bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Approach for Mining Correlated Frequent Subgraphs
Authors: Chowdhury Mohammad Ehsan Shahmi , Ahmed Chowdhury Farhan , Leung Carson K.
Published: October 2021

Nowadays graphical datasets are having a vast amount of applications. As a result, graph mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in numerous aspects. It is important to perform correlation analysis among the subparts (i.e., elements) of the frequent subgraphs generated using graph mining to observe interesting information. However, the majority of existing works focuses on complexities in dealing with graphical structures, and not much work aims to perform correlation analysis. For instance, a previous work realized in this regard, operated with a very naive raw approach to fulfill the objective, but dealt only on a small subset of the problem. Hence, in this article, a new measure is proposed to aid in the analysis for large subgraphs, mined from various types of graph transactions in the dataset. These subgraphs are immense in terms of their structural composition, and thus parallel the entire set of graphs in real-world. A complete framework for discovering the relations among parts of a frequent subgraph is proposed using our new method. Evaluation results show the usefulness and accuracy of the newly defined measure on real-life graphical datasets.
Deep Learning Methods for Sign Language Translation
Authors: Ananthanarayana Tejaswini , Srivastava Priyanshu , Chintha Akash , Santha Akhil , Landy Brian , Panaro Joseph , Webster Andre , Kotecha Nikunj , Sah Shagan , Sarchet Thomastine , Ptucha Raymond , Nwogu Ifeoma
Published: October 2021

Many sign languages are bona fide natural languages with grammatical rules and lexicons hence can benefit from machine translation methods. Similarly, since sign language is a visual-spatial language, it can also benefit from computer vision methods for encoding it. With the advent of deep learning methods in recent years, significant advances have been made in natural language processing (specifically neural machine translation) and in computer vision methods (specifically image and video captioning). Researchers have therefore begun expanding these learning methods to sign language understanding. Sign language interpretation is especially challenging, because it involves a continuous visual-spatial modality where meaning is often derived based on context.

The focus of this article, therefore, is to examine various deep learning–based methods for encoding sign language as inputs, and to analyze the efficacy of several machine translation methods, over three different sign language datasets. The goal is to determine which combinations are sufficiently robust for sign language translation
without
any gloss-based information.

To understand the role of the different input features, we perform ablation studies over the model architectures (input features + neural translation models) for improved continuous sign language translation. These input features include body and finger joints, facial points, as well as vector representations/embeddings from convolutional neural networks. The machine translation models explored include several baseline sequence-to-sequence approaches, more complex and challenging networks using attention, reinforcement learning, and the transformer model. We implement the translation methods over multiple sign languages—German (GSL), American (ASL), and Chinese sign languages (CSL). From our analysis, the transformer model combined with input embeddings from ResNet50 or pose-based landmark features outperformed all the other sequence-to-sequence models by achieving higher BLEU2-BLEU4 scores when applied to the controlled and constrained GSL benchmark dataset. These combinations also showed significant promise on the other less controlled ASL and CSL datasets.
DeFFusion: CNN-based Continuous Authentication Using Deep Feature Fusion
Authors: Li Yantao , Tao Peng , Deng Shaojiang , Zhou Gang
Published: October 2021

Smartphones have become crucial and important in our daily life, but the security and privacy issues have been major concerns of smartphone users. In this article, we present DeFFusion, a CNN-based continuous authentication system using Deep Feature Fusion for smartphone users by leveraging the accelerometer and gyroscope ubiquitously built into smartphones. With the collected data, DeFFusion first converts the time domain data into frequency domain data using the fast Fourier transform and then inputs both of them into a designed CNN, respectively. With the CNN-extracted features, DeFFusion conducts the feature selection utilizing factor analysis and exploits balanced feature concatenation to fuse these deep features. Based on the one-class SVM classifier, DeFFusion authenticates current users as a legitimate user or an impostor. We evaluate the authentication performance of DeFFusion in terms of impact of training data size and time window size, accuracy comparison on different features over different classifiers and on different classifiers with the same CNN-extracted features, accuracy on unseen users, time efficiency, and comparison with representative authentication methods. The experimental results demonstrate that DeFFusion has the best accuracy by achieving the mean equal error rate of 1.00% in a 5-second time window size.
Lamboozling Attackers: A New Generation of Deception
Authors: Shortridge Kelly , Petrich Ryan
Published: October 2021

The goal of this article is to educate software leaders, engineers, and architects on the potential of deception for systems resilience and the practical considerations for building deception environments. By examining the inadequacy and stagnancy of historical deception efforts by the information security community, the article also demonstrates why engineering teams are now poised to become significantly more successful owners of deception systems.
Synthesis of hydrocarbon vinyl polymer, its properties, applications, yearly production, and conjointly process
Authors: Sakib S M Nazmuz
Published: October 2021

