bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Digital Mining Facing Information Fusion Technology in the Field of National Costume Culture Design
Authors: Yu Min , Cui Rongrong
Published: October 2021

In order to improve the design effect of minority clothing, according to the needs of minority clothing design, this paper uses data mining and Internet of Things technologies to construct an intelligent ethnic clothing design system and builds an intelligent clothing design system that meets customer needs based on the idea of human-computer interaction. In data processing, this paper uses the constraint spectrum clustering algorithm to take the Laplacian matrix and the constraint matrix as input and finally outputs a clustering indicator vector to improve the data processing effect of minority clothing design. Finally, this paper verifies the performance of the system designed in this paper through experiments. From the experimental research, it can be known that the minority clothing design system based on the Internet of Things and data mining constructed in this paper has a certain effect and can effectively improve the minority clothing design effect.
[Retracted] Effects of Pulmonary Surfactant Combined with Noninvasive Positive Pressure Ventilation on KRT‐14 and ET‐1 Levels in Peripheral Blood and Therapeutic Effects in Neonates with Respiratory Distress Syndrome
Authors: Huang Lihan , Liang Hong , Liu Longbin , Lin Yucong , Lin Xinzhu
Published: October 2021

This study is aimed at exploring the effect of pulmonary surfactant (PS) combined with noninvasive positive pressure ventilation on the levels of Keratin‐14 (KRT‐14) and Endothelin‐1 (ET‐1) in peripheral blood and the therapeutic effect of neonatal respiratory distress syndrome (NRDS). Altogether 137 cases of neonates with respiratory distress syndrome treated in our hospital from April 2016 to July 2018 were collected. Among them, 64 cases treated with noninvasive positive pressure ventilation were considered as the control group, and 73 cases treated with PS combined with noninvasive positive pressure ventilation were considered as the observation group. The expression of KRT‐14 and ET‐1 in the two groups was compared. The therapeutic effect, death, complications, and blood gas indexes PaO2, PaCO2, and PaO2/FiO2 in the two groups were compared. Receiver operating characteristic curve (ROC) was applied to analyze the diagnostic value of KRT‐14 and ET‐1 in the therapeutic effect of NRDS. The effective rate of the observation group was higher than that of the control group. After treatment, PaO2 and PaO2/FiO2 in both groups were notably higher than that before treatment, while PaCO2 was notably lower than that before treatment. And after treatment, the levels of PaO2 and PaO2/FiO2 in the observation group were remarkably higher than that in the control group; PaCO2 was notably lower than that in the control group. After treatment, the levels of KRT‐14 and ET‐1 in the two groups were remarkably lower than those before treatment, and the levels of KRT‐14 and ET‐1 in the observation group were considerably lower than those in the control group after treatment. ROC curve showed that the area under the curve (AUC) of KRT‐14 was 0.791, and the AUC of ET‐1 was 0.816. PS combined with noninvasive positive pressure ventilation can notably improve the therapeutic effect of NRDS. KRT‐14 and ET‐1 levels may be potential therapeutic diagnostic indicators.
A Survey of Few-Shot Learning: An Effective Method for Intrusion Detection
Authors: Duan Ruixue , Li Dan , Tong Qiang , Yang Tao , Liu Xiaotong , Liu Xiulei
Published: October 2021

Few-shot learning (FSL) is a core topic in the domain of machine learning (ML), in which the focus is on the use of small datasets to train the model. In recent years, there have been many important data-driven ML applications for intrusion detection. Despite these great achievements, however, gathering a large amount of reliable data remains expensive and time-consuming, or even impossible. In this regard, FSL has been shown to have advantages in terms of processing small, abnormal data samples in the huge application space of intrusion detection. FSL can improve ML for scarce data at three levels: the data, the model, and the algorithm levels. Previous knowledge plays an important role in all three approaches. Many promising methods such as data enrichment, the graph neural network model, and multitask learning have also been developed. In this paper, we present a comprehensive review of the latest research progress in the area of FSL. We first introduce the theoretical background to ML and FSL and then describe the general features, advantages, and main methods of FSL. FSL methods such as embedded learning, multitask learning, and generative models are applied to intrusion detection to improve the detection accuracy effectively. Then, the application of FSL to intrusion detection is reviewed in detail, including enriching the dataset by extracting intermediate features, using graph embedding and meta-learning methods to improve the model. Finally, the difficulties of this approach and its prospects for development in the field of intrusion detection are identified based on the previous discussion.
Target Recognition of SAR Images Based on SVM and KSRC
Authors: Zhao Haiyan
Published: October 2021

A synthetic aperture radar (SAR) target recognition method combining linear and nonlinear feature extraction and classifiers is proposed. The principal component analysis (PCA) and kernel PCA (KPCA) are used to extract feature vectors of the original SAR image, respectively, which are classical and reliable feature extraction algorithms. In addition, KPCA can effectively make up for the weak linear description ability of PCA. Afterwards, support vector machine (SVM) and kernel sparse representation‐based classification (KSRC) are used to classify the KPCA and PCA feature vectors, respectively. Similar to the idea of feature extraction, KSRC mainly introduces kernel functions to improve the processing and classification capabilities of nonlinear data. Through the combination of linear and nonlinear features and classifiers, the internal data structure of SAR images and the correspondence between test and training samples can be better investigated. In the experiment, the performance of the proposed method is tested based on the MSTAR dataset. The results show the effectiveness and robustness of the proposed method.
Neural Network‐Oriented Big Data Model for Yoga Movement Recognition
Authors: Wang Hui
Published: October 2021

