chest CT plain scan in the Xuancheng People’s Hospital were collected retrospectively. The data of each patient included lung window reconstruction, mediastinum reconstruction, and bone window reconstruction. The depth neural network and 3D convolution neural network were used to construct the model and train the classification and segmentation algorithm. The pathological results were the gold standard for benign and malignant pulmonary nodules. The classification and segmentation algorithms under three CT reconstruction algorithms were compared and analyzed by analysis of variance. Results. Under the three CT reconstruction algorithms, the classification accuracy of pulmonary nodule density types was 98.2%, 96.4%, and 94.5%, respectively. The Dice coefficients of all nodule segmentation were

80.32
%
±
5.91
%

,

79.83
%
±
6.12
%

, and

80.17
%
±
5.89
%

, respectively. The diagnostic accuracy between benign and malignant pulmonary nodules under different reconstruction algorithms was 98.2%, 96.4%, and 94.5%, respectively. There was no significant difference in the classification accuracy, Dice coefficients, and diagnostic accuracy of pulmonary nodules under three different reconstruction algorithms (all

P
>
0.05

). Conclusion. The depth neural network algorithm combined with 3D convolution neural network has a good efficiency in identifying benign and malignant pulmonary nodules under different CT reconstruction classification and segmentation algorithms.
Aqueous Extract and Polysaccharide of Aconiti Lateralis Radix Induce Apoptosis and G0/G1 Phase Cell Cycle Arrest by PI3K/AKT/mTOR Signaling Pathway in Mesangial Cells
Authors: Li Xingyao , An Peng , Zhao Yanhong , Cai Zimo , Ye Bingyu , Wang Yafeng , Wang Wenfang , Gao Qi , Li Liuyun , Zhang Tao , Wu Xili
Published: April 2022

Mesangial proliferative glomerulonephritis (MesPGN) is a common renal disease that lacks effective drug intervention. Aconiti Lateralis Radix (Fuzi), a natural Chinese medical herb, is found with significant therapeutic effects on various diseases in the clinic. However, its effects on MesPGN have not been reported. This study is aimed to discuss the therapeutic effects of the aqueous extract of Aconiti Lateralis Radix (ALR) and the polysaccharides of Aconiti Lateralis Radix (PALR) on MesPGN as well as the underlying mechanism. In this study, we, firstly, studied the anti-MesPGN mechanism of ALR and PALR. ALR and PALR inhibit the proliferation of the mesangial cells through the PI3K/AKT/mTOR pathway, induce the G0/G1 phase of block and apoptosis, inhibit the activity of Cyclin E and CDK2, increase the expression of Bax, cleaved caspase-8/caspase-8, and cleaved caspase-3/caspase-3 proteins, and effectively inhibit the growth of the mesangial cells. Overall, our data suggest that ALR and PALR may be potential candidates for MesPGN and that PALR is more effective than ALR.
Computational Simulation Analysis for Physical Identifiable Teaching Technology
Authors: Du Aihui
Published: April 2022

In order to improve the identifiable teaching effect of physics, this paper studies the identifiable teaching technology of physics combined with computer simulation analysis technology, changes the traditional physics teaching method, and studies the identifiable teaching effect of computer simulation algorithm. The advantages are obvious; that is, it overcomes scaling, translation, and rotation and does not require a lot of time to learn samples, etc. In addition, this paper combines the computer simulation analysis technology and the actual needs of physics teaching to construct an intelligent and identifiable teaching system. The experimental research results show that the physics identifiable teaching system based on computer simulation analysis proposed in this paper can play an important role in physics teaching.
Simulation and Compensation of Axial Geometric Errors for Cycloidal Gears Based on Form Grinding
Authors: Jiang Chuang , Wang Huiliang , Han Tianhao , Liu Xing
Published: April 2022

To increase quality, reduce cycloidal gear noise, and avoid unnecessary vibration and shock, a compensation of axial geometric errors method is proposed based on the cycloidal gear form grinding. In the process of machining cycloidal gears, the relative position relationship between the grinding wheel and workpiece is affected by geometric errors of the motion axes, which has serious effects on the surface accuracy of the cycloidal gears. Combined with cycloidal gear form grinding kinematic principles, a geometric error model for each axis of a four-axis computer numerical control form grinding machine is established. By changing the compensation value of the geometrical errors on six degrees of freedom, the error of the cycloid gear tooth surface machined is obtained. Based on a sensitivity analysis of geometrical errors of each axis, the corrections are determined through an optimization process that targets the minimization of the tooth flank errors. The geometric errors of each axis of the cycloid gear grinding machine are compensated, and then, the cycloid gears produced by the machine are processed. Through the processing experiment, the error data of the actual processing before and after the compensation are compared, which indicates that the machining accuracy of the cycloid gear grinding machine is obviously improved. It has an important guiding significance in improving the precision and performance of large CNC form gear grinding machines.
Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models
Authors: Ghaderzadeh Mustafa , Eshraghi Mohammad Amir , Asadi Farkhondeh , Hosseini Azamossadat , Jafari Ramezan , Bashash Davood , Abolghasemi Hassan
Published: April 2022

