bluuebunny/crossref_metadata_2025 huggingface dataset:
Combined Mixed Potential Electrochemical Sensors and Artificial Neural Networks for the Quantificationand Identification of Methane in Natural Gas Emissions Monitoring
Authors: Halley Sleight , Tsui Lok-kun , Garzon Fernando
Published: September 2021

Sensors capable of quantifying methane concentration and discriminating between possible sources are needed for natural gas leak detection where multiple spatially overlapping sources including wetlands and agriculture may be present. We report on the fabrication by an additive manufacturing process of a four electrode La0.87Sr0.13CrO3, Indium Tin Oxide (In2O3 90 wt%, SnO2 10 wt%), Au, Pt mixed potential electrochemical sensor using yttria-stabilized zirconia (YSZ) as a solid electrolyte to natural gas detection. Artificial neural networks (ANNs) are used to automatically decode the possible source and concentration of methane. The ANNs trained on sensor data are capable of correctly discriminating between three sources of methane emissions from simulated mixtures of emissions from cattle, wetlands, or natural gas with >98% accuracy. Quantification error for methane in mixtures of CH4 in air, CH4 + NH3 in air, and simulated natural gas is less than 1.5% ppm when a two-temperature dataset is employed.
LncRNA NKILA Promotes Epithelial-Mesenchymal Transition of Liver Cancer Cells by Targeting miR-485-5p
Authors: Wang Yuxu , Li Chao , Shi Yuyi , Kuai Jing
Published: September 2021

Objective. Liver cancer (LC), one of the familiar malignancies, has a very high morbidity all over the world. The onset of the disease is hidden, and the patients usually do not express any special symptoms. Most of them will have been developed to the middle and later stage when they are diagnosed. This is one of the main reasons why the prognosis of LC is extremely pessimistic all the year round. Recently, researchers have focused mainly on molecular studies, among which LncRNA is a hot spot. This research aims to explore the biological behaviors of LncRNA NKILA and miR-485-5p in LC cells and verify the relationship between them, thereby providing a new theoretical basis for future prevention and treatment. Methods. Ninety-four early LC patients admitted to our hospital from January 2015 to January 2017 were regarded as the research objects. In addition, human LC cells SMMC-7721, HepG2, and normal liver cells HL-7702 were purchased. The LncRNA NKILA and miR-485-5p level in cancer and adjacent tissues, LC, and normal liver cells of patients was tested by PCR. Patients were followed up for 3 years. Then, LncRNA NKILA and miR-485-5p’s effects on prognosis and cell biological behavior were analyzed. At last, the relationship between LncRNA NKILA and miR-485-5p was assessed by a dual-luciferase reporter assay. Results. The LncRNA NKILA expression was high in LC tissues and cells (

P
<
0.050

), while miR-485-5p was low compared with the normal adjacent tissues (

P
<
0.050

). Prognostic follow-up manifested that high LncRNA NKILA or low miR-485-5p could predict the poor prognosis and high mortality risk of the patients (

P
<
0.050

). LC cells with downregulated LncRNA NKILA documented inhibited proliferation, invasion, and EMT, while the apoptosis level of the cells increased (

P
<
0.050

). The proliferation, invasion, and EMT were inhibited by miR-485-5p increase, while the apoptosis of the cells decreased after upregulating miR-485-5p (

P
<
0.050

). Online websites predicted that LncRNA NKILA had a binding site with miR-485-5p, and dual-luciferase reporter assay confirmed that LncRNA NKILA could directly target with miR-485-5p (

P
<
0.050

). The miR-485-5p in LC cells increased after LncRNA NKILA was silenced (

P
<
0.050

). The rescue experiment documented that LncRNA NKILA inhibition on LC cells was reversed by inhibiting miR-485-5p (

P
<
0.050

). Conclusion. The LncRNA NKILA with high expression advances LC cell proliferation, invasion, and EMT by targeting miR-485-5p.
Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy
Authors: Li Xueying , Fan Pingping , Li Zongmin , Chen Guangyuan , Qiu Huimin , Hou Guangli
Published: September 2021

Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.
Research on the Energy‐Saving Design Path of Rural Farmhouses under the Background of Ecological Livability
Authors: Shi Jingyuan , Li Qiuna
Published: September 2021