The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region‐Convolutional Neural Network (MR‐CNN)‐ based method for yoga movement recognition based on the image task of yoga movement recognition. The improved MR‐CNN model is based on the framework and structure of the region‐convolutional network, which proposes a certain number of candidate regions for the image by feature extraction and classifies them, then outputs these regions as detected bounding boxes, and does mask prediction for the candidate regions using segmentation branches. The improved MR‐CNN model uses an improved deep residual network as the backbone network for feature extraction, bilinear interpolation of the extracted candidate regions using Region of Interest (RoI) Align, followed by target classification and detection, and segmentation of the image using the segmentation branch. The model improves the convolution part in the segmentation branch by replacing the original standard convolution with a depth‐separable convolution to improve the network efficiency. Experimentally constructed polygon‐labeled datasets are simulated using the algorithm. The deepening of the network and the use of depth‐separable network improve the accuracy of detection while maintaining the reliability of the network and validate the effectiveness of the improved MR‐CNN.
Parameter Estimation of the Photovoltaic System Using Bald Eagle Search (BES) Algorithm
Authors: Nicaire Ndongmo Fotsa , Steve Perabi Ngoffe , Salome Ndjakomo Essiane , Grégroire Abessolo Ondoua
Published: October 2021

The global demand for renewable energy is growing, and one of the proposed solutions to this energy crisis is the use of photovoltaic systems. So far, they are a reliable solution, as they are nonpolluting and can be used almost anywhere on the planet. However, the design and development of more efficient photovoltaic cells and modules require an accurate extraction of their intrinsic parameters. Up to date, metaheuristic algorithms have proven to be the best methods to obtain accurate values of these intrinsic parameters. Hence, to extract these parameters reliably and accurately, this paper presents an optimization method based on the principle of bald eagle search (BES) during fish hunting. This search is divided into three steps: in the first stage (space selection), the eagle selects the space with the largest number of prey; in the second stage (space search), the eagle moves into the selected space to search for prey; in the third stage (dive), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Thus, we used the proposed BES algorithm to determine the parameters of the single-diode model (SDM), the double-diode model (DDM), and the PV modules. This algorithm converges very quickly and gives a root mean square error (RMSE) of

9.8602
e
−
04
 

for the single-diode model and

9.8248
e
−
4

for the dual-diode model. The results obtained show that the proposed algorithm is more efficient than the other methods available in the literature, in terms of the better accuracy of the results obtained. The good harmony of the I-V and P-V characteristic curve of the calculated parameters with that of the measured data from a PV module/cell data sheet proves that the proposed BES should be used among the methods provided in the literature for the identification of PV solar cell parameters.
Social Effect Analysis of Intelligent Sports Based on Principal Component Analysis and Fuzzy Control
Authors: Chen Xiaobo
Published: October 2021

In order to explore the social effects of intelligent sports systems, this paper combines principal component analysis technology and fuzzy control technology to construct an intelligent sports social effect analysis system. The original fuzzy data is expressed linearly by structural elements, and the original fuzzy data matrix is divided into a main data matrix part and an error data matrix part. According to the principal component analysis method of fuzzy data represented by structural elements, this paper studies the principal component analysis method of interval data using the left end point matrix, right end point matrix, and midpoint matrix of interval data. In addition, this article uses principal component analysis and fuzzy control to study the response of the intelligent motion system to the masses and conducts experiments to analyze the social effects. It can be seen from experimental research that the intelligent sports system constructed in this article has a high degree of satisfaction of the masses, which proves that the intelligent sports have a certain social effect.
Scaling Agile Adoption Motivators from Management Perspective: An Analytical Hierarchy Process Approach
Authors: Jan Rooh Ullah , Usman Muhammad , Abrar Muhammad Faisal , Ullah Najeeb , Asshad Muhammad , Ali Sikandar
Published: October 2021

Agile methodology has been noted as the mostly used method for software development over the past few years. This methodology guarantees the rapid delivery of software products with lower cost and greater customer contentment. The adoption of agile methods was initially applied in the software development industry for single, small project teams following the principles of Agile Manifesto. The intense need of the time is to identify from management perspective the motivators that can be focused on to adopt agile methodologies in large-scale projects. Therefore, the main purpose of the study is to develop from management perspective a priority-based taxonomy of motivators that helps in scaling agile methodologies in large-scale projects. The taxonomy is developed in two steps. In the first step, data are collected from a survey and open literature. In the second step, the analytical hierarchy process (AHP) is implemented on the collected data that produced the proposed priority-based taxonomy of the motivators. With the help of AHP model, the 21 motivators of successful project management are categorized into four main categories, i.e., “organization management,” “team,” “customer and technology,” and “process.” Furthermore, the AHP analysis is used to rank the motivators and their respective categories in order of their importance. The findings of this study are further reviewed and validated by the agile experts in other organizations. The proposed priority-based taxonomy of motivators can be used as a handbook that gives guidelines on scalability of agile methodologies to large projects, which helps in successful management of large-scale projects.
Effect of Sizing Agents on Surface Properties of T800 Grade CF and Thermal Aging Time on Mechanical Properties of T800 Grade CF/Epoxy Composites
Authors: Yang Tian , Zhao Yan , Liu Hansong , Xiong Shu
Published: October 2021

