bluuebunny/crossref_metadata_2025 huggingface dataset:
Uniqueness theorem of meromorphic function concerning small functions on annuli
Authors: Giang Ha Huong
Published: April 2022

In this paper, we will prove a uniqueness theorem in the case of meromorphic functions on the annuli share [Formula: see text] distinct elements with different multiple values.
On some properties of hyperideals in ternary hypersemirings
Authors: Mandal Manasi , Tamang Nita , Das Sampad
Published: April 2022

In this paper, we introduce the notion of prime hyperideals and maximal hyperideals in ternary hypersemirings and we study some of their properties. We construct a ternary semiring [Formula: see text], corresponding to a strongly distributive ternary hypersemiring [Formula: see text] and obtain various results among them. We also establish an inclusion preserving bijection between the set of all prime hyperideals of [Formula: see text] and collection of all prime total k-ideals of [Formula: see text].
Secular changes in metamorphism and metamorphic cooling rates track the evolving plate-tectonic regime on Earth
Authors: Brown Michael , Johnson Tim , Spencer Christopher J.
Published: April 2022

When disturbed, dynamic emergent systems, such as tectonics on Earth, may transition from one stable state to another if the perturbation is sufficiently large. Here, we identify such state shifts through examination of statistically significant change points in the time series of metamorphic pressure–temperature and cooling rate data. Change points occur in the mid-Paleoproterozoic, the Mesoproterozoic, the early and late Paleozoic and the Cenozoic. To compare the timing of change points with mantle geodynamics, we interrogate the time series of calculated mantle potential temperature, which yields a statistically significant drop of >130°C in the mid-Paleoproterozoic, indicating a change in the mechanism of mantle cooling. We interpret changes in the mid-Paleoproterozoic to relate to the global emergence of stable subduction, orogenesis associated with the formation of the Nuna megacontinent, and slab breakoff, consistent with the operation of a distinctive style of Proterozoic plate tectonics. By contrast, changes from the dawn of the Cambrian onward may relate to the large volume of sediments supplied to trenches following multiple Cryogenian and Paleozoic glaciations. Sediments acted as a lubricant, allowing deeper subduction, transport of lower continental plates to mantle depths and faster metamorphic cooling rates, features characteristic of modern plate tectonics.

Supplementary material:
A dataset of metamorphic cooling rates is available at
https://doi.org/10.6084/m9.figshare.c.5943418
Biogeomorphology
Authors: Viles Heather A.
Published: April 2022

Biogeomorphological research blossomed during the second half of the twentieth century, partly in response to some big, unanswered questions about the role of vegetation in fluvial geomorphology, but also as technical advances allowed more detailed study of the complex interactions between biota and Earth surface processes. Formal recognition of biogeomorphology (also known as ecogeomorphology) as a subfield of geomorphology came in the late 1980s, building on several foundational pieces of research. Key foci of interest for biogeomorphological research up until the end of the twentieth century were quantifying the impact of vegetation on erosion and the geomorphological roles of individual animal species, as well as understanding the human influences on biogeomorphic systems.
An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement
Authors: Hu Qiang , Guo Yuejun , Cordy Maxime , Xie Xiaofei , Ma Lei , Papadakis Mike , Le Traon Yves
Published: April 2022

Similar to traditional software that is constantly under evolution, deep neural networks need to evolve upon the rapid growth of test data for continuous enhancement (e.g., adapting to distribution shift in a new environment for deployment). However, it is labor intensive to manually label all of the collected test data. Test selection solves this problem by strategically choosing a small set to label. Via retraining with the selected set, deep neural networks will achieve competitive accuracy. Unfortunately, existing selection metrics involve three main limitations: (1) using different retraining processes, (2) ignoring data distribution shifts, and (3) being insufficiently evaluated. To fill this gap, we first conduct a systemically empirical study to reveal the impact of the retraining process and data distribution on model enhancement. Then based on our findings, we propose DAT, a novel distribution-aware test selection metric. Experimental results reveal that retraining using both the training and selected data outperforms using only the selected data. None of the selection metrics perform the best under various data distributions. By contrast, DAT effectively alleviates the impact of distribution shifts and outperforms the compared metrics by up to five times and 30.09% accuracy improvement for model enhancement on simulated and in-the-wild distribution shift scenarios, respectively.
A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues
Authors: Xiao Jiang , Li Huichuwu , Wu Minrui , Jin Hai , Deen M. Jamal , Cao Jiannong
Published: April 2022