Different from the state of large‐scale energy use and concentrated energy conservation in urban areas, the state of energy use and energy conservation in rural areas has its own characteristics and typical problems. Rural areas have been concentrated on the multiple differences in economic, social, and construction characteristics such as mountain and peace dam topography, natural and artificial construction, developed and underdeveloped, rapid urban development, and relatively backward rural development. It has practical problems that need to be solved urgently, such as the “insufficient savings” in the construction of farmhouses and the “insufficient open source” of clean energy applications. The discussion will be conducted from three aspects: the planning and layout design of rural houses in Chongqing, the performance of the enclosure structure of rural houses in key seasons, and the application of renewable clean energy in rural houses. Then put forward is the energy‐saving design path of Chongqing farmhouse. It aims to improve the indoor and outdoor thermal environment of farmhouses, saving heating energy consumption in winter and air‐conditioning energy consumption in summer and effectively benefit users to use renewable energy such as biogas, straw, and solar energy. Finally, explore the adaptability between the design methods of farmhouses and the local characteristics of Chongqing’s rural areas. In the work of Chongqing Design Going to the Countryside, we create a contemporary farmhouse case that is not only energy‐saving but also has good indoor and outdoor space quality. It also provides a reference for the energy‐saving design of farmhouses in Chongqing and other regions in China.
Identification of Novel Choroidal Neovascularization‐Related Genes Using Laplacian Heat Diffusion Algorithm
Authors: Sheng Minjie , Cai Haiying , Cheng Ming , Li Jing , Zhang Jian , Liu Lihua
Published: September 2021

Choroidal neovascularization (CNV) is a type of eye disease that can cause vision loss. In recent years, many studies have attempted to investigate the major pathological processes and molecular pathogenic mechanisms of CNV. Because many diseases are related to genes, the genes associated with CNV need to be identified. In this study, we proposed a network‐based approach for identifying novel CNV‐associated genes. To execute such method, we first employed a protein‐protein interaction network reported in STRING. Then, we applied a network diffusion algorithm, Laplacian heat diffusion, on this network by selecting validated CNV‐related genes as the seed nodes. As a result, some novel genes that had unknown but strong relationships with validated genes were identified. Furthermore, we used a screening procedure to extract the most essential genes. Eleven latent CNV‐related genes were finally obtained. Extensive analyses were performed to confirm that these genes are novel CNV‐related genes.
[Retracted] Osthole Attenuates Bleomycin‐Induced Pulmonary Fibrosis by Modulating NADPH Oxidase 4‐Derived Oxidative Stress in Mice
Authors: Fang Lijun , Wang Wei , Chen Jiazheng , Zuo Anju , Gao Hongmei , Yan Tao , Wang Pengqi , Lu Yujia , Lv Ruijuan , Xu Feng , Chen Yuguo , Lyu Linmao
Published: September 2021

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease characterized by the extensive accumulation of myofibroblasts and collagens. However, the exact mechanism that underlies this condition is unclear. Growing evidence suggests that NADPH oxidases (NOXs), especially NOX4‐derived oxidative stress, play an important role in the development of lung fibrosis. Bleomycin (BLM) is a tumor chemotherapeutic agent, which has been widely employed to establish IPF animal models. Osthole (OST) is an active constituent of the fruit of Cnidium ninidium. Here, we used an in vivo mouse model and found that OST suppressed BLM‐induced body weight loss, lung injury, pulmonary index increase, fibroblast differentiation, and pulmonary fibrosis. OST also significantly downregulated BLM‐induced NOX4 expression and oxidative stress in the lungs. In vitro, OST could inhibit TGF‐β1‐induced Smad3 phosphorylation, differentiation, proliferation, collagen synthesis, NOX4 expression, and ROS generation in human lung fibroblasts in a concentration‐dependent manner. Moreover, NOX4 overexpression could prevent the above effects of OST. We came to the conclusion that OST could significantly attenuate BLM‐induced pulmonary fibrosis in mice, via the mechanism that involved downregulating TGF‐β1/NOX4‐mediated oxidative stress in lung fibroblasts.
Effect of Experimental Blocking on the Suppression of Spatial Dependence Potentially Attributable to Physicochemical Properties of Soils
Authors: Darghan Aquiles E. , Reyes Giovanni , Rivera Carlos A. , Grisales Edwin F.
Published: September 2021

One of the basic principles of experimental design is blocking, which is an important factor in the treatment of the systematic spatial variability that can be found in the edaphic properties where agricultural experiments are conducted. Blocking has a mitigating or suppressing effect on the spatial dependence in the residuals of a model, something desirable in standard linear modeling, specifically in design models. Some computer programs yield a

p

value associated with the blocking effect in the analysis of variance table that in many cases has been incorrectly used to discard it, and although it may improve some properties of the analysis, it may affect the independence assumption required in several models. Therefore, the present research recommends the use of the