This paper investigates the effect of sizing agent molecular weight on carbon fiber (CF) surface properties and the effect of thermal aging time on mechanical properties of CF/epoxy composites. The surface properties of three CCF800 CF samples with varying sizing agent molecular weight were characterized by surface morphology, surface roughness, chemical functional groups, and element composition. The results showed that the sample with low molecular weight exhibited low roughness and high proportion of activated carbon atoms. The flexural strength, flexural modulus, and interlaminar shear strength of CCF800/5228 composites were measured at 25°C and 150°C by thermal ageing time 0, 100 h, 250 h, 500 h, and 1000 h. The results showed that the thermal aging time up to 1000 h had little effect on the flexural modulus, and the interlaminar shear performance at 150°C showed a trend of increasing at the beginning and then decreasing.
The Supply Chain Financial Supervision Mechanism of the Internet of Things Based on the Integration of RFID and Wireless Sensor Network
Authors: Zhu Qingyun , Cai Ying
Published: October 2021

Based on the fusion of wireless sensor modules and UHF RFID readers, this paper develops a UHF RFID reading and writing system based on wireless sensing technology and designs a communication interface circuit that integrates wireless sensing and RFID technology. The financial supervision mechanism of the Internet of Things compatible with these two technologies improves the communication distance limitation of the RFID system and improves the flexibility of the RFID reading and writing system. Secondly, in view of the security issues of WSN small and medium data distribution, this paper proposes a lightweight small data supply chain financial supervision program. The solution adopts two‐way authentication technology based on the improvement of the Internet of Things supply chain, the negotiation of the Internet of Things supply chain, and a lightweight encryption algorithm, which realizes the secure communication between ordinary nodes and gateway nodes, and can more effectively solve the problem of small and medium data distribution in WSN to resist the deficiencies of the financial supervision mechanism; compared with existing solutions of the same magnitude, it has higher security and efficiency. Experimental simulation shows that an important problem in the converged network is the imbalance of node energy consumption. In order to reduce the negative impact of the imbalance of energy consumption on the network, this article compares the two for direct transmission protocol and minimum energy multihop routing protocol. From the perspective of supply chain process performance, based on the proposed technical impact framework, it analyzes how companies actually carry out ID application work, making the research from general to individual, from theory to practice. The network energy consumption calculation method under this protocol improves an improved network architecture model that increases the communication scale. The test results show that the technical impact analysis framework established in this article based on the theory of the Internet of Things and the advantages of RFID technology can analyze the automation benefits, information benefits, and transformative benefits brought by automatic identification technology to the supply chain and introduce complementary resources and potential factors. It provides a theoretical basis and operation method for measuring the impact of RFID on the performance of the financial supervision process based on the Internet of Things supply chain in practical applications.
IFC and Monitoring Database System Based on Graph Data Models
Authors: Gradišar Luka , Dolenc Matevž
Published: October 2021

An efficient database management system that supports the integration and interoperability of different information models is a foundation on which the higher levels of cyber‐physical systems are built. In this paper, we address the problem of integrating monitoring data with building information models through the use of the graph data management system and the IFC standard (Industry Foundation Classes) to support the need for interoperability and collaborative work. The proposed workflow describes the conversion of IFC models into a graph database and the connection with data from sensors, which is then validated using the example of a bridge monitoring system. The presented IFC and sensor graph data models are structurally flexible and scalable to meet the challenges of smart cities and big data.
Bone Organic-Inorganic Phase Ratio Is a Fundamental Determinant of Bone Material Quality
Authors: Luo Yunhua , Amromanoh Ogheneriobororue
Published: October 2021

Background. Bone mineral density is widely used by clinicians for screening osteoporosis and assessing bone strength. However, its effectiveness has been reported unsatisfactory. In this study, it is demonstrated that bone organic-inorganic phase ratio is a fundamental determinant of bone material quality measured by stiffness, strength, and toughness. Methods and Results. Two-hundred standard bone specimens were fabricated from bovine legs, with a specialized manufacturing method that was designed to reduce the effect of bone anisotropy. Bone mechanical properties of the specimens, including Young’s modulus, yield stress, peak stress, and energy-to-failure, were measured by mechanical testing. Organic and inorganic mass contents of the specimens were then determined by bone ashing. Bone density and organic-inorganic phase ratio in the specimens were calculated. Statistical methods were applied to study relationships between the measured mechanical properties and the organic-inorganic phase ratios. Statistical characteristics of organic-inorganic phase ratios in the specimens with top material quality were investigated. Bone organic-inorganic phase ratio had strong Spearman correlation with bone material properties. Bone specimens that had the highest material quality had a very narrow scope of organic-inorganic phase ratio, which could be considered as the “optimal” ratio among the tested specimens. Conclusion. Bone organic-inorganic phase ratio is a fundamental determinant of bone material quality. There may exist an “optimal” ratio for the bone to achieve top material quality. Deviation from the “optimal” ratio is probably the fundamental cause of various bone diseases. This study suggests that bone organic-inorganic phase ratio should be considered in clinical assessment of fracture risk.
Numerical Solution of Nonlinear Stochastic Itô–Volterra Integral Equations Driven by Fractional Brownian Motion Using Block Pulse Functions
Authors: Deng Mengting , Jiang Guo , Ke Ting
Published: October 2021