In the last decade, many studies have significantly pushed the limits of
wireless device-free human sensing
(WDHS) technology and facilitated various applications, ranging from activity identification to vital sign monitoring. This survey presents a novel taxonomy that classifies the state-of-the-art WDHS systems into 11 categories according to their
sensing task type
and motion
granularity
. In particular, existing WDHS systems involve three primary sensing task types. The first type,
behavior recognition
, is a classification problem of recognizing predefined meaningful behaviors. The second type is
movement tracking
, monitoring the quantitative values of behavior states integrating with spatiotemporal information. The third type,
user identification
, leverages the unique features in behaviors to identify who performs the movements. The selected papers in each sensing task type can be further divided into sub-categories according to their motion granularity. Recent advances reveal that WDHS systems within a particular granularity follow similar challenges and design principles. For example, fine-grained hand recognition systems target extracting subtle motion-induced signal changes from the noisy signal responses, and their sensing areas are limited to a relatively small range. Coarse-grained activity identification systems need to overcome the interference of other moving objects within the room-level sensing range. A novel research framework is proposed to help to summarize WDHS systems from methodology, evaluation performance, and design goals. Finally, we conclude with several open issues and present the future research directions from the perspectives of
data collection
,
sensing methodology
,
performance evaluation
, and
application scenario
.
A Polymer-in-Salt Electrolyte Enables Room Temperature Lithium Metal Batteries
Authors: Xie Jingbing , Xu Hongli , Wang Qingrong , Ke Ruohong , Han Bing , Chang Jian , Wang Jun , Deng Yonghong
Published: April 2022

The lithium metal battery with solid-state polymer electrolyte (SPE) is a promising candidate for solid-state batteries with high safety and high energy density. However, the low room temperature ionic conductivity and poor electrolyte/electrode interfacial stability of the SPEs seriously hinder the practical application. Herein, we adopt a polymer-in-salt electrolyte (PISE) strategy on the comb-like polycaprolactone (PCL) to circumvent the low ionic conductivity and poor interfacial stability of the conventional SPE, thus enabling the full function of room temperature lithium metal batteries. The all-solid-state PISE exhibits a high ionic conductivity of 3.9 × 10−4 S cm−1 at 30 °C, a superior lithium-ion transference number of 0.61 and an improved oxidative stability of ∼4.8 V vs Li/Li+. Due to the ultra-stable interface generated by the superconcentrated lithium salt, the all-solid-state LiFePO4∣∣Li cells exhibit prominent high cycling stability, with high capacity retention (92%) after 300 cycles at ambient temperature. The full function of the ambient temperature PISE offers a promising pathway towards high energy density and high safety room temperature LMBs.
Operando Ultrasonic Monitoring of Lithium-Ion Battery Temperature and Behaviour at Different Cycling Rates and under Drive Cycle Conditions
Authors: Owen Rhodri E. , Robinson James B. , Weaving Julia S. , Pham Martin T. M. , Tranter Thomas G. , Neville Tobias P. , Billson Duncan , Braglia Michele , Stocker Richard , Tidblad Annika Ahlberg , Shearing Paul R. , Brett Dan J. L.
Published: April 2022

Effective diagnostic techniques for Li-ion batteries are vital to ensure that they operate in the required voltage and temperature window to prevent premature degradation and failure. Ultrasonic analysis has been gaining significant attention as a low cost, fast, non-destructive, operando technique for assessing the state-of-charge and state-of-health of Li-ion batteries. Thus far, the majority of studies have focused on a single C-rate at relatively low charge and discharge currents, and as such the relationship between the changing acoustic signal and C-rate is not well understood. In this work, the effect of cell temperature on the acoustic signal is studied and shown to have a strong correlation with the signal’s time-of-flight. This correlation allows for the cell temperature to be inferred using ultrasound and to compensate for these effects to accurately predict the state-of-charge regardless of the C-rate at which the cell is being cycled. Ultrasonic state-of-charge monitoring of a cell during a drive cycle illustrates the suitability of this technique to be applied in real-world situations, an important step in the implementation of this technique in battery management systems with the potential to improve pack safety, performance, and efficiency:
Contribution of sex hormones to the sexually dimorphic response of colorectal motility to noxious stimuli in rats
Authors: Horii Kazuhiro , Sawamura Tomoya , Onishi Ayaka , Yuki Natsufu , Naitou Kiyotada , Shiina Takahiko , Shimizu Yasutake
Published: April 2022

This study demonstrated that estradiol permits inhibitory regulation in the spinal defecation center not only in female rats but also in orchiectomized male rats. GABAergic pathways are likely involved in the effect of estradiol. This is the first report showing that sex hormones affect colorectal motility through the alteration of neural components of the regulatory pathways. Our findings provide a novel insight into pathophysiological mechanisms of defecation disorders related to changes in sex hormones.
The ultrastructural heterogeneity of lung surfactant revealed by serial section electron tomography: insights into the 3-D architecture of human tubular myelin
Authors: Lettau Marie , Timm Sara , Dittmayer Carsten , Lopez-Rodriguez Elena , Ochs Matthias
Published: April 2022