H

statistic associated with the corrected blocking efficiency to show the role of blocking in modeling with the incorporation of an additional advantage rarely considered related to the suppression or mitigation of spatial dependence. With the use of the Moran index, the spatial dependence of the residuals was studied in a simple factorial design in a completely randomized and blocking field layout, which evidenced the advantages of blocking in the mitigation or suppression of the spatial dependence despite the apparently little or no importance it seems to show in the analysis of variance table.
Three-Dimensional Diffusion Model in Sports Dance Video Human Skeleton Detection and Extraction
Authors: Li Zhi
Published: September 2021

The research in this paper mainly includes as follows: for the principle of action recognition based on the 3D diffusion model convolutional neural network, the whole detection process is carried out from fine to coarse using a bottom-up approach; for the human skeleton detection accuracy, a multibranch multistage cascaded CNN structure is proposed, and this network structure enables the model to learn the relationship between the joints of the human body from the original image and effectively predict the occluded parts, allowing simultaneous prediction of skeleton point positions and skeleton point association information on the one hand, and refinement of the detection results in an iterative manner on the other. For the combination problem of discrete skeleton points, it is proposed to take the limb parts formed between skeleton points as information carriers, construct the skeleton point association information model using vector field, and consider it as a feature, to obtain the relationship between different skeleton points by using the detection method. It is pointed out that the reorganization problem of discrete skeleton points in multiperson scenes is an NP-Hard problem, which can be simplified by decomposing it into a set of subproblems of bipartite graph matching, thus proposing a matching algorithm for discrete skeleton points and optimizing it for the skeleton dislocation and algorithm problems of human occlusion. Compared with traditional two-dimensional images, audio, video, and other multimedia data, the 3D diffusion model data describe the 3D geometric morphological information of the target scene and are not affected by lighting changes, rotation, and scale transformation of the target and thus can describe the realistic scene more comprehensively and realistically. With the continuous updating of diffusion model acquisition equipment, the rapid development of 3D reconstruction technology, and the continuous enhancement of computing power, the research on the application of 3D diffusion model in the detection and extraction of a human skeleton in sports dance videos has become a hot direction in the field of computer vision and computer graphics. Among them, the feature detection description and model alignment of 3D nonrigid models are a fundamental problem with very important research value and significance and challenging at the same time, which has received wide attention from the academic community.
Research on Data Analysis of Traditional Chinese Medicine with Improved Differential Evolution Clustering Algorithm
Authors: Zhu Honglei , Zhao Yingying , Wang Xueyun , Xu Yulong
Published: September 2021

Medical data analysis is an important part of intelligent medicine, and clustering analysis is a commonly used method for data analysis of Traditional Chinese Medicine (TCM); however, the classical K-Means algorithm is greatly affected by the selection of initial clustering center, which is easy to fall into the local optimal solution. To avoid this problem, an improved differential evolution clustering algorithm is proposed in this paper. The proposed algorithm selects the initial clustering center randomly, optimizes and locates the clustering center in the process of evolution iteration, and improves the mutation mode of differential evolution to enhance the overall optimization ability, so that the clustering effect can reach the global optimization as far as possible. Three University of California, Irvine (UCI), data sets are selected to compare the clustering effect of the classical K-Means algorithm, the standard DE-K-Means algorithm, the K-Means++ algorithm, and the proposed algorithm. The experimental results show that, in terms of global optimization, the proposed algorithm is obviously superior to the other three algorithms, and in terms of convergence speed, the proposed algorithm is better than DE-K-Means algorithm. Finally, the proposed algorithm is applied to analyze the drug data of Traditional Chinese Medicine in the treatment of pulmonary diseases, and the analysis results are consistent with the theory of Traditional Chinese Medicine.
Convolutional Neural Network Optimization Algorithm-Based Magnetic Resonance Imaging in Analysis of Chronic Pain Caused by the Myofascial Trigger Point
Authors: Jin Xin , Fan Lei , Yao Yongling
Published: September 2021

This study was to explore the value of magnetic resonance imaging (MRI) technology processed by convolutional neural network (CNN) optimization algorithms in the clinical research of patients with chronic pain caused by myofascial trigger points (MTrPs). Firstly, referring to the traditional iterative algorithm, this study iterated the convolution network and data consistency layer as a whole for several times, which increased the fitting ability of the data consistency layer and network. When it was applied to magnetic resonance examination, it could be concluded that the effect of its reconstruction method was better than the traditional convolution neural network without the data consistency layer. The image edge was clear, and the restoration effect of details was better. 100 patients with chronic neck pain caused by MTrP were collected and divided into an ultrasound treatment group and a local anesthetic drug injection group, with 50 cases in each group. In addition, 50 healthy volunteers were selected. After clinical treatment, the results showed that, after 3 weeks of treatment, the visual analog score (VAS) and the pain rating index (PRI) of the injection group were 3.16 ± 1.14 points and 4.92 ± 1.26 points, respectively; the present pain intensity (PPI) score was 2.06 ± 0.85 points, and the number of pain days per month was 7.73 ± 1.15. After 1 month of treatment, the VSA and PRI of the injection group were 1.24 ± 0.89 and 1.31 ± 0.97, respectively; the PPI score was 1.34 ± 0.65, and the number of pain days per month was 5.34 ± 0.98. In addition, there were 38 cases reaching the level of clinical cure, accounting for 76%. Therefore, all indicators in the injection group were better than those in the ultrasound treatment group, and the differences were statistically significant (