This paper presents a valid numerical method to solve nonlinear stochastic Itô–Volterra integral equations (SIVIEs) driven by fractional Brownian motion (FBM) with Hurst parameter

H
∈

1
/
2

,
1

. On the basis of FBM and block pulse functions (BPFs), a new stochastic operational matrix is proposed. The nonlinear stochastic integral equation is converted into a nonlinear algebraic equation by this method. Furthermore, error analysis is given by the pathwise approach. Finally, two numerical examples exhibit the validity and accuracy of the approach.
A New Type of Sturm-Liouville Equation in the Non-Newtonian Calculus
Authors: Goktas Sertac
Published: October 2021

In mathematical physics (such as the one-dimensional time-independent Schrödinger equation), Sturm-Liouville problems occur very frequently. We construct, with a different perspective, a Sturm-Liouville problem in multiplicative calculus by some algebraic structures. Then, some asymptotic estimates for eigenfunctions of the multiplicative Sturm-Liouville problem are obtained by some techniques. Finally, some basic spectral properties of this multiplicative problem are examined in detail.
Pancreatic Injury in Patients with SARS-Cov-2 (COVID-19) Infection: A Retrospective Analysis of CT Findings
Authors: Grusova Gabriela , Bruha Radan , Bircakova Bianka , Novak Matej , Lambert Lukas , Michalek Pavel , Tomas Grus , Burgetova Andrea
Published: October 2021

Objective. To determine the association between COVID-19 infection and peripancreatic changes on CT as a sign of acute pancreatic injury. Methods. Retrospective analysis of CT examinations in patients with confirmed COVID-19 infection yielded 103 instances. An age- and gender-matched cohort of patients without COVID-19 was found. CT examinations were evaluated for peripancreatic stranding or edema, fluid collection, or necrosis, without any other explanation. Depicted pulmonary parenchyma was evaluated for possible COVID-19-related changes. Clinical and laboratory data were retrieved from the clinical database. Results. Peripancreatic fat stranding (

n
=
8

) or fluid collection (

n
=
2

) without any other cause was found in 10 (10%) patients. Abdominal complaints were reported in 4 (40%) patients. Elevated serum amylase or lipase levels were documented in 5 (50%) patients who also satisfied the diagnostic criteria for acute pancreatitis. From the study sample of 103 patients with COVID-19, pulmonary parenchyma was depicted in 102 (99%), and from these, 57 (55%) had an evidence of pulmonary changes compatible with COVID-19 pneumonia. This proportion was not significantly different between patients with and without peripancreatic changes (

p
=
0.35

). In the matched cohort, we found peripancreatic changes in 2 (2%,

p
=
0.033

) patients. Patients with pancreatic injury and elevated amylase levels were more likely to require orotracheal intubation (35% vs. 12%,

p
=
0.021

). Conclusions. We showed that the prevalence of peripancreatic stranding or fluid collection is higher in patients diagnosed with COVID-19 infection compared to an age- and gender-matched cohort. Patients with pancreatic injury and elevated amylase levels are more likely to require orotracheal intubation. Our findings corroborate the link between COVID-19 infection and pancreatic injury from the perspective of imaging.
General Coastline Extraction Based on an Improved Active Contour Model in Jiaozhou Bay, Qingdao, China, from 1990 to 2018 Using Landsat Satellite Images
Authors: Dong Jie , Fu Jiani , Guan Yong , Liu Haisong , Wang Qing , Hao Ming
Published: October 2021

The coastline is located at the junction of the sea and the land, and it is essential for ecological environment. However, most existing methods can extract the coastline with obvious boundaries and cannot obtain the general coastline, including an intertidal zone and salt field. Accordingly, a new general coastline extraction method is proposed on the basis of an improved active contour model to extract the general coastline from remote sensing images. An improved active contour model was proposed to extract the water area by introducing aiming energy of water from the Modified Normalized Difference Water Index information. Then, mathematical morphology was applied to obtain the seawater area based on the extracted water area. Finally, the coastline was refined and generated by the improved active contour model in a buffer zone of the seawater boundary. Landsat images over Jiaozhou Bay in Shandong Province, China, from 1990 to 2018 were used to extract the general coastline. Results demonstrate that the proposed method can effectively extract the general coastline, which is close to the reference coastline. The length of the coastline decreased from 234.64 km in 1990 to 221.21 km in 2000. This value significantly increased to 255.05 km from 2000 to 2010. The main reason is that Hongdao Island merged with the mainland due to reclamation. The length of the coastline slightly decreased by approximately 12 km from 2010 to 2018 due to environmental protection measures and the reclamation prohibition.
The Identified Hub Gene GlcN in Osteoarthritis Progression and Treatment
Authors: Liu Jingsheng , Dong Xiaoli , Liu Yining , Wang Kai , Lei Shuanhu , Yang Mingxuan , Yue Haiyuan , Feng Haijun , Feng Kai , Li Kang , Zhou Jianwei , Chen Yanqiang , Du Wenjia , Kang Xuewen , Xia Yayi
Published: October 2021