Weibel’s hypothetical three-dimensional (3-D) model in 1966 provided first ultrastructural details into tubular myelin (TM), a unique, complex surfactant subtype found in the hypophase of the alveolar lining layer. Although initial descriptions by electron microscopy (EM) were already published in the 1950s, a uniform morphological differentiation from other intra-alveolar surfactant subtypes is still missing and potential structure-function relationships remain enigmatic. Technical developments in volume EM methods now allow a more detailed reinvestigation, to address unanswered ultrastructural questions, we analyzed ultrathin sections of humanized SP-A1/SP-A2 coexpressing mouse and human lung samples by conventional transmission EM. We combined these two-dimensional (2-D) information with 3-D analysis of single- and dual-axis electron tomography of serial sections for high z-resolution (in a range of a few nanometers) and extended volumes of up to 1 µm total z-information, this study reveals that TM constitutes a heterogeneous surfactant organization mainly comprised of distorted parallel membrane planes with local intersections, which are distributed all over the TM substructure. These intersecting membrane planes form, among other various polygons, the well-known 2-D “lattice”, respectively 3-D quadratic tubules, which in many analyzed spots of human alveoli appear to be less abundant than also observed nonconcentric 3-D lamellae, the additional application of serial section electron tomography to conventional transmission EM demonstrates a high heterogeneity of TM membrane networks, which indicates dynamic transformations between its substructures. Our method provides an ideal basis for further in and ex vivo structural analyses of surfactant under various conditions at nanometer scale.
Six-month longitudinal tracking of arterial stiffness and blood pressure in young adults following SARS-CoV-2 infection
Authors: Szeghy Rachel E. , Stute Nina L. , Province Valesha M. , Augenreich Marc A. , Stickford Jonathon L. , Stickford Abigail S. L. , Ratchford Stephen M.
Published: April 2022

We sought to investigate potential long-lasting effects of SARS-CoV-2 infection on markers of arterial stiffness among young adults for 6 mo following infection. Carotid femoral pulse wave velocity was significantly reduced while carotid stiffness measures remained unaltered over the 6-mo period. These findings suggest several months of recovery from infection may be necessary for young adults to improve various markers of arterial stiffness, possibly contributing to cardiovascular health and recovery among those infected with SARS-CoV-2.
Structural Configuration of Blood Cell Membranes Determines Their Nonlinear Deformation Properties
Authors: Kozlova Elena , Sergunova Viktoria , Inozemtsev Vladimir , Sherstyukova Ekaterina , Kozlov Aleksandr , Gudkova Olga , Chernysh Aleksandr
Published: April 2022

The ability of neutrophils and red blood cells (RBCs) to undergo significant deformations is a key to their normal functioning. Disruptions of these processes can lead to pathologies. This work studied the influence of structural configuration rearrangements of membranes after exposure to external factors on the ability of native membranes of neutrophils and RBCs to undergo deep deformation. The rearrangement of the structural configuration of neutrophil and RBC membranes under the influence of cytological fixatives caused nonlinear deformation phenomena. There were an increase in Young’s modulus, a decrease in the depth of homogeneous bending, and a change in the distance between cytoskeletal junctions. Based on the results of the analysis of experimental data, a mathematical model was proposed that describes the process of deep bending of RBСs and neutrophil membranes.
System for Recommending Telecommunication Packages Based on the Deep and Cross Network
Authors: Shi Congming , Wang Wen , Wei Shoulin , Lv Feiya
Published: April 2022

With the evolution of the 5th generation mobile network (5G), the telecommunications industry has considerably affected livelihoods and resulted in the development of national economies worldwide. To increase revenue per customer and secure long-term contracts with users, telecommunications firms and enterprises have launched diverse types of telecommunication packages to satisfy varied user requirements. Several systems for recommending telecommunication packages have been recently proposed. However, extracting effective feature information from large and complex consumption data remains challenging. Conventional methods for the recommendation of telecommunications packages either rely on complex expert feature engineering or fail to perform end-to-end deep learning (DL) during training. In this study, we propose a recommender system based on the Deep and Cross Network (DCN), deep belief network (DBN), embedding, and Word2Vec using the learning abilities of DL-based approaches. The proposed system fits the recommender system for telecommunication packages in terms of click-through rate prediction to provide a potential solution to the recommendation challenges faced by telecommunication enterprises. The proposed model captures the finite order interactional and deep hidden features. Additionally, the text information in the data is used to improve the model’s recommendation capability. The proposed method also does not require feature engineering. We conducted comprehensive experiments using real-world datasets, the results of which demonstrated that our proposed method outperformed other methods based on DBNs, DCNs, deep factorization machines, and deep neural networks in terms of the area under the ROC curve, cross entropy (log loss), and recall metrics.
A Data Mining-Based Evaluation Technique for Music Teaching
Authors: Qin Zeng
Published: April 2022

Musical data mining covers a number of methodologies to successfully apply data mining techniques for music processing, drawing together a multidisciplinary array of top experts. The field of music data acquisition has grown through time to solve the difficulties of obtaining and engaging with enormous amounts of music and associated data, such as styles, artists, lyrics, and reviews. In order to improve the quality of music teaching, a music teaching evaluation based on data mining is proposed. Data mining is becoming more widely accepted as a viable form of inquiry for analyzing data obtained in natural settings. More and more attention is paid to music teaching. Actual data is frequently inadequate, unreliable, and/or lacking in specific behaviors or patterns, as well as including numerous inaccuracies. Preprocessing data is a tried-and-true means of resolving such problems. Music teaching data is divided into three steps after preprocessing, that is, “object and object type,” “music teaching data normalization,” and “data integration.” A model is built with a high-dimensional characteristic distribution and essential parameters of convergent teaching capacity. The experimental results show that the data mining method can be used for music teaching evaluation and has the advantages of short evaluation time, high accuracy, and clear indicators.
[Retracted] Identification of a 5‐Nutrient Stress‐Sensitive Gene Signature to Predict Survival for Colorectal Cancer
Authors: Xiong Jingjie , Xiong Subing , Feng Xi , Zhang Huaming , Su Qisheng
Published: April 2022

