bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-objective Cuckoo Search-based Streaming Feature Selection for Multi-label Dataset
Authors: Paul Dipanjyoti , Kumar Rahul , Saha Sriparna , Mathew Jimson
Published: May 2021

The feature selection method is the process of selecting only relevant features by removing irrelevant or redundant features amongst the large number of features that are used to represent data. Nowadays, many application domains especially social media networks, generate new features continuously at different time stamps. In such a scenario, when the features are arriving in an online fashion, to cope up with the continuous arrival of features, the selection task must also have to be a continuous process. Therefore, the streaming feature selection based approach has to be incorporated, i.e., every time a new feature or a group of features arrives, the feature selection process has to be invoked. Again, in recent years, there are many application domains that generate data where samples may belong to more than one classes called multi-label dataset. The multiple labels that the instances are being associated with, may have some dependencies amongst themselves. Finding the co-relation amongst the class labels helps to select the discriminative features across multiple labels. In this article, we develop streaming feature selection methods for multi-label data where the multiple labels are reduced to a lower-dimensional space. The similar labels are grouped together before performing the selection method to improve the selection quality and to make the model time efficient. The multi-objective version of the cuckoo search-based approach is used to select the optimal feature set. The proposed method develops two versions of the streaming feature selection method:

) when the features arrive individually and

) when the features arrive in the form of a batch. Various multi-label datasets from various domains such as text, biology, and audio have been used to test the developed streaming feature selection methods. The proposed methods are compared with many previous feature selection methods and from the comparison, the superiority of using multiple objectives and label co-relation in the feature selection process can be established.
Similarity Embedding Networks for Robust Human Activity Recognition
Authors: Li Chenglin , Tong Carrie Lu , Niu Di , Jiang Bei , Zuo Xiao , Cheng Lei , Xiong Jian , Yang Jianming
Published: May 2021

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex real-world HAR data is limited by the availability of high-quality labeled activity data, which are hard to obtain. In this article, we design a similarity embedding neural network that maps input sensor signals onto real vectors through carefully designed convolutional and Long Short-Term Memory (LSTM) layers. The embedding network is trained with a pairwise similarity loss, encouraging the clustering of samples from the same class in the embedded real space, and can be effectively trained on a small dataset and even on a noisy dataset with mislabeled samples. Based on the learned embeddings, we further propose both nonparametric and parametric approaches for activity recognition. Extensive evaluation based on two public datasets has shown that the proposed similarity embedding network significantly outperforms state-of-the-art deep models on HAR classification tasks, is robust to mislabeled samples in the training set, and can also be used to effectively denoise a noisy dataset.
NeuSE: A Neural Snapshot Ensemble Method for Collaborative Filtering
Authors: Li Dongsheng , Liu Haodong , Chen Chao , Zhao Yingying , Chu Stephen M. , Yang Bo
Published: May 2021

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this article, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.
Maximum Likelihood Estimation of Power-law Degree Distributions via Friendship Paradox-based Sampling
Authors: Nettasinghe Buddhika , Krishnamurthy Vikram
Published: May 2021

This article considers the problem of estimating a power-law degree distribution of an undirected network using sampled data. Although power-law degree distributions are ubiquitous in nature, the widely used parametric methods for estimating them (e.g., linear regression on double-logarithmic axes and maximum likelihood estimation with uniformly sampled nodes) suffer from the large variance introduced by the lack of data-points from the tail portion of the power-law degree distribution. As a solution, we present a novel maximum likelihood estimation approach that exploits the
friendship paradox
to sample more efficiently from the tail of the degree distribution. We analytically show that the proposed method results in a smaller bias, variance and a Cramèr–Rao lower bound compared to the vanilla maximum likelihood estimate obtained with uniformly sampled nodes (which is the most commonly used method in literature). Detailed numerical and empirical results are presented to illustrate the performance of the proposed method under different conditions and how it compares with alternative methods. We also show that the proposed method and its desirable properties (i.e., smaller bias, variance, and Cramèr–Rao lower bound compared to vanilla method based on uniform samples) extend to parametric degree distributions other than the power-law such as exponential degree distributions as well. All the numerical and empirical results are reproducible and the code is publicly available on Github.
Incremental Community Detection on Large Complex Attributed Network
Authors: Chen Zhe , Sun Aixin , Xiao Xiaokui
Published: May 2021

Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains

users with

transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.
Sufficient Statistics Revisited
Authors: Kleven Henrik J.
Published: May 2021