Background. As a chronic disease, osteoarthritis has caused great trouble to the health of middle-aged and elderly people. Studies have shown that glucosamine (GlcN) can be used to abate the progression and improve this disease. Based on this point of view, we try to verify the connection between GlcN and osteoarthritis and find more effective biomarkers. Methods. We downloaded the GSE72575 data set from the GEO database, and used the R language to perform DEG analysis on the gene expression profile of the samples. Next, the GO function and the KEGG signaling pathways were analyzed through the DAVID database, and then, the KEGG pathways enriched in the gene set were analyzed based on GSEA. Then, the PPI network of DEGs was constructed based on the STRING online database, and finally, the hub genes were selected by Cytoscape. Results. Three GlcN-treated MH7A cell treatment groups and 3 control groups in the GSE72575 data set were studied. Through analysis, there were 52 DEGs in these samples. Then, through GO, KEGG, and GSEA, regulation of endoplasmic reticulum stress-induced intrinsic apoptotic signaling pathway, FoxO signaling pathway, JAK-STAT signaling pathway, PI3K-Akt signaling pathway, TGF-beta signaling pathway, and ECM receptor interaction were involved in the regulatory mechanisms of the osteoarthritis pathogenesis. After that, the hub genes IL6 and DDIT3 were identified through PPI network construction and analysis. And it was found that IL6 was lowly expressed in the group with GlcN-treated MH7A cells, while DDIT3 was highly expressed. Conclusion. The above results provide a basis for GlcN to participate in the treatment of osteoarthritis and a possibility for finding effective therapeutic targets.
Technoeconomic Assessment Based on Active Context-Knowledge Orchestration for Power Internet of Things
Authors: Zheng Yuanshuo , Sun Shujuan , Li Chenyang , Luo Jingtang , Dong Jiuling , Wang Yudong , Yang Xiaolong
Published: October 2021

Power Internet of Things (abbreviated as PIoT) is the information infrastructure to provide ubiquitous perception ability for smart grid (abbreviated as SG). To better deploy and utilize PIoT, its perception ability must be comprehensively assessed in terms of technical performance and economic benefits. However, at present, there is no assessment framework for PIoT due to the high diversity and heterogeneousness of SG scenarios. Additionally, there is information overlap between metrics in the assessment framework. The assessment model which could remove redundant information between metrics and simplify the assessment framework is an urgent demand to improve the effectiveness and timeliness of assessment. Consequently, first, aiming at the power system requirements of complex and diverse, a general assessment framework is put forward to assess the ability of PIoT in terms of technology and economy. Next, the requirement characteristics of power distribution scenario (abbreviated as PDS) are precisely analyzed with active context-knowledge orchestration technology. The general assessment framework is instantiated to build an instantiation assessment scheme in PDS. Moreover, an assessment model is established based on the instantiation assessment scheme to assess the efficiency of PIoT in Beijing. Finally, the assessment model is further refined with the machine learning technology to improve the efficiency of assessment. This refinement model achieves the extraction of 4-dimensional metrics from 23-dimensional metrics for assessment and finally improves assessment efficiency by 82.6%.
q‐Rung Orthopair Fuzzy Rough Einstein Aggregation Information‐Based EDAS Method: Applications in Robotic Agrifarming
Authors: Ashraf Shahzaib , Rehman Noor , Hussain Azmat , AlSalman Hussain , Gumaei Abdu H.
Published: October 2021

The main purpose of this manuscript is to present a novel idea on the q‐rung orthopair fuzzy rough set (q‐ROFRS) by the hybridized notion of q‐ROFRSs and rough sets (RSs) and discuss its basic operations. Furthermore, by utilizing the developed concept, a list of q‐ROFR Einstein weighted averaging and geometric aggregation operators are presented which are based on algebraic and Einstein norms. Similarly, some interesting characteristics of these operators are initiated. Moreover, the concept of the entropy and distance measures is presented to utilize the decision makers’ unknown weights as well as attributes’ weight information. The EDAS (evaluation based on distance from average solution) methodology plays a crucial role in decision‐making challenges, especially when the problems of multicriteria group decision‐making (MCGDM) include more competing criteria. The core of this study is to develop a decision‐making algorithm based on the entropy measure, aggregation information, and EDAS methodology to handle the uncertainty in real‐word decision‐making problems (DMPs) under q‐rung orthopair fuzzy rough information. To show the superiority and applicability of the developed technique, a numerical case study of a real‐life DMP in agriculture farming is considered. Findings indicate that the suggested decision‐making model is much more efficient and reliable to tackle uncertain information based on q‐ROFR information.
Exploring the Effect of Jiawei Buguzhi Pills on TGF-β-Smad Pathway in Postmenopausal Osteoporosis Based on Integrated Pharmacological Strategy
Authors: Yuan Xiao , Wu Yonghe , Yang Kailin , Liu Huiping , Zhang Guomin
Published: October 2021