P
<
0.05

). The results of MRI examination showed that compared with the healthy control group, patients with chronic pain caused by the myofascial trigger point had reduced axial kurtosis (AK), mean kurtosis (MK), and radial kurtosis (RK) in multiple brain areas such as the right parahippocampal gyrus and the right medial prefrontal cortex. In short, chronic pain caused by the trigger point of the myofascial membrane would affect the microstructure of the gray matter of the patient’s brain. In clinical treatment, the efficacy of local anesthetic injection was better than ultrasound therapy.
[Retracted] Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games
Authors: Yan Zhenggang , Yu Yue , Shabaz Mohammad
Published: September 2021

The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots’ temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three‐color components in the video are combined into a one‐dimensional feature vector to obtain the quantization level of the video. The one‐dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window’s mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented.
Effect of Mechanical Vibration on Microstructure and Mechanical Properties of Gray Cast Iron in Lost Foam Casting
Authors: Qiu Kai , Xiao Bota
Published: September 2021

In order to reveal the effect of vibration solidification on microstructure and properties of gray cast iron in lost foam casting, Y-shaped HT100 gray cast iron was prepared by vibration lost foam casting, and the effect of amplitude on the microstructure and properties of gray cast iron was studied. The results showed that, with the increase in amplitude, the short and thin A-type flake graphite increases, and the austenite structure transforms from columnar to cellular. The hardness, tensile strength, and elongation of gray cast iron first increase and then decrease with the increase in amplitude. The appropriate amplitude in the process of Y-shaped HT100 gray cast iron by lost foam casting is 2 mm, while the tensile strength and elongation of gray cast iron are the highest, with the values of 147.51 MPa and 1.17%, respectively. The change of mechanical properties of gray cast iron is mainly determined by the morphology and distribution of austenite in gray cast iron and A-type flake graphite.
Correlation and Diagnostic Value of Serum Cys-C, RBP4, and NGAL with the Condition of Patients with Traumatic Acute Kidney Injury
Authors: Ye Zufeng , Liu Hui , Zhao Bixia , Fu Hao , Li Ying , Chen Li
Published: September 2021

There is a lack of targeted biomarkers that can diagnose Acute Kidney Injury (AKI) early and accurately, which leads to deterioration of renal function and even death in patients who do not receive timely and effective treatment. In recent years, an increasing number of studies have shown that AKI-related markers such as cystatin C (Cys-C) and retinol-binding protein (RBP) can be used for early diagnosis of AKI to a certain extent. A total of 262 subjects were included in this study, of which 132 patients with traumatic AKI were enrolled in one group and named as the AKI group; 130 healthy subjects were enrolled in another group and named as the healthy group. AKI patients with different conditions were classified into AKI phase I, II, and III according to the KDIGO AKI diagnostic criteria, with 45, 59, and 28 in each group. In this study, we examined and compared serum Cys-C, RBP4, and neutrophil gelatinase-associated lipid transport protein (NGAL) levels between the AKI and healthy groups and between patients with AKI of different conditions, and the correlation and diagnostic value of three serum markers with the condition of traumatic AKI patients were also analyzed. The results showed that serum Cys-C, RBP4, and NGAL were significantly higher in the AKI group compared with the healthy group (

P
<
0.05

), and the mean concentrations of the three serum markers increased as the severity of the disease increased, while correlation analysis showed that all three serum markers were positively correlated with serum Scr levels (

P
<
0.001

). Further ROC curve analysis was performed, and the diagnostic values of serum Cys C, RBP4, and NGAL alone and in combination for traumatic AKI were 0.769, 0.741, 0.771, and 0.905, respectively. In short, serum Cys C, RBP4, NGAL have important value for the assessment and diagnosis of traumatic AKI patients.
Labor Industry Allocation, Industrial Structure Optimization, and Economic Growth
Authors: Shi Liyuan
Published: September 2021