This article reviews and generalizes the sufficient statistics approach to policy evaluation. The idea of the approach is that the welfare effect of policy changes can be expressed in terms of estimable reduced-form elasticities, allowing for policy evaluation without estimating the structural primitives of fully specified models. The approach relies on three assumptions: that policy changes are small, that government policy is the only source of market imperfection, and that a set of high-level restrictions on the environment and on preferences can be used to reduce the number of elasticities to be estimated. We generalize the approach in all three dimensions. It is possible to develop transparent sufficient statistics formulas under very general conditions, but the estimation requirements increase greatly. Starting from such general formulas elucidates that feasible empirical implementations are in fact structural approaches.
Forest Restoration in Low- and Middle-Income Countries
Authors: Vincent Jeffrey R. , Curran Sara R. , Ashton Mark S.
Published: May 2021

A series of international initiatives have set ambitious goals for restoring global forests. This review synthesizes natural and social science research on forest restoration (FR), with a focus on restoration on cleared land in low- and middle-income countries. We define restoration more broadly than reestablishing native forests, given that landholders might prefer other forest types. We organize the review loosely around ideas in the forest transition literature. We begin by examining recent trends in FR and forest transition indicators. We then investigate two primary parts of the forest transition explanation for forest recovery: wood scarcity, including its connection to restoration for climate change mitigation, and the dynamic relationships between migration and land use. Next, we review ecological and silvicultural aspects of restoration on cleared land. We conclude by discussing selected interventions to promote restoration and the challenge of scaling up restoration to achieve international initiatives' goals.
Surface Modification of Solid Electrodes with Gliadin Biopolymer Film: A Permselective Membrane in Electrochemical Studies
Authors: Hasanzadeh Mahsa , Farhadi Khalil , Tajik Hosein
Published: May 2021

The application of natural materials films for the surface modification of glassy carbon and gold electrodes to enhance their selective sense in the electrochemical detection of organic and inorganic species is a major challenge. In this study, gliadin was extracted from wheat flour and, for the first time, applied as a novel biocompatible permselective modifier film on gold and glassy carbon electrodes for the electrochemical detection of various compounds. The structure and surface morphology of the extracted gliadin were investigated by Fourier transform infrared (FT-IR) spectroscopy and atomic force microscopy (AFM) techniques. AFM and scanning electron microscopy (SEM) showed that porous structures with high nanometric roughness were present in gliadin membranes prepared from the casting solution of gliadin in ethanol. Compared to hydrophobic ions, the capacitance (C) values of electrical double layers of gliadin film modified electrodes for hydrophilic ions were very high. The findings of cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) proved high performance of gliadin membrane as permselectcive film for nominated cationic compounds with fast electrochemical kinetics responses in aqueous solutions (PBS, pH = 7). This behavior was verified by circulating solutions containing model compounds from a home-made continuous cell equipped with gliadin-modified polyamide membranes.
Review—Perovskite/Spinel Based Graphene Derivatives Electrochemical and Biosensors
Authors: dos Santos Otavio Augusto L. , Sneha M. , Devarani T. , Bououdina M. , Backx Bianca Pizzorno , Vijaya J. Judith , Bellucci S.
Published: May 2021

The development of sensors for the detection of different substances is a worldwide demand. Nanotechnology has allowed great advances in this area in an attempt to bring new possibilities. Sensors with perovskite and spinel structures have been widely investigated and demonstrated interesting performances, due to their fascinating optical, magnetic, and electrical properties. Despite this advancement, some limitations and drawbacks have to be resolved. Moreover, the combination of such structures with graphene derivatives has attracted particular attention in recent years; so far the obtained results manifest improved sensors’ performance, thanks to the effective combination of their individual properties. This comprehensive review aims to highlight the recent advances in Electrochemical and Biological nanosensors based on spinel and perovskite structures without and with graphene derivatives, by presenting sensing applications of diverse substances alongside advantages and futures challenges.
Effects of Palladium Chloride Concentration on the Nanoscale Surface Morphology of Electroless Deposited Palladium Thin Film
Authors: Mpofu Brighton. S , Tapiwanashe Mangosho , Tatenda Rugwejera , Munjeri Kudakwashe , Oscillate Dera T , Timothy Gutu
Published: May 2021

Understanding the effects of thin film electroless deposition parameters at nanoscale is crucial for complete understanding and control of the thin film deposition process. In this study, we investigated and optimized the effect of

PdCl

2

precursor concentration on the nanoscale surface morphology of electroless deposited Pd thin film. The FESEM characterization of plain substrates showed that the dominant features of plain alumina substrates were terraces, steps and bumpy microstructures and the final surface morphologies of the deposited Pd thin film was strongly dependant on the surface morphologies of the substrate. FESEM characterization results of seeding technique displayed a thin film of Pd nanoparticles on the surface of the alumina substrate. The number of times that the seeding process was carried out was optimized at five seeding times using hydrazine as a reducing agent. FESEM characterization revealed that the nanoscale surface morphology of the Pd thin film was strongly dependent on