Background. The high heterogeneity and the complexity of the tumor microenvironment of colorectal cancer (CRC) have enhanced the difficulty of prognosis prediction based on conventional clinical indicators. Recent studies revealed that tumor cells could overcome various nutritional deficiencies by gene regulation and metabolic remodeling. However, whether differentially expressed genes (DEGs) in CRC cells under kinds of nutrient deficiency could be used to predict prognosis remained unveiled. Methods. Three datasets (GSE70976, GSE13548, and GSE116087), in which colon cancer cells were, respectively, cultured in serum‐free, glucose‐free, or glutamine‐free medium, were included to delineate the profiles of gene expression by nutrient stress. DEGs were figured out in three datasets, and gene functional analysis was performed. Survival analyses and Cox proportional hazards model were then used to identify nutrient stress sensitive genes in CRC datasets (GSE39582 and TCGA COAD). Then, a 5‐gene signature was constructed and the risk scores were also calculated. Survival analyses, cox analyses, and nomogram were applied to predict the prognosis of patients with colorectal cancer. The effectiveness of the risk model was also tested. Results. A total of 48 genes were found to be dysregulated in serum, glucose, or glutamine‐deprived CRC cells, which were mainly enriched in cell cycle and endoplasmic reticulum stress pathways. After further analyses, 5 genes, MCM5, MCM6, CDCA2, GINS2, and SPC25, were identified to be differentially expressed in CRC and be related to prognosis of in CRC datasets. We used the above nutrient stress‐sensitive genes to construct a risk scoring model. CRC samples in the datasets were divided into low‐risk and high‐risk groups. Data showed that higher risk scores were associated with better outcomes and risk scores decreased significantly with tumor procession. Moreover, the risk score could be used to predict the probability of survival based on nomogram. Conclusions. The 5‐nutrient stress‐sensitive gene signature could act as an independent biomarker for survival prediction of CRC patients.
Avoidance Path of Foreign Exchange Risk Management for Overseas Construction Enterprises in China
Authors: Tang Honglei , Xiao Chao , Ju Shuimu , Xiao Hanjie , Zhou Jianhua , Liu Jian
Published: April 2022

Foreign exchange risk management is the focus of overseas construction enterprise management. The quality of its management will affect the profitability of enterprises to a large extent. It is also an important index for comprehensive evaluation of its internal management. The foreign exchange management strategies of China’s overseas construction enterprises mainly include two ways: operational hedging and financial hedging. According to the data of overseas construction enterprises in China from 2011 to 2018, the empirical application shows that enterprises that use only financial hedging have a negative relationship with corporate profits. The better foreign exchange risk management strategy of enterprises should be a comprehensive foreign exchange risk prevention strategy, which mainly relies on operational hedging supplemented by financial hedging.
Overexpressed RING Finger 44 Correlates with Poor Prognosis in Hepatocellular Carcinoma
Authors: Liu Yue , Xia Huasong , Li Meng , Chen Yuxin , Zhou Wei , Chen Yi , Wu Yanqing
Published: April 2022

Introduction. Liver carcinoma is one of the most common cancers in the world and remains one of the most difficult cancers to treat. Hepatocellular cancer is the most important type of liver cancer (90%). RING Finger 44 (RNF44) is one of the E3 ligases, which play an important role in substrate recognition. It was also reported that RING Finger 44 was connected with resistant melanoma. But the relationship between RNF44 and HCC remained unknown. Materials and Methods. To analyze the role of RING Finger 44 gene in hepatocellular carcinoma, we used bioinformatics to analyze the expression level, genetic changes, immunohistochemistry, immune infiltration, diagnostic value, survival, and functional enrichment of RING Finger 44. Results. Through analyzing The Genotype-Tissue Expression and The Cancer Genome Atlas databases, we found that the expression level of RING Finger 44 was significantly increased in hepatocellular carcinoma tissues. Meanwhile, the expression of RING Finger 44 was connected with immune cell infiltration and survival time, and the expression level of RING Finger 44 could perform as a useful diagnostic and prognostic index. The functional enrichment analysis of RING Finger 44 provided some possible pathways of RING Finger 44 in hepatocellular carcinoma, which provided an important direction for the further experiments in vitro or in vivo. Conclusions. RING Finger 44, the high expression level of which predicts poor prognosis, is a potential oncogene in hepatocellular carcinoma.
Abnormal Function of Circulating Follicular Helper T Cells Leads to Different Manifestations of B Cell Maturation and Differentiation in Patients with Osteosarcoma
Authors: Zhao Gang , Liang Jianxiao , Cao Jingjing , Jiang Shanyong , Lu Jianshu , Jiang Baoen
Published: April 2022