Objective. To explore the effect of Jiawei Buguzhi Pills (JWBGZP) on the TGF-β-Smad pathway in postmenopausal osteoporosis (PMO) based on integrated pharmacological strategy. Method. The ETCM database was used to collect JWBGZP. GeneCards and OMIM databases were utilized to obtain PMO-related genes. Cytoscape was used for network construction and analysis, and DAVID was used for GO and KEGG enrichment analysis of key targets. Animal experiments and cell experiments were conducted to further explore the mechanism. The bone mass density was detected by dual-energy X-ray bone densitometer. The TGF-β1 and Smad4 mRNA in bone tissue were detected by RT-qPCR. The TGF-β1 and Smad4 protein in bone tissue were detected by the western blot. The TGF-β1 and Smad4 protein in osteoblasts were determined by immunohistochemistry. Result. A total of 721 JWBGZP potential targets and 385 PMO-related genes were obtained. The enrichment analysis showed that JWBGZP may regulate the TGF-beta signaling pathway, oxidation-reduction process, aging, response to hypoxia, response to ethanol, negative regulation of cell proliferation, PI3K-Akt, HIF-1, and other signaling pathways. The animal experiments showed that compared with the model group, the femoral bone mineral density and lumbar bone mineral density of the JWBGZP group increased (P < 0.05); the expression levels of TGF-β1 and Smad mRNA and proteins in the JWBGZP group were significantly higher (P < 0.05). The cell experiment results showed a large number of osteoblast stained blue-purple and orange-red calcified nodules. The expression levels of TGF-β1 and Smad proteins in the JWBGZP group were significantly higher than those in the blank control group and the sham operation group, and the protein expression levels in the model group were the lowest (P < 0.05). Conclusion. JWBGZP may be involved in PI3K-Akt, HIF-1, estrogen, prolactin, and other signaling pathways and regulate MAPK1, AKT1, PIK3CA, JAK2, and other gene targets, regulate bone metabolism, and thereby treat PMO.
Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable?
Authors: Bu Qingwei , Li Qingshan , Liu Yun , Cai Chun
Published: October 2021

Assessing the ecotoxicity of pharmaceuticals is of urgent need due to the recognition of their possible adverse effects on nontarget organisms in the aquatic environment. The reality of ecotoxicity data scarcity promotes the development and application of quantitative structure activity relationship (QSAR) models. In the present study, we aimed to clarify whether a QSAR model of ecotoxicity specifically for pharmaceuticals is needed considering that pharmaceuticals are a class of chemicals with complex structures, multiple functional groups, and reactive properties. To this end, we conducted a performance comparison of two previously developed and validated QSAR models specifically for pharmaceuticals with the commonly used narcosis toxicity prediction model, i.e., Ecological Structure Activity Relationship (ECOSAR), using a subset of pharmaceuticals produced in China that had not been included in the training datasets of QSAR models under consideration. A variety of statistical measures demonstrated that the pharmaceutical specific model outperformed ECOSAR, indicating the necessity of developing a specific QSAR model of ecotoxicity for the active pharmaceutical contaminants. ECOSAR, which was generally used to predict the baseline or the minimum toxicity of a compound, generally underestimated the ecotoxicity of the analyzed pharmaceuticals. This could possibly be because some pharmaceuticals can react through specific modes of action. Nonetheless, it should be noted that 95% prediction intervals spread over approximately four orders of magnitude for both tested QSAR models specifically for pharmaceuticals.
Green Synthesis of New Pyrrolo [1,2-a] quinoxalines as Antiproliferative Agents in GPER-expressing Breast Cancer Cells
Authors: Carullo Gabriele , Mazzotta Sarah , Giordano Francesca , Aiello Francesca
Published: October 2021

4,5-Dihydropyrrolo [1,2-a]quinoxalines are interesting druggable scaffolds, with multifaceted biological properties, including anticancer properties targeting the G protein-coupled estrogen receptor 1 (GPER). In this work, the synthesis and preliminary antiproliferative activity of a small set of new 4,5-dihydropyrrolo[1,2-a]quinoxalines (18-20) and pyrrolo[1,2-a]quinoxalines (21, 22) has been reported, inspired by known antiproliferative agents (G-1, G-15, and G-36). The synthesis of the pyrroloquinoxalinic core was employed following the Pictet–Spengler reaction, using the surfactant p-dodecylbenzene sulphonic acid (p-DBSA), as catalyst. It demonstrated efficiency in the catalysis of the 4-phenylpyrrole [1,2-a] quinoxaline type compound formation in mild solvents such as water, ethanol, and hydroalcoholic solutions. In addition, the reactions proceeded in a short time (between 15 and 120 minutes) at room temperature and with high yields. The in vitro MTT assays showed that the presence of isopropyl groups furnished promising antiproliferative compounds. Although, the acetyl group provided also antiproliferative effects, breaking down its responsibility in the GPER transactivation. Nevertheless, it is possible to conclude that the 4,5-dihydropyrrolo[1,2-a]quinoxalines remain a feasible scaffold to develop anticancer agents against GPER-expressing cells.
A New (3, 6)-Connected Heterometallic Coordination Polymer: Therapeutic and Protective Effect on Ischemic Cerebral Infarction
Authors: Ma Jia , Wang Jianer , He Yuping , Huang Yiwei , Chen Yejun
Published: October 2021