PdCl

2

precursor concentration. Three types of secondary nanoscale surface morphologies formed were nanorods, nanoflakes and flower-like Pd nanostructures at various concentrations. The nanoflake surface density was strongly dependent on

PdCl

2

precursor concentration. Results of this research provided a foundation and method to tailor the nanoscale surface morphology to the specific requirement of surface dependent processes or reactions.
Media composition and O2 levels determine effects of 17β-estradiol and selective estrogen receptor modulators on mitochondrial bioenergetics and cellular reactive oxygen species
Authors: Moradi Fereshteh , Fiocchetti Marco , Marino Maria , Moffatt Christopher , Stuart Jeffrey A.
Published: May 2021

Estradiol (E2) and selective estrogen receptor modulators (SERMs) have broad-ranging cellular effects that include mitochondrial respiration and reactive oxygen species (ROS) metabolism. Many of these effects have been studied using cell culture models. Recent advances have revealed the extent to which cellular metabolism is affected by the culture environment. Cell culture media with metabolite composition similar to blood plasma [e.g., Plasmax, Human Plasma-Like Medium (HPLM)] alter cellular behaviors including responses to drugs. Similar effects have been observed with respect to O2 levels in cell culture. Given these observations, we investigated whether the effects of E2 and SERMs are also influenced by media composition and O2 level during cell culture experiments. We analyzed mitochondrial network characteristics, cellular oxidative metabolism, and H2O2 production in C2C12 myoblasts growing in physiological (5%) or standard cell culture (18%)O2 and in physiological (Plasmax) or standard cell culture [Dulbecco’s modified Eagle’s medium (DMEM)] media. Although E2 significantly lowered H2O2 production from cells growing in 18% O2/DMEM (standard cell culture), it had no effect on cells growing in Plasmax. Moreover, culture conditions significantly altered the effects of E2 and SERMs on mitochondrial abundance and network characteristics. These results indicate that the effects of E2 and SERMs on various aspects of cell physiology strongly depends on growth conditions, which in turn emphasizes the need to consider this carefully in cell culture experiments.
LncRNA GAS5 inhibits miR-579-3p to activate SIRT1/PGC-1α/Nrf2 signaling pathway to reduce cell pyroptosis in sepsis-associated renal injury
Authors: Ling Hua , Li Qiong , Duan Ze-Peng , Wang Yu-Jing , Hu Bing-Qin , Dai Xin-Gui
Published: May 2021

Sepsis is a life-threatening condition that can lead to several organ failures including kidney. In this study, we investigated the roles of GAS5 and miR-579-3p in regulating cell pyroptosis in the sepsis-induced renal injury model. Lipopolysaccharide (LPS) treatment or cecal ligation and puncture (CLP) surgery was used to create the in vitro and in vivo sepsis-induced renal injury model. The interactions between GAS5 and miR-579-3p, and miR-579-3p and SIRT1 were determined by bioinformatic prediction, luciferase reporter assay, and RIP assay. In vitro cell pyroptosis was examined by flow cytometry marked with active caspase-1 and PI. The protein levels of IL-1β and IL-18 induced by cell pyroptosis were quantified using ELISA assay. In vivo renal injuries were evaluated with HE and TUNEL stainings, bacterial load in serum and creatinine, and blood urea nitrogen content analyses. Expression levels of GAS5, miR-579-3p, pyroptosis, and SIRT1/PGC-1a/Nrf2 pathway-related molecules were evaluated by qRT-PCR or Western blot. GAS5 and SIRT1 were downregulated, whereas miR-579-3p was upregulated in in vitro and in vivo sepsis-induced renal injury models. GAS5 negatively and directly regulated miR-579-3p to reduce cell pyroptosis via the activation of SIRT1/PGC-1a/Nrf2 pathway. In addition, miR-579-3p suppressed PGC-1a/Nrf2 pathway to induce cell pyroptosis by directly targeting SIRT1. What’s more, overexpression of GAS5, or knockdown of miR-579-3p, enhanced SIRT1 expression that led to the improved survival rate, reduced the weight loss, and relieved renal injuries in septic mice. Overexpression of GAS5 demonstrated protective effects against sepsis-induced renal injury via downregulating miR-579-3p and activating SIRT1/PGC-1α/Nrf2 pathway to inhibit cell pyroptosis.
Early extubation to noninvasive respiratory support of former preterm lambs improves long-term respiratory outcomes
Authors: Dahl Mar Janna , Veneroni Chiara , Lavizzari Anna , Bowen Sydney , Emerson Haleigh , Rebentisch Andrew , Dawson Elaine , Summers Kyle , Pettet Luke , Wang Zhengming , Null Donald M. , Yoder Bradley A. , Dellacà Raffaele L. , Albertine Kurt H.
Published: May 2021