Objective. The objective of this study is to investigate the effect of dysfunctional circulating follicular helper T cells (Tfh) on B cell maturation and differentiation in patients with osteosarcoma (OS). Method. Data from 30 OS patients who underwent diagnosis and treatment in our hospital, as well as those of 30 healthy subjects (HC), were collected at the same time. Flow cytometry was employed to identify proportions of CD4+CXCR5+Tfh cells and Tfh cell subtypes Tfh17, Tfh1, and Tfh2 in the patient’s peripheral blood. CD40 L and IFNγ levels were detected after stimulating Tfh cells with an influenza antigen; the positive rates of CD27 and CD38 in B cells were detected before and after coculture with Tfh cells. qRT-PCR was carried out for Blimp-1 expression in B cells, and ELISA was employed to identify the levels of IgM, IgG, and IgA in B cells and IL-2, IL-10, and IL-4 in Tfh cells before and after coculture. Results. The percentage of CD4+CXCR5+Tfh cells in OS patients’ peripheral blood increased significantly. The Tfh cell ratio increased along with the TNM stage, and the Tfh cell ratio in OS metastasis patients is greater than that in nonmetastatic patients. In addition, Tfh2 and Tfh17 cells increased drastically in OS patients, and no meaningful change was seen in Tfh1 cells. CD40 L levels of Tfh cells in OS patients were less than those of the HC group, and IFNγ was substantially increased. After coculturing the OS group’s B cells with Tfh cells, the CD27+ and CD38+ cells of B cells were drastically greater, and Blimp-1 expression was also significantly increased. In addition, the levels of IL-21, IL-4, and IL-10 of Tfh cells in the OS group and the levels of IgA, IgG, and IgM in B cells were significantly reduced after coculture. Conclusion. Dysfunctional Tfh in OS patients can severely inhibit B cell development, maturation, and differentiation.
Evolutionary Game Analysis of Consumer Complaint Handling in E‐Commerce
Authors: Bai Shizhen , Liu Zejun , Lv Yang
Published: April 2022

Untimely complaint handling can affect the reputation of the e‐commerce platform and lead to customer loss, which is not conducive to the sustainable development of the e‐commerce industry. To address the problem of untimely handling of consumer complaints, based on the evolutionary game theory, the e‐commerce dispute mediation platform is introduced, the authenticity of consumer complaints and the probability of e‐commerce platform to be in the mediation platform are considered, a tripartite evolutionary game model of consumer‐mediation platform‐e‐commerce platform is constructed, and the problem of which strategy the mediation platform and e‐commerce platform choose to handle consumer complaints is studied. Numerical simulation and simulation analysis are conducted using MATLAB 2020b, and corresponding suggestions are given. The research results show that under certain conditions when the probability of mediation platform and e‐commerce platform positively handling complaint increases or the penalty for consumer distortion complaint increases; when the probability of consumer’s real complaint increases, the probability of mediation platform positively handling complaint increases; the consumer tend to choose real complaint, the mediation platform tends to choose positively handling strategy, the e‐commerce platform positively enters the mediation platform, the mediation platform and consumer’s behavior to the reputation of the e‐commerce platform increases, which can promote the e‐commerce platform to positively handle the consumer complaint.
Autonomous Classification of Digital Painting Images Based on Wireless Network
Authors: Liu Lu
Published: April 2022

With the advancement of digital technology, the precise classification and rapid search of digital painting is important to the creation of digital painting image digitization. In order to deeply study the construction of a self-service classification system based on wireless network, this paper uses the parameter sample insertion method, comparative analysis method, and deconstruction column method, analyzes the independent classification algorithm, and simplifies the algorithm. And finally, we realize and create an efficient system that can independently classify digital images. When studying the best points of the classification efficiency of classification systems, the number of training sets is 2000 and the number of test sets is 120. The input data is classified as 3 layers, and the learning rate of each layer is 1.5, 0.06, and the sparsity is 0.04. The results show that when the sparsity value of both layers of CRBM is taken as 0.02, the classification results reach the best, indicating that here is the best result of this experiment. Further studying the classification system stability, the image from the F1 training set is labeled as 0 and the network data is labeled as 1, thus providing a training two-way model, and the ratio between the training and test sets is 3 : 1. The classification accuracy is quite stable. With the addition of more training data, the classification accuracy remains at around 50%. This shows that the system stability is guaranteed. Based on wireless network technology, a complete system is designed to classify digital images.
Improved TLBO for Fusion of Infrared and Visible Images
Authors: Wang Jinghua , Yan Lei , Wang Fan , Li Shulin
Published: April 2022