Industrial structure optimization is an important explanatory variable of economic growth. The allocation of production factors among industries affects the evolution of industrial structure and then acts on economic growth. Labor force is one of the most important factors of production. In recent years, there has been a significant wage difference between different industries in China’s labor market, and the employees have shifted from low-wage industries to high-wage industries. Using the sample data of 282 prefecture-level cities from 2008 to 2018, this paper tests whether this wage-guided labor industry allocation will affect economic growth and whether there is an intermediary effect of industrial structure optimization. The empirical results show that the allocation of labor force to high-wage industries not only directly hinders economic growth but also indirectly hinders economic growth through the intermediary effect of industrial structure optimization. Furthermore, this paper makes a comparative study in different regions. The research conclusion shows that promoting the optimization of industrial structure and economic growth depends on correcting the price distortion of labor market and guiding the cross-industry rational allocation of labor force.
Fractional Order Accumulation NGM (1, 1, k) Model with Optimized Background Value and Its Application
Authors: Zhang Jun , Qin Yanping , Zhang Xinyu , Wang Bing , Su Dongxue , Duo Huaqiong
Published: September 2021

Aiming at the problem of unstable prediction accuracy of the classic NGM (1, 1, k) model, the modeling principle and parameter estimation method of this model are deeply analyzed in this study. Taking the minimum mean absolute percentage error as the objective function, the model is improved from the two perspectives of the construction method of the background value and the fractional order accumulation generation. The fractional order accumulation NGM (1, 1, k) model based on the optimal background value (short for the FBNGM (1, 1, k) model) is proposed in the study. The particle swarm optimization algorithm is used to estimate the parameters of the proposed model. Taking two actual cases with economic significance as examples, empirical analysis of the proposed model is conducted. The simulation and prediction results show the practicality and efficiency of the FBNGM (1, 1, k) model proposed in this study, which further broadens the application scope of the grey prediction model.
Study of TCM Syndrome Identification Modes for Patients with Type 2 Diabetes Mellitus Based on Data Mining
Authors: Zhao Tieniu , Yang Xiaonan , Wan Ruixin , Yan Lihui , Yang Rongrong , Guan Yuanyuan , Wang Dongjun , Wang Huijun , Wang Hongwu
Published: September 2021

Objective. To establish the diagnosis model for syndromes of type 2 diabetes mellitus (T2-DM) and explore symptoms, the pulse and tongue signs, and laboratory indexes related to syndromes of T2-DM. Methods. A syndromatologic and laboratory investigation was conducted in 554 T2-DM patients with 58 symptoms, 14 tongue signs, 6 pulse signs, and 12 laboratory indexes. The clinical data on the syndrome were collected and analyzed by using logistic regression analysis, decision tree, and K-nearest neighbor to establish a diagnostic model for effectively distinguishing the typical syndromes in T2-DM patients. Results. The most typical syndromes revealed in T2-DM were stomach heat flourishing (SHF) syndrome (261 patients, accounting for 47.1%) and Qi-Yin deficiency (QYD) syndrome (293 patients, 52.9%). According to the clinical data of the patients with these two syndromes, variables including 6 symptoms and signs, 2 pulse signs, 1 tongue sign, and 2 laboratory indicators were introduced into the logistic regression model. All of them were statistically significant. Then, a diagnostic model constructed by QUEST and CHAID algorithms of the decision tree for identifying the two syndromes was proved to have an accurate diagnostic rate of 85.2%. It was found that the following sign and symptoms were effective to differentiate these two syndromes: odor in the mouth, polyphagia, vulnerability to starvation, burning sensation in the stomach, fatigue, limb weakness, slippery and replete pulse, weak pulse, pink tongue, oral glucose tolerance test, and hemoglobin A1C. A classification model constructed by the K-nearest neighbor method to identify the two syndromes showed an accurate diagnostic rate of 88.3%. Three major statistically significant predictors included in the model were slippery and replete pulse, polyphagia, and weak pulse (

P
<
0.05

). Conclusion. A model for distinguishing the two typical syndromes (SHF syndrome and QYD syndrome) in T2-DM patients was effectively established. This model could help to provide methodological support for the standardization of traditional Chinese medicine (TCM) syndrome differentiation methods.
Experimental Study on Formation Slip under Injection-Production Interregional Pressure Difference Based on the Abnormal Similarity Theory
Authors: Hu Chaoyang , Wang Fengjiao , Wang Tingting , Ai Chi , Wu Chenyu
Published: September 2021