Invasive mechanical ventilation (IMV) and exposure to oxygen-rich gas during early postnatal life are contributing factors for long-term pulmonary morbidities faced by survivors of preterm birth and bronchopulmonary dysplasia. The duration of IMV that leads to long-term pulmonary morbidities is unknown. We compared two durations of IMV (3 h vs. 6 days) during the first 6–7 days of postnatal life in preterm lambs to test the hypothesis that minimizing the duration of IMV will improve long-term respiratory system mechanics and structural outcomes later in life. Moderately preterm (∼85% gestation) lambs were supported by IMV for either 3 h or 6 days before weaning from all respiratory support to become former preterm lambs. Respiratory system mechanics and airway reactivity were assessed monthly from 1 to 6 mo of chronological postnatal age by the forced oscillation technique. Quantitative morphological measurements were made for smooth muscle accumulation around terminal bronchioles and indices of alveolar formation. Minimizing IMV to 3 h led to significantly better ( P < 0.05) baseline respiratory system mechanics and less reactivity to methacholine in the first 3 mo of chronological age (2 mo corrected age), significantly less ( P < 0.05) accumulation of smooth muscle around peripheral resistance airways (terminal bronchioles), and significantly better ( P < 0.05) alveolarization at the end of 5 mo corrected age compared with continuous IMV for 6 days. We conclude that limiting the duration of IMV following preterm birth of fetal lambs leads to better respiratory system mechanics and structural outcomes later in life.
Airway compliance measurements in mouse models of respiratory diseases
Authors: Robichaud Annette , Fereydoonzad Liah , Collins Samuel L. , Loube Jeffrey Martin , Ishii Yumiko , Horton Maureen R. , Martin James G. , Mitzner Wayne
Published: May 2021

The quantification of airway compliance ( Caw) is essential to the study of airway alterations in disease models. However, the required measurements of airway pressure and volume are difficult to acquire in mice. We hypothesized that the inflation limb of full-range pressure-volume (PV) curves could be used to quantify Caw, as it contains a segment where only the airway tree is distended. The study objective was to assess the feasibility of the approach by analysis of full-range PV curves previously collected in three mouse models: an elastase model of emphysema, a genetic model spontaneously developing emphysema (leukotriene C4 synthase knockout; LTC4S-KO), and a bleomycin model of lung fibrosis. Attempts to validate results included Caw change relative to respiratory system compliance (Δ Caw/Δ C), the minute work of breathing (mWOB), and the elastance at 20.5 Hz ( Ers_20.5) from prior respiratory mechanics measurements in the same subjects. Caw was estimated at 3% of total compliance in healthy mice or 2.3 ± 1 μL/cmH2O ( n = 17). The technique detected changes in models of respiratory obstructive and restrictive diseases relative to control mice as well as differences in the two emphysema models studied. The changes in Caw were consistent with those seen in Δ Caw/Δ C, mWOB, or Ers_20.5, with some variations according to the model, as well as with results reported in the literature in humans and mice. Direct Caw measurements in subjects as small as mice could prove useful to further characterize other respiratory disease models associated with airway remodeling or to assess treatment effects.
A Review of Data Envelopment Analysis in Airline Efficiency: State of the Art and Prospects
Authors: Cui Qiang , Yu Li-Ting
Published: May 2021

The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions.
Locating and Multiplicative Locating Indices of Graphs with QSPR Analysis
Authors: Wazzan Suha , Saleh Anwar
Published: May 2021

In this paper, by introducing a new version of locating indices called multiplicative locating indices, we compute exact values of these indices on well-known families of graphs and graphs obtained by some operations. Also, we determine the importance of locating and multiplicative locating indices of hexane and its isomers. Furthermore, we show that locating indices actually have a reasonable correlation using linear regression with physico-chemical characteristics such as enthalpy, melting point, and boiling point. This approximation can be extended into several chemical compounds.
Research on Seismic Performance of an Innovative Upper‐Lower Half‐Story Precast Shear Wall
Authors: Sun Yanyan , Wang Zhenbo , Xu Jing
Published: May 2021