Image fusion is an image enhancement method in modern artificial intelligence theory, which can reduce the pressure in data storage and obtain better image information. Due to different imaging principles, information of the infrared image and visible images’ information is complementary and redundant. The infrared image can be fused with a visible image to obtain both the high-resolution texture details and the edge contour of the infrared image. In this paper, the fusion algorithm of forest sample image is studied at the feature level, which aims to accurately extract tree features through information fusion, ensure data stability and reliability, and improve the accuracy of target recognition. The main research contents of this paper are as follows: (1) teaching learning-based optimization (TLBO) algorithm was used to optimize the weighted coefficient in the fusion process, and the value range of random parameters in the model was adjusted to optimize the fusion effect. Compared with before optimization, image information increased by 2.05%, and spatial activity increased by 15.27%. (2) Experimental data show that the target recognition accuracy of feature-level fusion results was 93.6%, 13.9% higher than that of the original infrared sample image, and 18.8% higher than that of the original visible sample image. Pixel-level and feature-level fusion have their characteristics and application scopes. This method can improve the quality of the specified region in the image and is suitable for detecting intelligent information in forest regions.
Developing a Simplified Method to Investigate the Dynamic Behavior of Fluid Conveying Pipes under Mean Internal Pressure
Authors: Atashgah Mahdi Bayrami , Iranmanesh Mehdi , Mojtahedi Alireza
Published: April 2022

Natural frequencies and critical velocities are the basic dynamic characteristics of fluid conveying pipes. Investigation of vibrations of fluid conveying pipes to control their dynamic behavior is one of the main topics of these structures. This paper presents a simplified approach to determine the natural frequencies and critical velocities of fluid conveying pipes with different typical boundary conditions under the mean internal pressure due to the fluid flow. The approach is suggested by reducing the order of mass, damping, and stiffness based on the concepts of the Galerkin method. The results of the method are validated by experimental results and compared with various numerical methods reported in the literature. The effects of mean internal pressure on the natural frequencies of fluid conveying pipes are assessed, and critical pressure values are calculated for the different mode shapes of the pipes. The method presents significantly better results in all the velocities in a range less than the critical flow velocity. Also, the natural frequencies of fluid conveying pipes in the presence and absence of mean internal pressure are calculated by the explicit formulas obtained by the simplified approach. The method has a lower computational cost than other more complex techniques, and its relative errors are less than 5%.
The Traditional Chinese Medicine Formula FTZ Protects against Cardiac Fibrosis by Suppressing the TGFβ1-Smad2/3 Pathway
Authors: Zhang Yue , Wang Dongwei , Wu Kaili , Shao Xiaoqi , Diao Hongtao , Wang Zhiying , Sun Mengxian , Huang Xueying , Li Yun , Tang Xinyuan , Yan Meiling , Guo Jiao
Published: April 2022

Background. Fu fang Zhen Zhu Tiao Zhi (FTZ) is a patented preparation of Chinese herbal medicine that has been used as a natural medicine to treat several chronic diseases including cardiovascular disease. However, its effects on cardiac fibrosis remain unclear. Therefore, this study was designed to investigate the effects and potential mechanisms of FTZ in treating cardiac fibrosis. Methods. FTZ was administered to mice by oral gavage daily at a dosage of 1.2 g/kg or 2.4 g/kg of body weight for 7 weeks after a transverse aorta constriction (TAC) surgery. Doppler echocardiography, hematoxylin and eosin staining, and Masson’s trichrome staining were used to assess the effect of FTZ on the cardiac structure and function of mice that had undergone TAC. EdU and wound-healing assays were performed to measure the proliferative and migratory abilities of cardiac fibroblasts. Western blotting and qRT-PCR were used to determine the expression of TGFβ1, Col1A2, Col3, and α-SMA proteins and mRNA levels. Results. FTZ treatment reduced collagen synthesis, attenuated cardiac fibrosis, and improved cardiac function in mice subjected to TAC. Moreover, FTZ treatment prevented the proliferation and migration of cardiac fibroblasts and reduced Ang-II-induced collagen synthesis. Furthermore, FTZ downregulated the expression of TGFβ1, p-smad2, and p-smad3 and inhibited the TGFβ1-Smad2/3 pathway in the setting of cardiac fibrosis. Conclusion. FTZ alleviated the proliferation and migration of cardiac fibroblasts and suppressed collagen synthesis via the TGFβ1-Smad2/3 pathway during the progression of cardiac fibrosis. These findings indicated the therapeutic potential of FTZ in treating cardiac fibrosis.
Towards Attack to MemGuard with Nonlocal-Means Method
Authors: Xie Guangxu , Pei Qingqi
Published: April 2022

An adversarial example is the weakness of the machine learning (ML), and it can be utilized as the tool to defend against the inference attacks launched by ML classifiers. Jia et al. proposed MemGuard, which applied the idea of adversarial example to defend against membership inference attack. In a membership inference attack, the attacker attempts to infer whether a particular sample is in the training set of the target classifier, which may be a software or a service whose model parameters are unknown to the attacker. MemGuard does not tamper the training process of the target classifier, meanwhile achieving better tradeoff between the privacy and utility loss. However, many defenses of the adversarial example have been proposed, which decreases the effectiveness of the adversarial example. Inspired by the defenses of the adversarial example, we try to attack the MemGuard. As a result, we utilize the nonlocal-means method to do the attack by using the inherent relationship between neighbor entries to remove the added noise. Due to the low dimensionality of the confidence score vector, our attack avoids the high computation overhead of the nonlocal-means method. Besides, we use practical datasets to test our attack, and the experimental results demonstrate the effectiveness of our attack.
Analysis of English Writing Text Features Based on Random Forest and Logistic Regression Classification Algorithm
Authors: Sun Chuan , Luo Bo
Published: April 2022