Solvothermal reaction of 4,4’,4’-nitrilotribenzoic acid (H3TCA) with Ca(NO3)2·4H2O and Cd(NO3)2·4H2O successfully afforded a heterometallic compound, that is, [Cd2Ca(TCA)2(DMA)2(H2O)3]n·2n(DMA) (1, DMA = N,N’-dimethylacetamide). The structure of 1 was further characterized through EA, diffraction of single crystal X-ray, and thermogravimetric analysis (TGA) together with powder X-ray diffraction (PXRD). Moreover, complex 1’s luminescent performance in solid state was also discussed at environmental temperature. The applications on the ischemic cerebral infarction therapy were evaluated, and the specific mechanism was investigated at the same time. Firstly, the hypersensitivity C-reactive protein (hs-CRP) content in human brain microvascular endothelial cell (HBMEC) was measured with real-time reverse transcription-polymerase chain reaction (RT-PCR). Then, interleukin-18 and interleukin-1β content released through the HBMEC was detected with enzyme-linked immunosorbent assay (ELISA).
A Fair and Privacy-Preserving Image Trading System Based on Blockchain and Group Signature
Authors: Wang Le , Liu Xuefeng , Lin Xiaodong
Published: October 2021

With the rise of digital images in our daily lives, there is a growing need to provide an image trading market where people can monetize their images and get desired images at prices that fit their budget. Those images are usually uploaded and stored onto centralized image trading service providers’ servers and the transactions for image trading are processed by these providers. Unfortunately, transaction unfairness and users’ privacy breaches have become major concerns since the service providers might be untrusted and able to manipulate image trading prices and infer users’ private information. Recently, several approaches have been proposed to address the unfairness issue by using the decentralized ledger technique and smart contract, but users’ privacy protection is not considered. In this paper, we propose a fair and privacy-preserving protocol that supports image fair exchange and protect user privacy. In particular, we exploit blockchain and Merkle tree to construct a fair image trading protocol with low communication overhead based on smart contract, which serves as an external judge that resolves disputes between buyers and sellers in image transactions. Moreover, we extend a popular short group signature scheme to protect users’ identity privacy, prevent linkability of transactions from being inferred, and ensure traceability of malicious users who may sell fake images and/or refuse to pay. Finally, we design and build a practical and open-source image trading system to evaluate the performance of our proposed protocol. Experimental results demonstrate its effectiveness and efficiency in real-world applications.
Recent Updates of Natural and Synthetic URAT1 Inhibitors and Novel Screening Methods
Authors: Chen Ying , You Ruya , Wang Ke , Wang Yousheng
Published: October 2021

Human urate anion transporter 1 (hURAT1) is responsible for the reabsorption of uric acid in the proximal renal tubules and is a promising therapeutic target for treating hyperuricemia. To mitigate the side effects of URAT1-targeted clinical agents such as benzbromarone, there is significant interest in discovering new URAT1 inhibitors and developing technology that can evaluate URAT1 inhibition. This review summarizes the methods for assay of URAT1 inhibition and the progress on the discovery of natural and synthetic URAT1 inhibitors in the past five years.
NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images
Authors: Liao Xin , Huang Qin , Zheng Xin
Published: October 2021

As a rare malignant tumor, cervical neuroendocrine cancer (NEC) is difficult in diagnosis even for experienced pathologists. A computer-assisted diagnosis may be helpful for the improvement of diagnostic accuracy. Nevertheless, the computer-aided pathological diagnosis has to face a great challenge that the hundred-million-pixels or even gig-pixels whole slide images (WSIs) cannot be applied directly in the existing deep convolution network for training and analysis. Therefore, the construction of a neural network to realize the automatic screening of cervical NEC is challenging; meanwhile, as far as we know, little attention has been paid to this field. In order to address this problem, here we present a multiple-instance learning method for automatic recognition of cervical NEC on pathological WSI, which consists of the Sliding Detector module and Lesion Analyzer module. A pathological WSI dataset, which is composed of 84 NEC cases and 216 NEC-free cases from the Pathological Department of West China Second University Hospital, is applied to evaluate the performance of the method. The experimental results show that the recall rate, accuracy rate, and precision rate of our method for automatic recognition are 92.9%, 92.7%, and 83.0%, respectively, demonstrating the effectiveness and the potential in clinical practice. The application of this method in computer-assisted pathological diagnosis is expected to decrease the misdiagnosis as well as the false diagnosis of rare cervical NEC, and, consequently, improve the therapeutic effect of cervical cancers.
Equipment Quality Data Integration and Cleaning Based on Multiterminal Collaboration
Authors: Ji Cui-Bin , Duan Gui-Jiang , Zhou Jun-Yan , Xuan Wei-Jie
Published: October 2021