An innovative precast connection (the precast connection installed in the middle of the shear wall) was proposed for the shear wall. To verify the effectiveness of the proposed precast connection, two cast‐in‐situ shear walls (RCW1 and RCW2) and three precast shear walls (PCW1, PCW2, and PCW3) were manufactured and investigated. The construction joints were inserted in the bottom and the middle for RCW1 and RCW2; and the structural glue horizontal connection, structural glue cogged connection, and cast‐in‐situ plug grouting connection were utilized for PCW1, PCW2, and PCW3, respectively. The failure mode, loading capacity, ductility, stiffness degradation, and energy dissipation of specimens were analyzed under the horizontal low‐frequency cycled loading. Simultaneously, a numerical simulation was carried out on the ABAQUS software, and simulation results were consistent with experimental results. The result showed that the moment‐shear failure occurred in all the specimens except PCW1; the bottoms of PCW2 and PCW3 were still vulnerable regions. The bearing capacity and the ductility of RCW2 were improved to different degrees by installing the construction joint in the middle of the shear wall. Specifically, the structural glue cogged connection and the cast‐in‐situ plug grouting connection have no obvious effect on the reduction of bearing capacity but can improve the ductility of the specimen; the stiffness degradation and energy dissipation of RCW1, RCW2, PCW2, and PCW3 were basically the same.
Strong GE-Filters and GE-Ideals of Bordered GE-Algebras
Authors: Öztürk Mehmet Ali , Lee Jeong-Gon , Bandaru Ravikumar , Jun Young Bae
Published: May 2021

The notion of strong GE-filters and GE-ideals (generated) is introduced, and the related properties are investigated. The intersection of strong GE-filters (resp., GE-ideals) is proved to be a strong GE-filter (resp., GE-ideal), and the union of strong GE-filters (resp., GE-ideals) is generally not a strong GE-filters (resp., GE-ideal) by example. Conditions for a subset of a bordered GE-algebra to be a strong GE-filter are provided, and a characterization of a strong GE-filter is considered. In order to do so, irreducible GE-filter is defined first and its properties are examined. Conditions for a GE-filter to be irreducible are discussed. Given a GE-filter, and a subset in a bordered GE-algebra, the existence of an irreducible GE-filter, which contains the given GE-filter and is disjoint to the given subset, is considered. Conditions under which any subset of a bordered GE-algebra can be a GE-ideal are provided, and GE-ideal that is generated from a subset in a bordered GE-algebra is discussed. Also, what element it is formed into is stated. Finally, the smallest GE-ideal which contains a given GE-ideal and an element in a bordered GE-algebra is established.
Identification of Novel Biomarkers for Evaluating Disease Severity in House-Dust-Mite-Induced Allergic Rhinitis by Serum Metabolomics
Authors: Xie Shaobing , Zhang Hua , Xie Zhihai , Liu Yongzhen , Gao Kelei , Zhang Junyi , Xie Shumin , Wang Fengjun , Fan Ruohao , Jiang Weihong
Published: May 2021

The aim of this study was to identify differences in serum metabolomics profiles of house-dust-mite (HDM)-induced allergic rhinitis (AR) patients compared to controls and to explore novel biomarkers reflecting disease severity. Serum samples were collected from 29 healthy controls and HDM-induced 72 AR patients, including 30 mild patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomics detection was performed, and orthogonal partial least square discriminate analysis was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from controls. MSAR patients also could be discriminated from MAR patients based on their metabolic fingerprints. Most observed metabolite changes were related to glycine, serine, and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism, and fatty acid metabolism. Levels of sarcosine, sphingosine-1-phosphate, cytidine, and linoleic acid significantly correlated with the total nasal symptom score and visual analogue scale in AR patients. These results suggest that metabolomics profiling may provide novel insights into the pathophysiological mechanisms of HDM-induced AR and contribute to its evaluation of disease severity.
Aboriginal Bacterial Flora in the Uricase-Deficient Rat Gut is Not the Main Factor Affecting Serum Uric Acid
Authors: Fan Nan , Li Lvyu , Xia Heng , Gao Yinfang , Li Yongkun , Chen Huan , Yun Yu , Duan Weigang
Published: May 2021

The relationship between intestinal bacteria and hyperuricemia is a hot research topic. To better understand this relationship, uricase-deficient Sprague–Dawley rats (Kunming-DY rats) were used. The wild-type rats and Kunming-DY rats were used as controls. Kunming-DY rats were treated with ampicillin (90 mg/kg) and ciprofloxacin (150 mg/kg) for 5 days. Bacterial 16S rDNA in the fresh stool was sequenced, and the abundance was calculated. The rats’ serum uric acid (SUA) level was assayed, and the rats’ intake and output in 24 h were recorded. The bacterial diversity in three groups’ fresh stool was analyzed. The gut bacterial diversity and abundance changed in the Kunming-DY rats. More than 99% of bacteria were inhibited or killed by the combination of antibiotics. In contrast to each of the antibiotics alone, the combination of antibiotics lowered the Kunming-DY rats’ SUA level; it also caused mild diarrhea, which increased uric acid excretion through stool. These results suggested that the aboriginal gut bacteria in uricase-deficient rats play a minor role in determining the SUA levels. It is too early to conclude that aboriginal gut bacteria are a tempting target for lowering SUA levels.
Anterior Gradient Protein 2 Promotes Mucosal Repair in Pediatric Ulcerative Colitis
Authors: Ye Xiaolin , Wu Jie , Li Jing , Wang Hongyu
Published: May 2021