The compound elected for creating this report is propenonitrile hydrocarbon vinyl polymer. The compound is additionally abbreviated as ABS and it's better-known to be a thermoplastic compound. It's an opaque engineering compound and is employed in a very wide selection of areas as well as electronic housing, automotive elements, totally different classes of shopper product, pipe fittings, and conjointly producing of plaything based mostly toy things. The report is comprised of synthesis of compound, its properties, applications, yearly production, and conjointly process of compound into the ultimate form and article.
Hypercapnia-induced vasodilation in the cerebral circulation is reduced in older adults with sleep-disordered breathing
Authors: Rastogi R. , Morgan B. J. , Badr M. S. , Chowdhuri S.
Published: October 2021

This study demonstrates, for the first time, in individuals with sleep-disordered breathing (SDB) that aging is associated with decreased hypercapnia-induced cerebral vasodilation (HCVD). In addition to advanced age, the magnitude of nocturnal oxygen desaturation due to SDB is an equal independent predictor of HCVD.
Airway luminal area and the resistive work of breathing during exercise in healthy young females and males
Authors: Peters Carli M. , Leahy Michael G. , Hohert Geoffrey , Lane Pierre , Lam Stephen , Sin Don D. , McKenzie Donald C. , Sheel Andrew William
Published: October 2021

We hypothesized that the higher resistive work of breathing in females compared with males during high-intensity exercise is due to smaller airways. In vivo measures of the fourth to eighth airway generations made using optical coherence tomography show that females tend to have smaller airway luminal areas of the fourth to sixth airway generations. Sex differences in airway luminal area result in a greater resistive work of breathing during exercise in females compared with males.
Reconstitution of the host holobiont in germ-free born male rats acutely increases bone growth and affects marrow cellular content
Authors: Czernik Piotr J. , Golonka Rachel M. , Chakraborty Saroj , Yeoh Beng San , Abokor Ahmed A. , Saha Piu , Yeo Ji-Youn , Mell Blair , Cheng Xi , Baroi Sudipta , Tian Yuan , Patterson Andrew D. , Joe Bina , Vijay-Kumar Matam , Lecka-Czernik Beata
Published: October 2021

Integration of microbiota in a host begins at birth and progresses during adolescence, forming a multidirectional system of physiological interactions. Here, we present an instantaneous effect of natural, bacterial gut colonization on the acceleration of longitudinal and radial bone growth in germ-free born, 7-wk-old male rats. Changes in bone mass and structure were analyzed after 10 days following the onset of colonization through cohousing with conventional rats and revealed unprecedented acceleration of bone accrual in cortical and trabecular compartments, increased bone tissue mineral density, improved proliferation and hypertrophy of growth plate chondrocytes, bone lengthening, and preferential deposition of periosteal bone in the tibia diaphysis. In addition, the number of small in size adipocytes increased, whereas the number of megakaryocytes decreased, in the bone marrow of conventionalized germ-free rats indicating that not only bone mass but also bone marrow environment is under control of gut microbiota signaling. The changes in bone status paralleled with a positive shift in microbiota composition toward short-chain fatty acids (SCFA)-producing microbes and a considerable increase in cecal SCFA concentrations, specifically butyrate. Furthermore, reconstitution of the host holobiont increased hepatic expression of IGF-1 and its circulating levels. Elevated serum levels of 25-hydroxy vitamin D and alkaline phosphatase pointed toward an active process of bone formation. The acute stimulatory effect on bone growth occurred independently of body mass increase. Overall, the presented model of conventionalized germ-free rats could be used to study microbiota-based therapeutics for combatting dysbiosis-related bone disorders.
Review on NSM CFRP Strengthened RC Concrete Beams in Shear
Authors: Kadhim Majid M. A. , Altaee Mohammed J. , Adheem Ali Hadi , Chabuk Ali , Al-Ansari Nadhir
Published: October 2021

The use of NSM FRP strengthening of concrete structures has become an attractive option to retrofit the existing structures against shear and flexure. This paper reviews only the utilization of NSM for shear in previous review articles. A database of tests of NSM strengthened beams in shear is presented to evaluate the existing design formulations of calculating the NSM contribution in shear. These formulations were in agreement with the experimental results in the database. Further research topics are also identified such as the shape of NSM FRP bars, combined effects of existing steel stirrups, and NSM FRP reinforcement and analytical formulations.
Clinical Characteristics and Long-Term Prognosis of Elderly Valvular Heart Disease Patients with Diabetes Mellitus: Five-Year Experience from a Single-Center Study of Southern China
Authors: Liang Yuan-Feng , Song Feier , Liu Huixia , Liu Jian , Zhang Yu-Yuan , Lin Wei-Dong , Liao Hong-Tao , Guo Hui-Ming , Tse Gary , Liu Fang-Zhou , Lin Zhanyi
Published: October 2021