In oilfield development, the pore pressure difference between adjacent areas leads to cracks and slipping in the weak structural surface layer, which triggers the shear failure of the casing. The formation slip involves a large range of formation, and its amount is not proportional to the size of the slipping rock mass, which conventional physical models cannot simulate. In this study, based on the abnormal similarity theory, we derived the similarity coefficients of mechanical parameters with different horizontal and vertical proportions. Furthermore, an experimental device for simulating the formation crack and slip under interregional formation pressure difference was developed. Through the experiments, we obtained slip conditions under different pressure differences between adjacent areas and different oil layers and fault surface depths. The study shows that the pore pressure difference between adjacent areas is the driving force of the formation slips. The slip zone is located in the middle of two abnormal pressure zones, and the distance between the adjacent areas can affect the slip range. The deep burial of the oil layer and shallow depth of the weak structural surface can trigger a more significant formation slip. The experimental method proposed in this paper provides an experimental device and method for understanding the formation of cracks and slips on weak structural surfaces. The experimental results provide a theoretical basis for the prevention of shear-type casing damage caused by formation slip.
The Safety and Effectiveness of Bevacizumab in the Treatment of Nonsquamous Non‐Small‐Cell Lung Cancer: A Meta‐Analysis of Randomized Controlled Trials
Authors: Zhou Yue , He Mei , Li Rui , Peng Yuan , Li Feng , Li Shengqian , Yang Ming
Published: September 2021

Objective. Bevacizumab was currently available for nonsquamous non‐small‐cell lung cancer (NSqNSCLC) patients and has been studied in several randomized controlled trials (RCTs) for treatment of these patients. This meta‐analysis summarizes the most up‐to‐date evidences regarding the effects and adverse reactions of bevacizumab in the treatment of NSqNSCLC patients. Methods. The authors searched for RCTs from electronic database including PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials. Experimental arm was defined as the bevacizumab‐containing group and the control arm as the bevacizumab‐free group. Data of objective response rate (ORR), disease control rate (DCR), progression‐free survival (PFS), overall survival (OS), and adverse reactions were synthetically extracted. A protocol for this meta‐analysis has been registered on PROSPERO (http://www.crd.york.ac.uk/prospero). Results. Ten RCTs that involved a total of 3134 patients were included. The experimental group was associated with significant superior ORR (RR 1.63, 95% CI 1.24 to 2.14, P < 0.001), OS (HR 0.90, 95% CI 0.82 to 0.99, P < 0.001), and prolonged PFS (HR 0.68, 95% CI 0.62 to 0.74, P < 0.001) compared to the control. No significant difference was observed regarding DCR (RR 1.13, 95% CI 0.99 to 1.30, P = 0.08). The experimental group showed higher rate of hypertension (RR 6.91, 95% CI 4.62 to 10.35, P < 0.00001) and hemorrhagic events (RR 3.07, 95% CI 1.78 to 5.30, P < 0.0001) than the control group. The experimental group showed lower rate of anemia (RR 0.72, 95% CI 0.55 to 0.96, P = 0.02) than the control group. No significant difference was observed regarding treatment‐related adverse event grade 3‐5 (TRAE3‐5) (RR 1.23, 95% CI 0.99 to 1.53, P = 0.06), thrombocytopenia (RR 1.11, 95% CI 0.92 to 1.33, P = 0.29), and neutropenia (RR 1.11, 95% CI 0.88 to 1.40, P = 0.36). Conclusion. This meta‐analysis showed that bevacizumab could increase ORR, OS, and prolonged PFS for treatment of NSqNSCLC patients. However, no significant improvement in DCR was observed and bevacizumab could increase the rate of hypertension and hemorrhagic events. Bevacizumab was an acceptable option for NSqNSCLC patients. This trial is registered with PROSPERO registration number: CRD42021226790.
Comprehensive Analysis of the Expression and Prognosis for E2Fs in Human Clear Cell Renal Cell Carcinoma
Authors: Zhang Chongli , Cui Yong , Wang Guannan , Zhao Wugan , Zhao Haiyu , Huang Xuejie , Zhang Min , Li Wencai
Published: September 2021

Background. E2F transcription factors is a family of transcription factors, and lots of studies have shown that they play a key role in the occurrence and development of many tumors. However, the association between expression, prognostic value, and immune infiltration in the tumor microenvironment of the eight E2Fs members in ccRCC is still unclear. Methods. We used online databases, such as ONCOMINE, UALCAN, Kaplan–Meier plotter, GEPIA, Metascape, TIMER, and cBioPortal, to analyze the effect of mRNA expression of E2Fs family members in ccRCC on the prognosis of patients and the relationship with immune infiltration. Results. Except for E2F5, other seven members of the family of E2Fs mRNA expression levels in ccRCC tissues were significantly higher than control tissues. And the high expression of E2Fs mRNA in ccRCC patients was related to cancer stage and tumor grade. Survival analysis results suggested that elevated mRNA expression levels of E2F1/2/3/4/7/8 were significantly related to the shorter overall survival (OS) in ccRCC patients (