Introduction. While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic methods for this COVID-19 variant became more complex, health-care centers faced a dramatic increase in patients. Thus, the need for less expensive and faster diagnostic methods led researchers and specialists to work on improving diagnostic testing. Method. Inspired by the COVID-19 diagnosis methods, the latest and most efficient deep learning algorithms in the field of extracting X-ray and CT scan image features were used to identify COVID-19 in the early stages of the disease. Results. We presented a general framework consisting of two models which are developed by convolutional neural network (CNN) using the concept of transfer learning and parameter optimization. The proposed phase of the framework was evaluated on the test dataset and yielded remarkable results and achieved a detection sensitivity, specificity, and accuracy of 0.99, 0.986, and 0.988, for the first phase and 0.997, 0.9976, and 0.997 for the second phase, respectively. In all cases, the whole framework was able to successfully classify COVID-19 and non-COVID-19 cases from CT scans and X-ray images. Conclusion. Since the proposed framework was based on two deep learning models that used two radiology modalities, it was able to significantly assist radiologists in detecting COVID-19 in the early stages. The use of models with this feature can be considered as a powerful and reliable tool, compared to the previous models used in the past pandemics.
Analysis and Optimization of the Online Vocal Teaching System Based on Intelligent Computing
Authors: Jiang Luzhen
Published: April 2022

With the continuous development of science and technology, network technology has become more and more advanced and network applications are widely used in all walks of life, and vocal music teaching is no exception. The Intelligent Agent plays a unique role in networked teaching by making up for the lack of intelligence, adaptiveness, autonomy, and interoperability and interactivity in all aspects of traditional teaching. This paper introduces the knowledge of Intelligent Agent and then discusses its application in the networked teaching environment. Compared with the traditional teaching environment, the intelligent network teaching environment is more conducive to the initiative and innovation of students, reflecting the characteristics of student-centred learning and achieving a variety of functions such as teaching resource sharing, information interaction, online communication, and distance learning with the support of computer network technology. It is a new field to be explored, especially opening up a new chapter of online vocal music teaching.
Antileukaemic Cell Proliferation and Cytotoxic Activity of Edible Golden Cordyceps (Cordyceps militaris) Extracts
Authors: Tima Singkome , Tapingkae Tawat , To-anun Chaiwat , Noireung Parinn , Intaparn Phikul , Chaiyana Wantida , Sirithunyalug Jakkapan , Panyajai Pawaret , Viriyaadhammaa Natsima , Nirachonkul Wariya , Rueankham Lapamas , Aung Win Lae , Chueahongthong Fah , Chiampanichayakul Sawitree , Anuchapreeda Songyot
Published: April 2022

Golden cordyceps (Cordyceps militaris) is a mushroom of the genus Cordyceps. It has been used as a food supplement for both healthy and ill people. In this study, the antileukaemic cell proliferation activities of golden cordyceps extracts were examined and compared with standard cordycepin (CDCP) in EoL-1, U937, and KG-1a cells. Wilms’ tumour 1 (WT1) protein was used as a biomarker of leukaemic cell proliferation. The cytotoxicity of the extracts on leukaemic cells was determined using the MTT assay. Their inhibitory effects on WT1 protein expression and cell cycle progression of EoL-1 cells were investigated using Western blotting and flow cytometry, respectively. Induction of KG-1a cell differentiation (using CD11b as a marker) was determined using flow cytometry. The golden cordyceps extracts exhibited cytotoxic effects on leukaemic cells with the highest IC50 value of 16.5 ± 3.9 µg/mL, while there was no effect on normal blood cells. The expression levels of WT1 protein in EoL-1 cells were decreased after treatment with the extracts. Moreover, cell cycle progression and cell proliferation were inhibited. The levels of CD11b increased slightly following the treatment. All these findings confirm the antileukaemic proliferation activity of golden cordyceps.
A Retrospective Analysis of Metagenomic Next Generation Sequencing (mNGS) of Cerebrospinal Fluid from Patients with Suspected Encephalitis or Meningitis Infections
Authors: Gu Jina , Chen Lin , Zeng Chengjun , Yang Xiaoyan , Pan Danmei , Cao Hongchao , Qian Qinbin
Published: April 2022