Background. Diabetes mellitus (DM) is a prognostic marker in elderly patients with cardiovascular diseases, but its predictive value in elderly valvular heart disease (VHD) patients is unclear. This study aimed to investigate the effect of DM on the long-term outcome of elderly VHD patients. Methods. This single-center, observational study enrolled patients aged 65 and older consecutively with confirmed VHD using echocardiography. Patients, divided into the DM group and non-DM group, were followed up for major adverse cardiac and cerebrovascular events (MACCEs), including all-cause death, ischemic stroke, and heart failure rehospitalization. Results. Our study consisted of 532 patients over a median follow-up of 52.9 months. Compared with the non-DM group (n = 377), the DM group (n = 155) had higher incidences of ischemic stroke (25.2% vs. 13.5%,

P
=
0.001

), heart failure rehospitalization (37.4% vs. 20.7%,

P
<
0.001

), and MACCEs (60.0% vs. 35.8%,

P
<
0.001

). After adjustment of confounders by the multivariable cox regression, DM appeared as an independent predictor for MACCEs (adjusted hazard ratio, aHR: 1.88; 95% confidence interval 1.42–2.48;

P
<
0.001

). In the subgroup analysis of VHD etiology and functional style, conversely, DM was a protective factor for MACCEs in the patients with rheumatic VHD compared with those without rheumatic VHD (aHR: 0.43 vs. 2.27,

P
=
0.004

). Conclusions. DM was an independent predictor for ischemic stroke and heart failure rehospitalization in elderly VHD patients undergoing conservative treatment.
The Quality of Selected Online Learning Platforms and Their Effect on Education in the Sultanate of Oman
Authors: Jabbar Alkubaisi Ghaith Abdulsattar A. , Al-Saifi Nura Said , Al-Shidi Arwa Rashid , Al-Shukaili Zaid Said
Published: October 2021

The COVID-19 pandemic has led to the largest interruption in education around the world, affecting some 1.6 billion students. Classroom education stopped, to be replaced by online e-learning platforms. In higher education, e-learning is made available through recorded lectures, with online platforms becoming a significant part of the overall system. This study aims to identify the quality of online learning platforms, applying a set of criteria from the perspective of faculty members and students in higher education institutions in the Sultanate of Oman. The descriptive approach is used and the researchers collected data through a questionnaire directed at 32 faculty members and 104 students. The results show that e-learning programs are generally of high quality, from the perspective of the participants, but with statistically significant differences according to the type of program.
The Spectrum of Mapping Ideals of Type Variable Exponent Function Space of Complex Variables with Some Applications
Authors: Bakery Awad A. , Mohammed Mustafa M.
Published: October 2021

The topological and geometric behaviors of the variable exponent formal power series space, as well as the prequasi-ideal construction by

s

-numbers and this function space of complex variables, are investigated in this article. Upper bounds for

s

-numbers of infinite series of the weighted

n

th power forward and backward shift operator on this function space are being investigated, with applications to some entire functions.
Prediction of House Price Index Based on Bagging Integrated WOA-SVR Model
Authors: Wang Xiang , Gao Shen , Zhou Shiyu , Guo Yibin , Duan Yonghui , Wu Daqing
Published: October 2021

Aiming at the shortcomings of a single machine learning model with low model prediction accuracy and insufficient generalization ability in house price index prediction, a whale algorithm optimized support vector regression model based on bagging ensemble learning method is proposed. Firstly, gray correlation analysis is used to obtain the main influencing factors of house prices, and the segmentation forecasting method is used to divide the data set and forecast the house prices in the coming year using the data of the past ten years. Secondly, the whale optimization algorithm is used to find the optimal parameters of the penalty factor and kernel function in the SVR model, and then, the WOA-SVR model is established. Finally, in order to further improve the model generalization capability, a bagging integration strategy is used to further integrate and optimize the WOA-SVR model. The experiments are conducted to forecast the house price indices of four regions, Beijing, Shanghai, Tianjin, and Chongqing, respectively, and the results show that the prediction accuracy of the proposed integrated model is better than the comparison model in all cases.
Immunogenomic Landscape Analysis of Prognostic Immune-Related Genes in Hepatocellular Carcinoma
Authors: Qin Peng , Zhang Mengyu , Liu Xue , Dong Ziming
Published: October 2021