Mucosal healing comprises a key goal of ulcerative colitis (UC) treatment. Anterior gradient protein 2 (AGR2) plays an important role in maintaining intestinal homeostasis in UC. However, the role of AGR2 in the repair of mucosal injury is not yet clear. This study is aimed at investigating the expression of AGR2 in the intestinal tissues of children with UC and its role in repairing mucosal injury. Forty UC patients who were hospitalized in the Pediatric Gastroenterology Ward of Shengjing Hospital affiliated with China Medical University between July 1, 2013, and May 31, 2020, and 20 children who had normal colonoscopy results during the same period (control group) made up the study sample. The disease activity of UC was evaluated based on the pediatric ulcerative colitis activity index, and the ulcerative colitis endoscopic index was evaluated according to the Rachmilewitz score. Immunohistochemical staining was employed to examine the differences in AGR2 expression in the intestinal mucosa between groups. The protective effect of AGR2 in a model of tumor necrosis factor‐alpha‐ (TNF‐α‐) induced intestinal mucosal barrier injury and the underlying molecular mechanism were explored through in vitro experiments. The results showed that compared with the normal control group, UC patients in the remission or active period had significantly higher expression of AGR2 in the intestine. AGR2 expression was positively correlated with Ki67, an intestinal epithelial cell proliferation marker, but negatively correlated with the degree of endoscopic mucosal injury. In an in vitro model, AGR2 overexpression promoted cell proliferation and migration and inhibited TNF‐α‐induced intestinal epithelial barrier damage by activating yes‐associated protein (YAP). Collectively, our study suggests that AGR2 might serve as a valuable biomarker to help assess the condition and mucosal healing status of UC patients. In vitro, AGR2 promoted the repair of intestinal mucosal barrier injury by activating YAP.
Set-Valued SU-Type Fixed Point Theorems via Gauge Function with Applications
Authors: Ali Amjad , Alansari Monairah , Uddin Fahim , Arshad Muhammad , Asif Awais , Basendwah Ghada Ali
Published: May 2021

In this article, we have designed two existence of fixed point theorems which are regarding to set-valued SU-type

θ

η

-contraction and

Γ

α

-contraction via gauge function in the setting of metric spaces. An extensive set of nontrivial example will be given to justify our claim. At the end, we will give an application to prove the existence behavior for the system of functional equation in dynamical system and integral inclusion.
Evaluation of Aloe weloensis (Aloeacea) Mucilages as a Pharmaceutical Suspending Agent
Authors: Mengesha Yohannes , Tuha Abdu , Seid Yimer , Adem Admassu Assen
Published: May 2021

Natural polymers, specifically mucilages, have been used as a suspending agent for a long period of time. Natural excipients can serve as an alternative to synthetic products since they are less expensive, less toxic, and devoid of environmental pollution. There are many species of Aloe found in Ethiopia which can be used as a source of mucilage. In this study, mucilage from Aloe weloensis, which is found in Wollo floristic region, was extracted and tested as a suspending agent at different suspending agent concentrations and compared with standard suspending agents (acacia and sodium carboxy methylcellulose (NaCMC)) by formulating zinc oxide suspension. The mucilage obtained from Aloe weloensis leaves has shown comparable suspending agent ability with acacia. The rate of sedimentation and viscosity was higher at 1% and 4% mucilage concentrations than acacia though the difference was not significant (

p
>
0.05

). The suspension was slightly basic and easily dispersible than NaCMC. Suspensions formulated from NaCMC were superior in terms of viscosity and sedimentation volume which was significantly different (

p
<
0.05

) accompanied by lower flow rates than suspensions formulated from acacia and Aloe weloensis mucilages. The results suggested that Aloe weloensis mucilage could be used as an alternative suspending agent.
The Mixture of the Marshall–Olkin Extended Weibull Distribution under Type-II Censoring and Different Loss Functions
Authors: Alotaibi Refah , Khalifa Mervat , Baharith Lamya A. , Dey Sanku , Rezk H.
Published: May 2021

To study the heterogeneous nature of lifetimes of certain mechanical or engineering processes, a mixture model of some suitable lifetime distributions may be more appropriate and appealing as compared to simple models. This paper considers a mixture of the Marshall–Olkin extended Weibull distribution for efficient modeling of failure, survival, and COVID-19 data under classical and Bayesian perspectives based on type-II censored data. We derive several properties of the new distribution such as moments, incomplete moments, mean deviation, average lifetime, mean residual lifetime, Rényi entropy, Shannon entropy, and order statistics of the proposed distribution. Maximum likelihood and Bayes procedure are used to derive both point and interval estimates of the parameters involved in the model. Bayes estimators of the unknown parameters of the model are obtained under symmetric (squared error) and asymmetric (linear exponential (LINEX)) loss functions using gamma priors for both the shape and the scale parameters. Furthermore, approximate confidence intervals and Bayes credible intervals (CIs) are also obtained. Monte Carlo simulation study is carried out to assess the performance of the maximum likelihood estimators and Bayes estimators with respect to their estimated risk. The flexibility and importance of the proposed distribution are illustrated by means of four real datasets.
China’s Stem Cell Research and Knowledge Levels of Medical Practitioners and Students
Authors: Luo Deng , Xu Zihui , Wang Zhongjing , Ran Wenzhuo
Published: May 2021