We determined the clinical value of metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) for the diagnosis of patients with suspected encephalitis or meningitis infection. Clinical data were collected and retrospectively analyzed from patients with suspected cases of encephalitis or meningitis who presented at four hospitals in Ningbo from January 1st, 2019 to December 31st, 2020. Of a total of 66 suspected cases, 41 (62.12%) were diagnosed with central nervous system infections, which included 18 cases (27.27%) of viral infection, 13 cases (19.70%) of bacterial infection, 3 cases (4.55%) of Mycobacterium tuberculosis, 5 cases (7.58%) of fungal infection, and 2 cases (3.03%) of Rickettsia infection. From these cases, mNGS identified 25 (37.88%) true-positive cases, 8 (12.12%) false-positive cases, 20 (30.30%) true-negative cases, and 13 (19.70%) false-negative cases. The sensitivity of mNGS was 65.79% with a specificity of 71.43%. The positive rate was higher compared with traditional methods (37.88% vs. 24.39%). The results indicate that mNGS technology is a more sensitive method for detecting suspected infectious encephalitis or meningitis compared with traditional pathogen detection methods.
The Construction and Trend of Feminist Literature Theory Based on Social Media Data Mining
Authors: Cai Lanlan , Xu Xuhong
Published: April 2022

Up to now, the development path of Chinese women's liberation movement and modern Chinese women's literature is fundamentally different from that of the West. Therefore, the study of Chinese women's literature can not only rely on the essentialism of western feminist literary theory but also must return to the social reality and cultural reality of China. Based on social media data mining, this study uses the Word2vec model to map the text content to a more abstract word vector space, improves the original Text Rank algorithm from three aspects, semantic association between words, word frequency, and word directionality, then carries out feature extraction, and applies this algorithm to the generation of user tags. The feasibility and superiority of the model are verified by comparative experiments on LFR benchmark network. The research in this study provides a reference for the analysis of users' interests and behaviors and has certain theoretical significance and application value.
[Retracted] Acupuncture for Knee Osteoarthritis: A Systematic Review of Randomized Clinical Trials with Meta‐Analyses and Trial Sequential Analyses
Authors: Tian Hao , Huang Liuyang , Sun Mingsheng , Xu Guixing , He Jiamei , Zhou Zhuo , Huang Fengyuan , Liu Yilin , Liang Fanrong
Published: April 2022

Background. Knee osteoarthritis (KOA) can cause chronic pain and seriously affect the quality of patient lives. The continued emergence of high‐quality RCTS requires us to update the quality of evidence. This study aims to evaluate the efficacy of acupuncture for KOA patients and calculate the required information size (RIS) to determine whether further clinical studies are required. Methods. We searched PubMed, Embase, WOS, CBM, CNKI, VIP, WHO ICTRP, ChiCTR, and Grey literature to collect randomized controlled trials (RCTs) of acupuncture for KOA from inception to December 2021. A meta‐analysis was performed according to the Cochrane systematic review method by using Review Manager 5.4 and TSA 0.9.5.10 beta, and GRADE was used to evaluate the quality of the evidence. Trial sequential analysis was used to control random errors and calculate the required information size. Results. Eleven RCTs with 2484 patients were included in our meta‐analysis, meeting the inclusion criteria for the meta‐analysis. The meta‐analysis indicated that acupuncture had beneficial effect on knee osteoarthritis in reducing pain [n = 2387; SMD = −0.12, 95% CI (‐0.20, ‐0.04); I2 = 0%] and improved patients function activities [n = 2408; MD = −1.25, 95% CI (‐1.97, ‐0.53); I2 = 0%], but true acupuncture showed no significant effect in relieve patient’s stiffness [n = 1337; MD = −0.07, 95% CI (‐0.30, 0.15); I2 = 0%]. We pooled the studies which found no significant difference in improving the quality life of mental [n = 1462; SMD = 0.02, 95% CI (‐0.23, 0.27); I2 = 78%] and patients physical health (SF‐36 or SF‐12) [n = 1745; SMD = 1.01, 95% CI (‐0.08, 2.11); I2 = 0%] compared with sham acupuncture. The pain and function TSA graphs indicated that cumulative Z‐curves intersected with the traditional level of statistical favoring acupuncture, and more RCTs will required in the future studies. Conclusion. Acupuncture has beneficial effect on pain relief and improves function activities, and this treatment can be recommended as a beneficial alternative therapy in patients with KOA, particularly for chronic patients and those currently undergoing long‐term pain and help them increasing quality of life. But it should be further verified through more RCTs in function. Available studies suggested that acupuncture was superior to sham acupuncture in reduce pain and function as verified by TSA.
Placental Tissues as Biomaterials in Regenerative Medicine
Authors: Roy Annelise , Mantay Morgan , Brannan Courtney , Griffiths Sarah
Published: April 2022