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death. HBV infection is an important risk factor for the tumorigenesis of HCC, given that the inflammatory environment is closely related to morbidity and prognosis. Consequently, it is of urgent importance to explore the immunogenomic landscape to supplement the prognosis of HCC. The expression profiles of immune‐related genes (IRGs) were integrated with 377 HCC patients to generate differentially expressed IRGs based on the Cancer Genome Atlas (TCGA) dataset. These IRGs were evaluated and assessed in terms of their diagnostic and prognostic values. A total of 32 differentially expressed immune‐related genes resulted as significantly correlated with the overall survival of HCC patients. The Gene Ontology functional enrichment analysis revealed that these genes were actively involved in cytokine‐cytokine receptor interaction. A prognostic signature based on IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) stratified patients into high-risk versus low-risk groups in terms of overall survival and remained as an independent prognostic factor in multivariate analyses after adjusting for clinical and pathologic factors. Several IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) of clinical significance were screened in the present study, revealing that the proposed clinical-immune signature is a promising risk score for predicting the prognosis of HCC.
UPLC/ESI-MS Phytochemical Screening of Deverra tortuosa Haematological and Histopathological Studies and Streptozotocin-Induced Diabetes in Rat
Authors: Alhumaydhi Fahad A. , Aljohani Abdullah S. M. , Elsharkawy Eman R.
Published: October 2021

Herbal plants represent a new source of hypoglycemic antidiabetic drugs; haematological and histopathological studies of methanol extract of Deverra tortuosa in streptozotocin-induced diabetic rats were investigated in vivo. A single intraperitoneal injection of 160 mg/kg bodyweights of streptozotocin was used to cause diabetes. Blood glucose levels were tested with an AccuCheck Advantage II glucometer and blood glucose test strips. After diabetes was confirmed, animals were orally treated with the extract, metformin, and insulin according to the experimental design. After extract therapy, histological alterations in the pancreas of diabetic rats were investigated. When compared to a control group, daily oral administration of D. tortuosa extract (300 mg/kg body weight) plus metformin (100 mg/kg) had a positive effect on blood glucose levels as well as showed an increased number of white blood cells (WBCs) and red blood cells (RBCs). The treatment with the extract for two weeks showed a positive impact on pancreatic histopathological changes in the groups with the diabetic rats. Phenolic fraction of the methanol extract was screened by the liquid chromatography-mass spectroscopy (LC-MS) method, which unveiled the existence of flavonoid compounds and phenolics as kaempferol, rutin, isorhamnetin-3-O-rutinoside, caffeic acid, and 4-hydroxybenzoic acid 4-O-glucoside. The results confirmed the use of the plant as an antidiabetic agent; the research recommended further studies on the plant to use the plant as an antidiabetic drug, where the plant extract also showed improvement in blood parameters.
Irisin: A Promising Target for Ischemia‐Reperfusion Injury Therapy
Authors: Wang Yani , Liu Huibin , Sun Na , Li Jing , Peng Xiang , Jia Ying , Karch Jason , Yu Bo , Wehrens Xander H. T. , Tian Jinwei
Published: October 2021

Ischemia‐reperfusion injury (IRI) is defined as the total combined damage that occurs during a period of ischemia and following the recovery of blood flow. Oxidative stress, mitochondrial dysfunction, and an inflammatory response are factors contributing to IRI‐related damage that can each result in cell death. Irisin is a polypeptide that is proteolytically cleaved from the extracellular domain of fibronectin type III domain‐containing protein 5 (FNDC5). Irisin acts as a myokine that potentially mediates beneficial effects of exercise by reducing oxidative stress, improving mitochondrial fitness, and suppressing inflammation. The existing literature also suggests a possible link between irisin and IRI, involving mechanisms similar to those associated with exercise. This article will review the pathogenesis of IRI and the potential benefits and current limitations of irisin as a therapeutic strategy for IRI, while highlighting the mechanistic correlations between irisin and IRI.
Type I Half Logistic Burr X-G Family: Properties, Bayesian, and Non-Bayesian Estimation under Censored Samples and Applications to COVID-19 Data
Authors: Algarni Ali , M. Almarashi Abdullah , Elbatal I. , S. Hassan Amal , Almetwally Ehab M. , M. Daghistani Abdulkader , Elgarhy Mohammed
Published: October 2021

In this paper, we present a new family of continuous distributions known as the type I half logistic Burr X-G. The proposed family’s essential mathematical properties, such as quantile function (QuFu), moments (Mo), incomplete moments (InMo), mean deviation (MeD), Lorenz (Lo) and Bonferroni (Bo) curves, and entropy (En), are provided. Special models of the family are presented, including type I half logistic Burr X-Lomax, type I half logistic Burr X-Rayleigh, and type I half logistic Burr X-exponential. The maximum likelihood (MLL) and Bayesian techniques are utilized to produce parameter estimators for the recommended family using type II censored data. Monte Carlo simulation is used to evaluate the accuracy of estimates for one of the family’s special models. The COVID-19 real datasets from Italy, Canada, and Belgium are analysed to demonstrate the significance and flexibility of some new distributions from the family.
Christ–Lee Model: (Anti-)chiral Supervariable Approach to BRST Formalism
Authors: Chauhan B. , Kumar S.
Published: October 2021