Over the last few decades, China has greatly expanded its scope of stem cell research, generating various scientific advances and medical applications. However, knowledge of the extent and characteristics of domestic stem cell development, particularly medical workers’ opinions, is lacking. This study’s purposes were to analyze the growth trends of China’s stem cell community and identify the knowledge and attitudes held by Chinese medical workers regarding stem cell research. We found that there are currently 13 high-quality stem cell research centers with more than 400 PhD-level researchers across Mainland China. These centers feature many high-caliber scientists from the stem cell research community. From 1997 through 2019, the National Natural Science Foundation of China allocated roughly $576 million to 8,050 stem cell programs at Chinese universities and research institutions. China’s annual publications on stem cells increased from less than 0.6% of the world’s total stem cell publications in 1999 to more than 14.1% in 2014. Our survey also revealed that most participants held positive attitudes toward stem cell research, supported further funding, and had high general awareness about stem cells.
Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining
Authors: Gao Fan , Li Fan , Wang Zhifei , Ge Wenqi , Li Xinqin
Published: May 2021

In this paper, the multilevel classification model of high-speed railway signal equipment fault based on text mining technology is proposed for the data of high-speed railway signal fault. An improved feature representation method of TF-IDF is proposed to extract the feature of fault text data of signal equipment. In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. The multitask cooperative voting decision tree was designed to correct the membership relationship of classification results of each layer. Ten years of signal switch machine fault data of high-speed railway are used for experimental analysis; the experiment shows that the multilevel classification model can effectively improve the classification of signal equipment fault multilevel classification task evaluation index and can ensure the correctness of the subordinate relations’ classification results.
Advanced Fuzzy-Logic-Based Traffic Incident Detection Algorithm
Authors: Zhu Changhong , Guo Zhenjun , Ke Jie
Published: May 2021

This study demonstrates an incident detection algorithm that uses the meteorological and traffic parameters for improving the poor performance of the automatic incident detection (AID) algorithms under extreme weather conditions and for efficiently using the meteorological devices on advanced freeways. This algorithm comprises an incident detection module that is based on learning vector quantization (LVQ) and a meteorological influencing factor module. Field data are obtained from the Yuwu freeway in Chongqing, China, to verify the algorithm. Further, the performance of this algorithm is evaluated using commonly used criteria such as mean time to detection (MTTD), false alarm rate (FAR), and detection rate (DR). Initially, an experiment is conducted for selecting the algorithm architecture that yields the optimal detection performance. Additionally, a comparative experiment is performed using the California algorithm, exponential smoothing algorithm, standard normal deviation algorithm, and McMaster algorithm. The experimental results demonstrate that the algorithm proposed in this study is characterized by high DR, low FAR, and considerable suitability for applications in AID.
[Retracted] Forecast and Simulation of the Public Opinion on the Public Policy Based on the Markov Model
Authors: Li Zi
Published: May 2021

Public policy and public opinion directly affect the image of the government, but due to the lack of appropriate monitoring and early warning tools, the government’s handling of credit changes is seriously lagging behind. In response to this problem, this paper integrates the internet, public information, market credit information, and other data, uses hidden Markov models and natural language processing technology, and establishes a modern government public policy and public opinion monitoring and early warning model to evaluate government credit in real time; the government can formulate relevant policies based on the evaluation results to improve the government’s governance capabilities. Empirical analysis shows that, based on the dynamic scoring framework and Markov model, the government credit monitoring and early warning models established, respectively, have 90% of the reference value, and the analysis results have the same reference. This method can effectively predict the trend of hot online public opinion. The subsequent establishment of an online public opinion early warning system and an online public opinion guidance mechanism provided theoretical support.
E-Commerce Enterprise Supply Chain Financing Risk Assessment Based on Linked Data Mining and Edge Computing
Authors: Qu Qiao , Liu Cheng , Bao Xinzhong
Published: May 2021