The characteristics of English writing text in natural scenes are characterized by low character detection rate, difficulty in small character detection, and various character detection categories. In order to improve the classification effect of English-written texts, solve the problem of feature loss and precision reduction of the prediction model based on dimensionality reduction neural network classifier in the analysis process. An improved stacking model combining random forest and logistic regression is proposed to analyze the characteristics of written English texts. The model uses multiple undersampling, trains multiple random forests as primary classifier, and uses logistic regression as secondary classifier. Experimental results show that this model can effectively improve the classification efficiency of unbalanced text classification. While ensuring the main features, the accuracy of prediction is substantially improved. It is proved that the model has high practicability in analyzing the features of English writing texts.
Certain Concepts of Interval-Valued Intuitionistic Fuzzy Graphs with an Application
Authors: Xu Peng , Guan Hao , Talebi A. A. , Ghassemi M. , Rashmanlou Hossein
Published: April 2022

Interval-valued intuitionistic fuzzy graph (IVIFG), belonging to the FGs family, has good capabilities when facing with problems that cannot be expressed by FGs. When an element membership is not clear, neutrality is a good option that can be well supported by an IVIFG. The previous definitions of limitations in edge irregular FG have led us to offer new definitions in IVIFGs. Hence, in this paper, some types of edge irregular interval-valued intuitionistic fuzzy graphs (EI-IVIFGs) such as neighborly edge totally irregular (NETI), strongly edge irregular (SEI), and strongly edge totally irregular (SETI) are introduced. A comparative study between NEI-IVIFGs and NETI-IVIFGs is done. With the help of IVIFGs, the most efficient person in an organization can be identified according to the important factors that can be useful for an institution. Finally, an application of IVIFG has been introduced.
Comparison between the Severity Scoring Systems A-DROP and CURB-65 for Predicting Safe Discharge from the Emergency Department in Patients with Community-Acquired Pneumonia
Authors: Limapichat Thanya , Supavajana Suvanun
Published: April 2022

Background. In most community-acquired pneumonia (CAP) treatment guidelines, the Pneumonia Severity Index (PSI) and CURB-65 are used as prognostic tools. Recently, simpler and more effective predictive tools for CAP treatment, such as the A-DROP scoring system, have been developed. However, no study has performed a comparative evaluation to identify the superior tool for predicting when patients can be discharged safely. Objectives. To compare the performances of A-DROP and CURB-65, simple predictive tools for CAP, based on 30-day death rates and 72-hour revisit rates for CAP following discharge from the emergency department (ED). Method. This single-center retrospective observational study enrolled patients who were at least 18 years old and diagnosed with CAP at the Songklanagarind Hospital ED from January 2015 to April 2021. Following a severity assessment using the A-DROP and CURB-65 scoring systems, the 30-day mortality rates and 72-hour revisit rates after discharge from the ED were compared. Results. A total of 408 patients were enrolled in this study. Six (1.47%) died within 30 days after presentation, whereas 29 (7.1%) returned to the ED within 72 hours after discharge. Most patients (72%) who revisited the ED were over the age of 65 years. The areas under the receiver operating characteristic curves for the prediction of 30-day mortality were 0.756 (95% confidence interval [CI]: 0.526–0.987) and 0.808 (95% CI: 0.647–0.970) for A-DROP and CURB-65, respectively. The areas under the receiver operating characteristic curves for the prediction of 72-hour revisit were 0.617 (95% confidence interval [CI]: 0.507–0.728) and 0.639 (95% CI: 0.536–0.743) for A-DROP and CURB-65, respectively. Conclusion. A-DROP and CURB-65 yield similar results and can be used to assess low-risk patients with CAP for discharge from the ED. Older patients, even those with low-risk scores, should be particularly considered for admission to a short-term observation unit or ward.
An Anonymous Verifiable Random Function with Applications in Blockchain
Authors: Yao Shuang , Zhang Dawei
Published: April 2022

Verifiable random function is a powerful function that provides a noninteractively public verifiable proof for its output. Recently, verifiable random function has found essential applications in designing secure consensus protocols in blockchain. How to construct secure and practical verifiable random functions has also attracted more and more attention. In this paper, we propose a practical anonymous verifiable random function. Security proofs show that the proposed anonymous verifiable random function achieves correctness, anonymity, uniqueness, and pseudorandomness. In addition, we show a concrete application of our proposed anonymous verifiable random function in blockchain to improve the consensus mechanism for Hyperledger fabric. Finally, we implement the proposed anonymous verifiable random function and evaluate its performance. Test results show that the proposed anonymous verifiable random function supports faster computing operations and has a smaller proof size.
A New Model of Intelligent Nursing Based on 5G Network Architecture and RFID Technology
Authors: Han Zhiyong , Wang Siyuan , Chu Xin , Jiao Yumei
Published: April 2022