We derive the off-shell nilpotent of order two and absolutely anticommuting Becchi-Rouet-Stora-Tyutin (BRST), anti-BRST, and (anti-)co-BRST symmetry transformations for the Christ–Lee (CL) model in one0+1-dimension (1D) of spacetime by exploiting the (anti-)chiral supervariable approach (ACSA) to BRST formalism where the quantum symmetry (i.e., (anti-)BRST along with (anti-)co-BRST) invariant quantities play a crucial role. We prove the nilpotency and absolute anticommutativity properties of the (anti-)BRST along with (anti-)co-BRST conserved charges within the scope of ACSA to BRST formalism where we take only one Grassmannian variable into account. We also show the (anti-)BRST and (anti-)co-BRST invariances of the Lagrangian within the scope of ACSA.
Ozoroa insignis reticulata (Baker f.) R. Fern. & A. Fern. Root Extract Inhibits the Production of Extracellular Proteases by Staphylococcus aureus
Authors: Katsukunya Jonathan , Makurira Rumbidzai , Mukanganyama Stanley
Published: October 2021

Treatment of infections caused by S. aureus has become a challenge due to the emergency of resistant strains. Ozoroa reticulata root extracts have been used in traditional medicine to treat throat and chest pains in Zimbabwe. The objective of the study was to determine the effects of O. reticulata root bark extracts on the production of extracellular proteases by S. aureus. The root barks were collected, dried, and crushed into powder. To obtain different phytoconstituents, plant extractions were performed. Extractions were carried out using two solvent mixtures: ethanol : water (50 : 50 v/v) and dichloromethane : methanol (50 : 50 v/v). Serial exhaustive extractions were also performed using methanol, ethanol, dichloromethane, acetone, ethyl acetate, hexane, and water. The broth microdilution assays were used to assess the antibacterial effects of the Ozoroa reticulata root bark extracts against S. aureus. Ciprofloxacin was used as a positive control. Qualitative screening for extracellular protease production by S. aureus on BCG-skim milk agar plates using the most potent extract was carried out. The proteolytic zones were measured and expressed as the ratio of the diameter of the colony to the total diameter of the colony plus the zone of hydrolysis (

P

z

values). The ethyl acetate extract was found to be the most potent inhibitor of the growth of S. aureus with 99% inhibition and a minimum inhibitory concentration (MIC) of 100 µg/mL. Inhibition of extracellular protease production was directly proportional to the concentration of the extract. At 100 µg/mL, the ethyl acetate extract had a

P

z

value of 0.84, indicative of mild proteolytic activity. A

P

z

value of 0.94 was observed at a concentration of 200 µg/mL and signified weak proteolytic activity. In conclusion, the extract inhibited the production of extracellular proteases in S. aureus. Further work on the isolation and purification of bioactive compounds responsible for inhibiting the production of extracellular proteases is of importance in the discovery of agents with antivirulent effects on S. aureus.
Long-Time Analysis of a Time-Dependent SUC Epidemic Model for the COVID-19 Pandemic
Authors: Hwang Youngjin , Kwak Soobin , Kim Junseok
Published: October 2021

In this study, we propose a time-dependent susceptible-unidentified infected-confirmed (tSUC) epidemic mathematical model for the COVID-19 pandemic, which has a time-dependent transmission parameter. Using the tSUC model with real confirmed data, we can estimate the number of unidentified infected cases. We can perform a long-time epidemic analysis from the beginning to the current pandemic of COVID-19 using the time-dependent parameter. To verify the performance of the proposed model, we present several numerical experiments. The computational test results confirm the usefulness of the proposed model in the analysis of the COVID-19 pandemic.
Using a Blur Metric to Estimate Linear Motion Blur Parameters
Authors: Askari Javaran Taiebeh , Hassanpour Hamid
Published: October 2021

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.
Design and Implementation of Wireless Environment Monitoring System Based on STM32
Authors: Xilong Qu , Shengzong Liu , Sha Fu , Hong He , Ying Hu , Leyi Xiao
Published: October 2021