In recent years, the rapid development of information technology has affected the way the world economy operates. The emergence of e-commerce has greatly shortened the time and space distance between economic participants and maximized the sharing of resources. However, the financial management and risk assessment capabilities of the existing supply are insufficient to adapt to the rapidly developing new environment. This article uses a combination of normative analysis and empirical analysis to analyze the status quo of the supply chain of small and medium e-commerce companies. First, this article establishes an evaluation framework for the supply chain of e-commerce companies based on edge computing. Second, according to the distribution of the supply chain, this article adds the member’s predetermined quota, reputation, execution time, and other indicators as parameters to establish a fuzzy neural network model. On this basis, combined with the price regression model, the pricing plan is evaluated. The results show that the financing risk obtained by this model differs very little from the actual risk. The above-mentioned model constructs an e-commerce enterprise supply chain financing risk management model that adapts to the environment of the new era.
Imaging Assessment of the Postoperative Spine: An Updated Pictorial Review of Selected Complications
Authors: Corona-Cedillo Roberto , Saavedra-Navarrete Melanie-Tessa , Espinoza-Garcia Juan-Jose , Mendoza-Aguilar Alexela-Nerey , Ternovoy Sergey K. , Roldan-Valadez Ernesto
Published: May 2021

Imaging of the postoperative spine requires the identification of several critical points by the radiologist to be written in the medical report: condition of the underlying cortical and cancellous bone, intervertebral disc, and musculoskeletal tissues; location and integrity of surgical implants; evaluation of the success of decompression procedures; delineation of fusion status; and identification of complications. This article presents a pictorial narrative review of the most common findings observed in noninstrumented and instrumented postoperative spines. Complications in the noninstrumented spine were grouped in early (hematomas, pseudomeningocele, and postoperative spine infection) and late findings (arachnoiditis, radiculitis, recurrent disc herniation, spinal stenosis, and textiloma). Complications in the instrumented spine were also sorted in early (hardware fractures) and late findings (adjacent segment disease, hardware loosening, and implant migration). This review also includes a short description of the most used diagnostic techniques in postoperative spine imaging: plain radiography, ultrasound (US), computed tomography (CT), magnetic resonance (MR), and nuclear medicine. Imaging of the postoperative spine remained a challenging task in the early identification of complications and abnormal healing process. It is crucial to consider the advantages and disadvantages of the imaging modalities to choose those that provide more accurate spinal status information during the follow‐up. Our review is directed to all health professionals dealing with the assessment and care of the postoperative spine.
Optimization Model and Method of Variable Speed Limit for Urban Expressway
Authors: Li Shubin , Wang Tao , Ren Hualing , Shi Baiying , Kong Xiangke
Published: May 2021

The urban expressway network is the main part of the urban traffic network carrying most of the city’s traffic pressure for its continuity and rapidity, but the control method of the traffic flow was too simple to other control methods in application in addition to the ramp control and the fixed speed control. In this paper, the theory of variable speed limit (VSL) was used to develop an optimal control model based on the improved traffic flow simulation model according to the characteristics of urban expressway traffic flow. The objective of the proposed model is to minimize the delay and maximize the traffic flow. It can adjust the traffic flow on the network in space time so that the whole network is in a state of equilibrium which not only is conducive to the control of the local traffic congestion and avoids the spread of congestion but also improves the traffic safety. The SPSA-based solution algorithm was proposed by taking into account the needs of real-time online applications. It can not only ensure the accuracy of the solution but also meet the requirements of the simulation time. The simulation results show that the variable speed limit can be optimized in moderate demand, and the proposed model and algorithm are effective and feasible in this paper. The conclusions are useful to help the traffic management department to formulate reasonable traffic control strategies.
Drug Disease Relation Extraction from Biomedical Literature Using NLP and Machine Learning
Authors: Ben Abdessalem Karaa Wahiba , Alkhammash Eman H. , Bchir Aida
Published: May 2021

Extracting the relations between medical concepts is very valuable in the medical domain. Scientists need to extract relevant information and semantic relations between medical concepts, including protein and protein, gene and protein, drug and drug, and drug and disease. These relations can be extracted from biomedical literature available on various databases. This study examines the extraction of semantic relations that can occur between diseases and drugs. Findings will help specialists make good decisions when administering a medication to a patient and will allow them to continuously be up to date in their field. The objective of this work is to identify different features related to drugs and diseases from medical texts by applying Natural Language Processing (NLP) techniques and UMLS ontology. The Support Vector Machine classifier uses these features to extract valuable semantic relationships among text entities. The contributing factor of this research is the combination of the strength of a suggested NLP technique, which takes advantage of UMLS ontology and enables the extraction of correct and adequate features (frequency features, lexical features, morphological features, syntactic features, and semantic features), and Support Vector Machines with polynomial kernel function. These features are manipulated to pinpoint the relations between drug and disease. The proposed approach was evaluated using a standard corpus extracted from MEDLINE. The finding considerably improves the performance and outperforms similar works, especially the f-score for the most important relation “cure,” which is equal to 98.19%. The accuracy percentage is better than those in all the existing works for all the relations.