Placental tissues encompass all the tissues which support fetal development, including the placenta, placental membrane, umbilical cord, and amniotic fluid. Since the 1990s there has been renewed interest in the use of these tissues as a raw material for regenerative medicine applications. Placental tissues have been extensively studied for their potential contribution to tissue repair applications. Studies have attributed their efficacy in augmenting the healing process to the extracellular matrix scaffolds rich in collagens, glycosaminoglycans, and proteoglycans, as well as the presence of cytokines within the tissues that have been shown to stimulate re‐epithelialization, promote angiogenesis, and aid in the reduction of inflammation and scarring. The compositions and properties of all birth tissues give them the potential to be valuable biomaterials for the development of new regenerative therapies. Herein, the development and compositions of each of these tissues are reviewed, with focus on the structural and signaling components that are relevant to medical applications. This review also explores current configurations and recent innovations in the use of placental tissues as biomaterials in regenerative medicine.
Output Feedback Model Predictive Control for NCSs with Input Quantization
Authors: Qu Hongchun , Li Yu , Liu Wei
Published: April 2022

This paper addresses the robust output feedback model predictive control (MPC) schemes for networked control systems (NCSs) with input quantization. The logarithmic quantizer is considered in this paper, and the sector bound approach is applied, which appropriately treats the quantization error as a sector‐bounded uncertainty. The presented method involves an offline designed state observer using linear matrix inequality (LMI) and online robust output feedback MPC algorithms which optimize one free control move followed by the output feedback using the estimated state. Moreover, due to the uncertainty of estimation error, a technique of refreshing the bound of estimation error which involves the quantization error is provided so as to guarantee the recursive feasibility of the optimization problem. The proposed MPC schemes inherit the characteristics of the synthesis approach of MPC, guaranteeing the recursive feasibility of the optimization problem and the stability of a closed‐loop system, and explicitly account for quantization error. Two simulation examples are given to illustrate the effectiveness of the proposed methods.
Simultaneous Corneal Topography and Epithelial Thickness Mapping from a Single Measurement Using Optical Coherence Tomography
Authors: Sikorski Bartosz L.
Published: April 2022

Purpose. To evaluate the performance of corneal epithelial thickness mapping (ETM) and demonstrate simultaneous measurement of ETMs and corneal topography using REVO NX (Optopol Technology, Zawiercie, Poland)—an OCT device for anterior and posterior segment imaging. Methods. One hundred thirty-seven eyes of 137 normal subjects and patients with corneal diseases were recruited to the study. Each subject was scanned with REVO NX. ETMs and corneal topography maps were reconstructed from a single measurement. Corneal topography was also carried out using Pentacam (Oculus, Wetzlar, Germany). One hundred twenty-eight eyes were qualified for the final analysis. Forty healthy eyes were used to evaluate the performance of ETM, and 88 eyes were used to compare ETMs and corneal topography. The repeatability and reproducibility of ETMs in healthy subjects were assessed on the basis of 17 spatial zones derived from an 8-mm diameter corneal scan using within-subject standard deviation, test-retest repeatability, within-subject coefficient of variation (CoV), and intraclass correlation coefficient (ICC). Results. The ICC for both repeatability and reproducibility of ETMs for the central sector was 0.95. The ICC value for the other sectors was only moderately lower. However, the CoV for repeatability (≤1.55%) was slightly higher than the value reported for the RTVue device (Optovue, Inc, Fremont, California, USA), for which a CoV in the central zone of 1.07% was reported in unoperated eyes. The superior quadrants were found to be the thinnest while the inferior ones were the thickest. ETMs and topography maps created from a single OCT measurement present a complementary image of the cornea. Conclusions. ETMs obtained using REVO NX show high levels of repeatability and reproducibility in normal eyes. Because the topographic and epithelial thickness analyses are performed using the same data, which means they are based on the exact same 3D corneal model, they do not require reciprocal centration and map matching. This ensures a complete point-to-point correlation between ETMs and corneal topography maps, which paints a fuller picture of a given pathology.
Modeling of Ideological and Political Education System in Colleges and Universities Based on Naive Bayes-BP Neural Network in the Era of Big Data
Authors: Cui Yan
Published: April 2022