This paper first designed the overall scheme of a wireless-based intelligent environment monitoring system; this system is comprised of the following four parts: server part, client part, slave computer part, and master computer part. The master computer is equipped with a wireless transmission module and an LCD module. Slave computers are equipped with temperature-humidity sensing modules and wireless transmission modules. The server side has two major interfaces: data display and server establishment. An Android phone as the client can realize the communication between the mobile APP and server through network communication to obtain environmental data in real time. The hardware part of the system consists of wireless transmission module, temperature and humidity module, smoke module, lighting module, flame module, and LCD module. The software implementation part mainly includes the design and implementation of serial communication, the design and implementation of wireless communication, the design and implementation of collection of data acquisition in each sensor module, and the design and implementation of network communication as well as the design and implementation of display unit. With the cooperation of these four parts, we can automatically sense and control the state of the environment to realize human-computer interaction.
A Privacy-Masking Learning Algorithm for Online Distributed Optimization over Time-Varying Unbalanced Digraphs
Authors: Hu Rong , Zhang Binru
Published: October 2021

This paper investigates a constrained distributed optimization problem enabled by differential privacy where the underlying network is time-changing with unbalanced digraphs. To solve such a problem, we first propose a differentially private online distributed algorithm by injecting adaptively adjustable Laplace noises. The proposed algorithm can not only protect the privacy of participants without compromising a trusted third party, but also be implemented on more general time-varying unbalanced digraphs. Under mild conditions, we then show that the proposed algorithm can achieve a sublinear expected bound of regret for general local convex objective functions. The result shows that there is a trade-off between the optimization accuracy and privacy level. Finally, numerical simulations are conducted to validate the efficiency of the proposed algorithm.
Study on Certain Subclass of Analytic Functions Involving Mittag-Leffler Function
Authors: Alessa Nazek , Venkateswarlu B. , Loganathan K. , Thirupathi Reddy P. , Shashikala A. , Namgyel Ngawang
Published: October 2021

We propose and explore a new subclass of regular functions described by a new derivative operator in this paper. Some coefficient estimations, growth and distortion aspects, extreme points, star-like radii, convexity, Fekete-Szego inequality, and partial sums are derived.
Automatic Recognition of Financial Instruments Based on Anisotropic Partial Differential Equations
Authors: Pan Wenfu , Chen Li , Zhang Ruxing
Published: October 2021

In this paper, anisotropic partial differential equations are used to conduct an indepth study and analysis of automatic recognition of financial bills. Firstly, it obtains the invoices of the group enterprise, uses scanning technology and related image recognition technology to capture, process, compress and slice the paper bill content, and then carries out data identification and verification of the image. It classifies the obtained electronic data information into bills, converts it into electronic information related to bills according to the corresponding categories of the bill template, and stores it in the bill table of the database to achieve the management operation of formatted electronic files. After categorizing the bills according to the electronic information of bills to match the business scenarios, financial journal vouchers can be generated according to the preconfigured voucher templates of the corresponding business scenarios, and the financial journal vouchers are converted into voucher messages using XML technology. Finally, we use agent technology to design middleware for heterogeneous financial systems to realize the function of communicating voucher messages to each other in different business systems. The system automatically extracts the key information of invoices through OCR technology and performs real-time verification and cyclical feedback to the verification results to the suppliers. The system has realized the intelligent management of the power company’s VAT invoices, thus greatly enhancing the efficiency of VAT invoice verification and settlement. The automatic tax invoice recognition system adopts a network structured tax invoice recognition model, which eliminates the cumbersome steps of character decomposition and character classification in traditional OCR character recognition. After several trials, it has obtained better experimental results in terms of recognition accuracy, with an accuracy rate of over 93% in the recognition of tax invoice data set.
Application of Multisensor Information Fusion Technology in the Measurement of Dynamic Machining Errors of Computer Numerical Control (CNC) Machine Tools
Authors: Li Xiaoping , Deng Yonghong , Li Xuezhe
Published: October 2021

A CNC machine tool is process control equipment integrating machine, electricity, and liquid, which makes its fault diagnosis complex and special due to its own advanced, complex, and intelligent characteristics. Traditional diagnostic methods rely on the engineering experience of technical personnel, which incorporates human subjective factors, and can only perform qualitative analysis, resulting in low diagnostic efficiency. And through a single sensor to detect and diagnose the machine tool, the accuracy and credibility of the decision are low, and the system is also weak against interference. In this paper, we first summarize the composition and working principle of CNC machine tools and analyze the working condition signals generated by CNC machine tools and the sensors that collect the signals and decide to use a multisensor multisignal fusion‐based approach to monitor the machine tool status. It is possible to obtain more effective and valuable information from the observed information through multiple sensors so that the goal of fusion can be achieved. In this paper, a multisensor fusion technique based on wavelet transform and neural network fusion is applied to a machine tool condition monitoring system. The theoretical basis of wavelet analysis and neural network is introduced, and the composition of the condition monitoring system and the process of applying multisensor fusion technology based on wavelet analysis and neural network in the condition monitoring system are given. A complete software and hardware system for online monitoring of CNC machine tools is established. In order to improve the accuracy of the mathematical model, the use of a neural network to fit the nonlinear data and the use of coarse set theory to simplify the relevant data can effectively solve the accurate establishment of the mathematical model in the error compensation method. The thermal error compensation method for CNC machine tools is proposed based on rough set theory, ant colony algorithm, and neural network. This paper first investigates the current development of error compensation technology for CNC machining centers, analyzes the various error sources of CNC machine tools, and finds out the influencing factors affecting the errors of CNC machine tools.
[Retracted] Astragalin Protects against Spinal Cord Ischemia Reperfusion Injury through Attenuating Oxidative Stress‐Induced Necroptosis
Authors: Sun Feng , Zhang Haiwei , Shi Jianhui , Huang Tianwen , Wang Yansong
Published: October 2021