P

 = 3.9E – 06), while high mRNA expression of E2F6 is not related to OS (

P

 = 0.061). Mutations of E2Fs were correlated with shorter OS of ccRCC patients (

P

 = 7.094E – 5). In addition, mRNA expression of E2F1/2/3/4/7/8 was positively correlated with infiltration of six types of immune cells, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. Conclusions. These results indicate that E2F1/2/3/4/7/8 may be used as a prognostic marker for the survival of ccRCC patients and laid the foundation for studying the immune infiltration role of E2Fs family members in tumors.
Preliminary Evaluation of a New Orthotic for Patellofemoral and Multicompartment Knee Osteoarthritis
Authors: Budarick Aleksandra R. , Bishop Emily L. , Clark Marcia L. , Cowper-Smith Christopher D.
Published: September 2021

Purpose. Traditional knee osteoarthritis (OA) braces are usually indicated for a minority of patients with knee OA, as they are only suitable for those with unicompartmental disease affecting the tibiofemoral joint. A new assistive brace design is intended for use in a wider range of knee OA patients with heterogeneous symptoms characteristic of patellofemoral, tibiofemoral, or multicompartmental knee OA. The purpose of this case series was to explore whether the use of this novel “tricompartment offloader” (TCO) brace was associated with clinically relevant improvements in pain and function. Materials and Methods. A retrospective analysis of individuals with knee OA (

n
=
40

) was conducted to assess pain, function, physical activity, and use of medication and other treatments before and after brace use. Validated outcome measures including the Visual Analog Scale (VAS) and Lower Extremity Functional Scale (LEFS) were used to assess pain and physical function (primary outcome measures). Exploratory measures were used to quantify physical activity levels and use of medication and other treatments (secondary outcome measures). Results. Average total pain (VAS) scores decreased by 36.6 mm and physical function (LEFS) scores increased by 16.0 points following the use of the TCO brace. Overall, 70% of the participants indicated increased weekly physical activity and 60% reported a decrease in their use of at least one other treatment. Conclusions. Results from this case series suggest that the TCO brace shows strong potential to fill a conservative treatment gap for patients with heterogeneous symptoms of knee OA that are characteristic of patellofemoral or multicompartment disease. Further investigation is warranted.
Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection
Authors: Awotunde Joseph Bamidele , Chakraborty Chinmay , Adeniyi Abidemi Emmanuel
Published: September 2021

The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning‐based intrusion detection paradigm for IIoT with hybrid rule‐based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule‐based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well‐known network datasets, NSL‐KDD and UNSW‐NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL‐KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW‐NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.
Distributed Observer Design for Linear Systems under Time‐Varying Communication Delay
Authors: Chen Kairui , Wang Junwei , Zhong Xiaojing , Lai Guanyu
Published: September 2021

The paper investigates the state estimation problem of general continuous‐time linear systems with the consideration of time‐varying communication delay. A solution is proposed in terms of the networked distributed observer, which consists of multiple local observers. Each local observer relies on only part of the system output and exchanges information with neighbors through undirected links modeled by a prespecified communication graph. A simple approach for computing observer parameters is presented by solving a parametric algebraic Riccati equation. Furthermore, by the Lyapunov–Krasovskii stability theorem, an upper bound of the delay could be calculated explicitly and together with the conditions of joint observability and connectivity of the communication graph; the resulting distributed observers work coordinately to achieve an asymptotic estimate of the full plant state. An illustrative example is provided to confirm the analytical results.
Research on Subpixel Algorithm of Fixed‐Point Tool Path Measurement
Authors: Zhang Xi , Guo Zixie , Liu Xiangwei , Zhang Longjia
Published: September 2021

Tool safety is an important part of machining and machine tool safety, and machine tool path image detection can effectively obtain the in‐machine condition of a tool. To obtain an accurate image edge and improve image processing accuracy, a novel subpixel edge detection method is proposed in this study. The precontour is segmented by binarization, the second derivative in the neighborhood of the demand point is calculated, and the obtained value is sampled according to the specified rules for curve fitting. The point whose curve ordinate is 0 is the subpixel position. The experiment proves that an improved subpixel edge can be obtained. Results show that the proposed method can extract a satisfactory subpixel contour, which is more accurate and reliable than the edge results obtained by several current pixel‐level operators, such as the Canny operator, and can be used in edge detection with high‐accuracy requirements, such as the contour detection of online tools.
Tribological Characteristics of Carbon Nanotubes‐Reinforced Plasma‐Sprayed Al2O3‐TiO2 Ceramic Coatings
Authors: Kalangi Chaitanya , Bolleddu Venkateshwarlu , Allasi Haiter Lenin
Published: September 2021