With the advancement of digital manufacturing technology, data‐driven quality management is getting more and more attention, and it is developing rapidly under the impetus of technology and management. Quality data are growing exponentially with the help of increasingly interconnected devices and IoT (Internet of Things technologies). Aiming at the problems of insufficient quality data acquisition and poor data quality of complex equipment, the research on quality data integration and cleaning based on digital total quality management is carried out. The data integration architecture of complex equipment quality based on multiterminal collaboration is constructed. The architecture integrates a variety of integration methods and standards, such as XML, OPC‐UA, and QIF protocol. Then, to unify the data view, a cleaning method of complex equipment quality data based on the combination of edit distance and longest common subsequence similarity calculation is proposed, and its effectiveness is verified. It provides the basis for the design of the digital total quality management system of complex equipment.
Determination of Halogens by Ion Chromatography in Edible Mushrooms after Microwave-Induced Combustion for Sample Preparation
Authors: Coelho Junior Gilberto Silva , Rondan Filipe Soares , Hartwig Carla Andrade , Santos Rafael Francisco , Mello Paola Azevedo , Mesko Marcia Foster
Published: October 2021

In this study, the microwave-induced combustion (MIC) method was evaluated for the sample preparation of the most consumed mushroom species in Brazil (Champignon, Shiitake, and Shimeji) and further halogen determination by ion chromatography (IC). For this, sample mass, combustion aid mass, and absorbing solution (H2O and 50 mmol·L−1, 100 mmol·L−1, or 150 mmol·L−1 NH4OH) were evaluated. Bromine and iodine concentrations, determined by IC, were lower than the limits of detection (LODs, Br: 6 mg·kg−1 and I: 24 mg·kg−1). Inductively coupled plasma mass spectrometry (ICP-MS) was also used for Br and I determination, and the LODs were lower (Br: 0.066 mg·kg−1 and I: 0.014 mg·kg−1) than those obtained by IC. Concentrations of Cl, obtained by IC, ranged from 523 mg·kg−1 to 13053 mg·kg−1 with LOD of 40 mg·kg−1. In turn, Br and I concentrations, obtained by ICP-MS, ranged from 2.49 mg·kg−1 to 5.50 mg·kg−1 and from <0.014 mg·kg−1 to 0.047 mg·kg−1, respectively. Fluorine concentrations, determined by IC, were always lower than LOD (23 mg·kg−1). The trueness of the proposed methods was evaluated by recovery tests using standard solutions and a reference material (RM NIST 8435). When using the standard solution, recoveries ranged from 95% to 103% for halogen determination by IC and from 105% to 109% for Br and I determination by ICP-MS. When using the RM, recoveries of 102% for Cl by IC and of 87% and 86% for Br and I by ICP-MS, respectively, were obtained.
Dimethyl Fumarate Ameliorates Nucleus Pulposus Cell Dysfunction through Activating the Nrf2/HO-1 Pathway in Intervertebral Disc Degeneration
Authors: Wang Ruihong , Luo Dawei , Li Zhiwei , Han Huimin
Published: October 2021

Background. Oxidative stress, inflammation, and nucleus pulposus cells (NPCs) apoptosis are involved in pathogenesis of intervertebral disc (IVD) degeneration (IVDD). Dimethyl fumarate (DMF) has been found to effectively depress oxidative stress and inflammation via the Nrf2 pathway. Hence, this project was designed to explore the underlying mechanisms of how DMF protects NPCs from damage by LPS challenge. Methods and Results. CCK8 assay and flow cytometry of apoptosis indicated that DMF treatment attenuated LPS-induced NPC damage. Western blot analysis demonstrated that DMF enhanced the expressions of nuclear factor-erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) in LPS-challenged NPCs. DMF treatment significantly decreased the accumulation of ROS, downregulated inflammatory cytokines (p-NF-κB, IL-1β, and TNF-α), and ER stress-associated apoptosis proteins (Bip, calpain-1, caspase-12, caspase-3, and Bax) in LPS-challenged NPCs. The level of antiapoptotic protein Bcl-2 was promoted by DMF treatment in LPS-challenged NPCs. Glutathione (GSH) assay showed that DMF treatment improved reduced to oxidized glutathione ratio in LPS-challenged NPCs. Furthermore, the results of western blot analysis indicated that in LPS-challenged NPCs, DMF treatment ameliorated the elevated levels of matrix degradation enzymes (MMP-13, aggrecanase 1) and type I collagen and the reduced levels of matrix composition (type II collagen and ACAN). However, Nrf2 knockdown abolished these protective effects of DMF. Conclusion. Our data suggested that treatment with DMF mitigated LPS-induced oxidative stress, inflammation, and ER stress-associated apoptosis in NPCs via the Nrf2/HO-1 signaling pathway, thus reliving LPS-induced dysfunction of NPCs, which offered a novel potential pharmacological treatment strategy for IVDD.