Spinal cord ischemia/reperfusion (SCI/R) injury is a devastating complication usually occurring after thoracoabdominal aortic surgery. However, it remains unsatisfactory for its intervention by using pharmacological strategies. Oxidative stress is a main pharmacological process involved in SCI/R, which will elicit downstream programmed cell death such as the novel defined necroptosis. Astragalin is a bioactive natural flavonoid with a wide spectrum of pharmacological activities. Herein, we firstly evaluated the effect of astragalin to oxidative stress as well as the possible downstream necroptosis after SCI/R in mice. Our results demonstrated that astragalin improves the ethological score and histopathological deterioration of SCI/R mice. Astragalin mitigates oxidative stress and ameliorates inflammation after SCI/R. Astragalin blocks necroptosis induced by SCI/R. That is, the amelioration of astragalin to the motoneuron injury and histopathological changes. Indicators of oxidative stress, inflammation, and necroptosis after SCI/R were significantly blocked. Summarily, we firstly illustrated the protection of astragalin against SCI/R through its blockage to the necroptosis at downstream of oxidative stress.
Classroom Resource Optimization of the English Four-Step Model Based on Deep Learning
Authors: Zhao Min , Wang Lu
Published: October 2021

The existing LBL (lecture-based learning) and CBL (case-based learning) teaching modes of English majors in colleges and universities do not contribute to cultivating students’ inquisitive thinking and independent learning ability, whereas the problem-centered PBL (problem-based learning) teaching mode can precisely make up for these shortcomings. This study firstly constructed a PBL “four-step” teaching mode and then realized the innovative application of PBL teaching model in the teaching of English majors in new universities. Then, we designed a method of English classroom resource optimization based on IMOCS-BP neural network, and then, the classroom resource optimization reconstructs the teaching process with unique connotation essence. The experiments show that the BL “four-step” teaching model is designed to achieve the four key elements of “flexible English learning environment, transformed learning the culture, customized content and professional educators”; the designed neural network has optimized the English classroom resources.
Taxonomic Analysis of Oral Microbiome during Orthodontic Treatment
Authors: Campobasso Alessandra , Lo Muzio Eleonora , Battista Giovanni , Ciavarella Domenico , Crincoli Vito , Lo Muzio Lorenzo
Published: October 2021

Background. Orthodontic appliances induce significant changes in the oral microbiome, but this shift in microbial composition has not been well established by the available evidence yet. Objectives. To perform a systematic review of existing literature in order to assess the taxonomic microbial changes in orthodontic patients during Fixed Appliance Treatment (FAT) and Clear Aligner Treatment (CAT), using next-generation sequencing (NGS) technique of the bacterial 16S rRNA gene. Search Methods and Selection Criteria. The search for articles was carried out in PubMed, including articles published in English until May 2021. They included every human study report potentially relevant to the review. Data Collection and Analysis. After duplicate study selection and data extraction procedures according to the PICOS scheme, the methodological quality of the included papers was assessed by the Swedish Council on Technology Assessment in Health Care Criteria for Grading Assessed Studies (SBU) method. Results. The initial search identified 393 articles, 74 of which were selected by title and abstract. After full-text reading, six articles were selected according to inclusion criteria. The evidence quality for all the studies was moderate. Conclusions. Orthodontic treatment seems to transiently affect the composition of subgingival microbiome, although not salivary, maintaining a stable microbial diversity. Different results were found in the shift of microbiome between plaque and saliva, depending on the type of orthodontic treatment. This review should be interpreted with some caution because of the number, quality, and heterogeneity of the included studies.
The Role of RNA Methylation in Regulating Stem Cell Fate and Function-Focus on m6A
Authors: Sun Weiwei , Zhang Bin , Bie Qingli , Ma Na , Liu Na , Shao Zewei
Published: October 2021