RFID technology is an emerging automatic identification technology in recent years. Based on the research on RFID indoor positioning system, this paper proposes a clustering SPA relative positioning algorithm based on nearest neighbours in the 5G network architecture system. After analysing the algorithm process, simulation experiments are carried out. The experimental results show that the algorithm can effectively improve the positioning accuracy of the system. Based on the research on the application of RFID in hospital medical care management, the medical care mobile management system is designed, mainly including the database system of the system, the handheld data acquisition and wireless transmission system based on the 5G network platform, and the management running on the desktop PC. The system has been designed in detail, with a view to RFID technology can be applied to medical care management.
A Novel Energy-Aware Routing in Wireless Sensor Network Using Clustering Based on Combination of Multiobjective Genetic and Cuckoo Search Algorithm
Authors: Zhao Xiuniao , Zhong Wentao , Navaei Yahya Dorostkar
Published: April 2022

The development of various applications of wireless sensor networks has aroused great interest in using these types of networks in various fields. These networks, without infrastructure and self-organization, are easily deployed in most environments and collect information about environmental phenomena for analysis and proper response to accidents and send them to the basic centers. They do. Wireless sensor networks are made up of some sensor nodes that both act as sensors and act as relay nodes concerning to each other. On the other hand, the lack of infrastructure in these networks has limited resources so that the nodes of the battery are fed with limited energy. Due to the location of networks in difficult and impassable areas, it is not possible to recharge or replace the node battery. Therefore, saving energy consumption in this type of network is one of the most important challenges. Since the rate of energy consumption when sensing information and receiving data packets from another node is a fixed value, so sensor nodes have the highest energy consumption when sending data. Therefore, routing methods try to reduce energy consumption based on systematic approaches. One of the most promising solutions to reduce energy consumption in wireless sensor networks is to cluster the nodes and select the threaded node based on the data transfer parameters so that the average energy consumption in the nodes is reduced and the network lifetime is increased. Therefore, in this research, a new optimization approach using multiobjective genetic algorithm and cuckoo algorithm for clustering wireless sensor networks is presented. In this study, in order to select clustered nodes from a multiobjective genetic algorithms based on reducing intracluster distances and reducing energy consumption in cluster member nodes and near-optimal routing based on cuckoo optimization algorithm to transfer information between nodes have been used in the direction of the cavity. The implementation results show that considering the evolutionary capabilities of the multiobjective genetic algorithm and the cuckoo optimization algorithm, the proposed method in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, compared to previous methods, has improved.
A Music Curriculum Integration and Reconstruction Model Based on Advanced Iterative Reconstruction Algorithm
Authors: Zeng Qin
Published: April 2022

Music curriculum fusion and super-resolution reconstruction based on musical elements have gradually attracted the attention of researchers. The traditional music element fusion and reconstruction algorithm is based on the fusion and reconstruction of all the pixel information of the source music element, which has the problem of high time and space complexity. Based on the advanced iterative reconstruction theory, this paper uses the measurement matrix to measure the dimensionality of the music signal, compresses the music element data while acquiring the music elements, reduces the sampling frequency, reduces the sampling amount of the music element data, and greatly reduces the data. It solves the problem of spatial resolution reduction caused by degradation and the problem of music element feature extraction, classification, and identification and can obtain more music element features and detailed parameters. Aiming at the back-projection algorithm of musical symbol filtering, the advanced iterative reconstruction algorithm of musical symbol filtering of a triangular line array is simulated. The experimental results show that the feasibility analysis factor of the scheme reaches 0.917, and the running time of the reconstruction algorithm is reduced to 0.131 s, which promotes a large amount of data in music curriculum element fusion and super-resolution reconstruction.
ROS-Ethereum: A Convenient Tool to Bridge ROS and Blockchain (Ethereum)
Authors: Zhang Shenhui , Tang Ming , Li Xiulai , Liu Boyi , Zhang Bo , Hu Fei , Ni Sirui , Cheng Jieren
Published: April 2022

Robot Operating System (ROS) has received widespread utilization with the development of robotics, self-driving, etc., recently. Meanwhile, the other technology blockchain is frequently applied to various fields with its trustworthy characteristics and immutability in data storage. However, ROS has no ability to interact with the blockchain, which hinders research in related fields. Therefore, we wonder if we can develop a convenient tool to bridge ROS and blockchain. Inspired by this, we propose ROS-Ethereum. It bridges ROS and Ethereum, a widely used blockchain platform. ROS-Ethereum is based on the User Datagram Protocol (UDP) communication mechanism and the SM algorithm family along with Ethereum technology. Simply put, ROS-Ethereum allows users to invoke the contract when interacting with the blockchain, which makes this process easier and safer. We conduct experiments in real robots to verify the effectiveness of ROS-Ethereum and evaluate it from the following metrics: (1) the encryption efficiency and stability of the algorithm and (2) ROS-Ethereum transaction response time and packet loss rate.