Thermal‐sprayed coatings are widely used in various oil and gas industries for wear and corrosion applications. However, increasing performance and requirements make conventional coatings inadequate for future needs. Furthermore, the heat conductivity of bulk materials cannot be minimized easily. Therefore, the use of low porous coating with nanocomposite doping is an effective way to produce coatings with reduced thermal conductivity. Plasma‐sprayed Al2O3‐TiO2 coatings are found in a wide range of applications recently in many industries because of their exceptional properties including low expenses and ease of availability. In this work, the wear‐resistant and low porous coatings of Al2O3 + 3 wt%TiO2 and respective carbon nanotube (CNT) doped coatings are prepared and characterized. The coatings are deposited on the AISI 1020 steel substrate using air plasma spraying. The impact of CNTs reinforcement on the percentage of pores and wear performance of coatings is investigated. Also, wear tracks morphology is investigated to determine the wear mechanism that is responsible for the wear of coatings. From the analysis, it is observed that the formation of cracks as well as micropores is decreased by the addition of carbon nanotubes. Moreover, uniform CNT distribution and good adhesion of coatings with the substrate are the major factors that improve the wear performance of the coated surface.
Transportation Path Optimization of Modern Logistics Distribution considering Hybrid Tabu Search Algorithm
Authors: Guo Jinjin , Zhang Huiying , Sun Guie
Published: September 2021

The optimization of transportation path for distribution vehicles, which is about the mixed tabu search method, can not only reduce the cost but also improve the optimization of the vehicle transportation path. This algorithm first studies the current situation of the logistics distribution vehicle transportation path and then uses the hybrid tabu search method to calculate the vehicle transportation path. According to the real case analysis, it can be proved that the hybrid tabu search calculation method can improve the efficiency, the proposed modern logistics distribution vehicle transportation path is also the best method, and the transportation cost is greatly reduced.
Relationship between Learning Styles and Academic Performance among Virtual Nursing Students: A Cross-Sectional Study
Authors: kohan Noushin , Janatolmakan Maryam , Rezaei Mansour , Khatony Alireza
Published: September 2021

Background. The lack of attention of nursing professors to students’ learning styles can cause academic failure. The results of studies on the relationship between students’ learning style and academic achievement are contradictory. Therefore, this study was designed to investigate the relationship between VARK learning styles and academic performance among virtual nursing students. Methods. In this cross-sectional study, 237 virtual nursing students were enrolled by the convenience sampling method. The VARK learning styles questionnaire was used for data collection. The basis for determining academic performance was the grade point average(s) (GPA) of the previous semester(s). Students were divided into two groups based on their GPA, including strong (GPA ≥15) and weak (GPA ≤14.99) groups. Results. In both strong and weak groups, most of the subjects were unimodal (with a frequency of 92.9% and 78.5%, respectively), and the rest were multimodal. The most common learning styles in strong and weak students were kinesthetic (57.1%) and auditory (37.2%), respectively. The results of chi-square test did not show statistically significant differences between learning styles and academic performance of strong and weak students. Conclusion. There was no significant relationship between the dominant learning styles and academic performance of strong and weak students. However, nursing professors need to adapt their teaching methods to the students’ learning styles. More studies are recommended to shed more light on this area of research.
Removal of Congo Red by Silver Carp (Hypophthalmichthys molitrix) Fish Bone Powder: Kinetics, Equilibrium, and Thermodynamic Study
Authors: Parvin Shahanaz , Hussain Md. Manjur , Akter Farhana , Biswas Biplob Kumar
Published: September 2021

Powdered form of bones of silver carp fish, an available species in Bangladesh, was investigated as a prominent bioadsorbent for the removal of Congo red from synthetic solution. Experiments were conducted in batch process, and a number of influencing parameters, such as solution pH, adsorbent dosage, contact time, and initial Congo red concentration, were thoroughly investigated for optimization. Kinetic and equilibrium data were well described by pseudo-second-order model and Langmuir isotherm, respectively. Suitability of pseudo-second-order model to best fit with the adsorption process was corroborated by squared sum of errors analysis. Mass transfer mechanism was confirmed by intraparticle pore diffusion and Bangham’s diffusion models. Maximum sorption capacity of fish bone powder was found to be 666.67 mg·g−1. The optimum condition (adsorbent dose: 5 g·L−1; pH: 2.0; operating time: 4 h) for maximum sorption was determined as well. The increasing negative value of Gibbs free energy (ΔG) with temperature rise indicated spontaneous nature and feasibility of adsorption. The positive values of ΔH and ΔS suggested that the adsorption reaction is endothermic and random (at the solid/liquid interface) in nature. The activation energy (29.84 kJ·mol−1) indicated that the sorption process was of physisorption type. A considerably high adsorption capacity pointed towards utilization of this apparently useless biomaterial as an effective adsorbent.