Big data is the product of the development of the times, which affects all aspects of social development and reorganizes the ideological and political network system of universities. The development of big data brings new opportunities and challenges to the NIPE (network ideological and political education) system of universities. Therefore, universities should pay attention to the data in the ideological and political network system. The NIPEDM service platform was developed, which combines the existing Web-based PC and mobile development technology to find a new educational DM (data mining) path. Using classifiers to predict students’ future performance, a new NB_BPNN (Naive Bayes-BP neural network) model is proposed, which effectively combines the advantages of two existing models. The experimental results show that the new model has achieved good results in the field of learning evaluation.
Profiling the Enthusiastic, Neutral, and Sceptical Users of Conditionally Automated Cars in 17 Countries: A Questionnaire Study
Authors: Nordhoff Sina , Louw Tyron , Madigan Ruth , Lee Yee Mun , Innamaa Satu , Lehtonen Esko , Malin Fanny , Bjorvatn Afsaneh , Beuster Anja , Happee Riender , Kessel Tanja , Merat Natasha
Published: April 2022

The L3Pilot project tested SAE Level 3 (L3) conditionally automated driving functions addressing driving and travel behavior, impacts on safety, efficiency, environment and socio-economics, and user acceptance. To investigate individual variance in acceptance of conditionally automated cars, an online survey was performed among 18,631 respondents from 17 countries evaluating differences in age, gender, knowledge about the functionality of conditionally automated cars, awareness, information consumption behavior, and expected benefits of conditionally automated cars. Respondents were divided into Enthusiasts, Neutrals, and Sceptics differing in a high, moderate, and low acceptance of conditionally automated cars, respectively. Enthusiasts, Neutrals, and Sceptics differed most with regard to the expected benefits in the productive use of travel time, comfort, and safety of conditionally automated cars. Enthusiasts were male, younger, more knowledgeable about conditionally automated cars, more aware of automated cars, and more likely to receive information about automated cars from different sources, expecting improvements in the productive use of travel time, comfort, and safety due to conditionally automated cars. All groups were most knowledgeable about the lane keeping behavior of conditionally automated cars and least knowledgeable about the operation of conditionally automated cars in dedicated operational design domains. The results indicate that the communication and marketing of automated cars should create a realistic image of the capabilities and limitations of conditionally automated cars where user education programs should be harmonized to calibrate expectations and educate the public.
Nonlinear Stochastic SIS Epidemic Model Incorporating Lévy Process
Authors: El Koufi Amine
Published: April 2022

In this work, we study a stochastic SIS epidemic model with Lévy jumps and nonlinear incidence rates. Firstly, we present our proposed model and its parameters. We establish sufficient conditions for the extinction and persistence of the disease in the population using some stochastic analysis background. We illustrate our theoretical results by numerical simulations. We conclude that the white noise and Lévy jump influence the transmission of the epidemic.
Machine Vision Nondestructive Inspection System Assisted by Industrial IoT Supervision Mechanism
Authors: Wang Hairong , Lu Rong , Yu Duo
Published: April 2022

This paper introduces the development status of machine vision nondestructive testing technology and industrial IoT supervision mechanism. The study designs and implements a machine vision nondestructive testing system from two aspects: construction of industrial IoT supervision and detection model, and optimization of machine vision nondestructive testing algorithm. In this paper, the random deployment of dynamic and static nodes is adopted. The coverage rate after random deployment and the moving distance of dynamic nodes are two necessary research parameters. To improve the initial coverage and optimize the mobile path of dynamic nodes, this paper proposes a mobile deployment optimization scheme based on the supervisory mechanism model of industrial IoT, which improves the traversal of the quantum genetic algorithm by improving the genetic variation rules, thus improving the initial deployment of the network. The optimized machine vision nondestructive detection algorithm is used for mobile path optimization from dynamic nodes to target locations. Simulation results show that a random deployment of 100 static nodes and 20 dynamic nodes in a 400 m × 400 m factory area works best with a coverage rate of 6.719% and an average movement distance of 23.47 m, and the movement path avoids the obstacle area. The average accuracy of the modified machine vision nondestructive testing system is 1.59% higher than that before the modification, and the average detection accuracy of the final experiment reaches 95.46%. Not only is the coverage rate better than that of the cellular structure-based dynamic node optimization scheme, but also the monitoring range of the plant tends to be more comprehensive in the actual deployment environment. Through the analysis of test results, the system achieves the monitoring and display of data on the one hand and provides a natural information access and interaction experience for IoT managers on the other hand, which meets the requirements of real time, accuracy, and stability of industrial IoT information data to a certain extent.