The biological role of RNA methylation in stem cells has attracted increasing attention. Recent studies have demonstrated that RNA methylation plays a crucial role in self-renewal, differentiation, and tumorigenicity of stem cells. In this review, we focus on the biological role of RNA methylation modifications including N6-methyladenosine, 5-methylcytosine, and uridylation in embryonic stem cells, adult stem cells, induced pluripotent stem cells, and cancer stem cells, so as to provide new insights into the potential innovative treatments of cancer or other complex diseases.
Bioactive Compounds from Kefir and Their Potential Benefits on Health: A Systematic Review and Meta‐Analysis
Authors: Vieira Carla P. , Rosario Anisio Iuri L. S. , Lelis Carini A. , Rekowsky Bruna Samara S. , Carvalho Anna Paula A. , Rosário Denes Kaic A. , Elias Thaísa A. , Costa Marion P. , Foguel Debora , Conte-Junior Carlos A.
Published: October 2021

Despite evidence of health benefits from kefir administration, a systematic review with meta‐analysis on bioactive compounds associated with these benefits is still absent in the literature. Kefir is fermented milk resulting from the metabolism of a complex microbiota in symbiosis. Recent researches have investigated the bioactive compounds responsible for the preventive and therapeutic effects attributed to kefir. However, differences in functional potential between industrial and artisanal kefir are still controversial. Firstly, we identified differences in the microbial composition among both types of kefir. Available evidence concerning the action of different bioactive compounds from kefir on health, both from in vitro and in vivo studies, was subsequently summarized to draw a primary conclusion of the dose and the intervention time for effect, the producer microorganisms, the precursor in the milk, and the action mechanism. Meta‐analysis was performed to investigate the statistically significant differences (P < 0.05) between intervention and control and between both types of kefir for each health effect studied. In summary, the bioactive compounds more commonly reported were exopolysaccharides, including kefiran, bioactive peptides, and organic acids, especially lactic acid. Kefir bioactive compounds presented antimicrobial, anticancer, and immune‐modulatory activities corroborated by the meta‐analysis. However, clinical evidence is urgently needed to strengthen the practical applicability of these bioactive compounds. The mechanisms of their action were diverse, indicating that they can act by different signaling pathways. Still, industrial and artisanal kefir may differ regarding functional potential—OR of 8.56 (95% CI: 2.27–32.21, P ≤ .001)—according to the observed health effect, which can be associated with differences in the microbial composition between both types of kefir.
Research on Maize Disease Recognition Method Based on Improved ResNet50
Authors: Wang Guowei , Yu Haiye , Sui Yuanyuan
Published: October 2021

In order to solve the problem of accuracy and speed of disease identification in real-time spraying operation in maize field, an improved ResNet50 maize disease identification model was proposed. Firstly, this paper uses the Adam algorithm to optimize the model, adjusts the learning strategy through the inclined triangle learning rate, increases L2 regularization to reduce over fitting, and adopts exit strategy and ReLU incentive function. Then, the first convolution kernel of the ResNet50 model is modified into three 3 x 3 small convolution kernels. Finally, the ratio of training set to verification set is 3 : 1. Through experimental comparison, the recognition accuracy of the maize disease recognition model proposed in this paper is higher than that of other models. The image recognition accuracy in the data set is 98.52%, the image recognition accuracy in the farmland is 97.826%, and the average recognition speed is 204 ms, which meets the accuracy and speed requirements of maize field spraying operation and provides technical support for the research of maize field spraying equipment.
Effects of Reliability Index on Optimal Configuration of Hybrid Solar/Battery Energy System by Optimization Approach: A Case Study
Authors: Liu Hao , Wu Baojia , Maleki Akbar , Pourfayaz Fathollah , Ghasempour Roghayeh
Published: October 2021

Stand-alone hybrid energy systems based on solar and energy storage are an effective option for rural areas to meet the load demand. The objective of the current work is to the optimal configuration of a stand-alone hybrid photovoltaic/battery energy storage system with the help of an efficient metaheuristic algorithm, improved harmony search, to supply electrical of a residential load in Iran. The objective function is a minimization of total life cycle cost (TLCC) subject to the reliability index (loss of load probability). The optimal configurations of hybrid systems are compared in respect of different losses of load probability (0 to 20%). Sensitivity analysis and effects of economic parameters based on photovoltaic and battery prices are carried out to study the possibility of the suggested scheme. The results show that, by increasing the reliability index from 0 to 20%, the optimal number of panels and batteries decreases by 52 and 1202. Also, it is found that the TLCC of the system and cost of system components are increased by decreasing of the reliability index value.
Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer
Authors: Patel Achal , García-Closas Montserrat , Olshan Andrew F. , Perou Charles M. , Troester Melissa A. , Love Michael I. , Bhattacharya Arjun
Published: October 2021

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings.

Significance:

This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.