bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Reproducing Kernel Particle Method for Three-Dimensional Advection-Diffusion Problems
Authors: Peng Piaopiao , Fu Yida , Cheng Yumin
Published: September 2021

In this paper, a hybrid reproducing kernel particle method (HRKPM) for three-dimensional (3D) advection-diffusion problems is presented. The governing equation of the advection-diffusion problem includes the second derivative of the field function to space coordinates, the first derivative of the field function to space coordinates and time, so it is necessary to discretize the time domain after discretizing the space domain. By introducing the idea of dimension splitting, a 3D advection-diffusion problem can be transformed into a series of related two-dimensional (2D) ones in the dimension splitting direction. Then, the discrete equations of these 2D problems are established by using the RKPM, and these discrete equations are coupled by using the difference method. Finally, by using the difference method to discretize the time domain, the formula of the HRKPM for solving 3D advection-diffusion problem is obtained. Numerical results show that the HRKPM has higher computational efficiency than the RKPM when solving 3D advection-diffusion problems.
Combined Damage Influence Prediction of Curved Composite Structural Responses Using VCCT Technique and Experimental Verification
Authors: Kumar Vikash , Dewangan Hukum Chand , Sharma Nitin , Panda Subrata Kumar
Published: September 2021

The influences of crack and delamination on the natural frequency and strain energy release rate of the laminated doubly curved shell structure are computed via a commercial finite element tool (ABAQUS). The effect of individual damages (crack and delamination) is modeled using the virtual crack closure technique (VCCT), considering the curvature effect. Initially, the model validity is established by comparing the results with the available results in the open domain. Additionally, the model validity has been verified via in-house experimentation for frequency responses. Further, the natural frequency and strain energy release rate (SERR) have been calculated for the structure to examine the influences of the individual or combined effect of damages by varying the design-dependent input geometrical parameters. The inclusive characteristics of the current model in conjunction with geometrical configurations are summarized for subsequent references.
Partial factorizations of products of binomial coefficients
Authors: Du Lara , Lagarias Jeffrey C.
Published: September 2021

Let [Formula: see text] the product of the elements of the [Formula: see text]th row of Pascal’s triangle. This paper studies the partial factorizations of [Formula: see text] given by the product [Formula: see text] of all prime factors [Formula: see text] of [Formula: see text] having [Formula: see text], counted with multiplicity. It shows [Formula: see text] as [Formula: see text] for a limit function [Formula: see text] defined for [Formula: see text]. The main results are deduced from study of functions [Formula: see text] that encode statistics of the base [Formula: see text] radix expansions of the integer [Formula: see text] (and smaller integers), where the base [Formula: see text] ranges over primes [Formula: see text]. Asymptotics of [Formula: see text] and [Formula: see text] are derived using the prime number theorem with remainder term or conditionally on the Riemann hypothesis.
Economic Research on the Global Allocation of Scarce Water Resources Needs Better Data
Authors: Dantas Ianna Raissa Moreira , Delzeit Ruth , Klepper Gernot
Published: September 2021

Water sustainability is central to modern political and academic debates. Despite increasing efforts to promote regional and global integrated water management, climate change, population, and economic growth, and increasing consumption of water-intensive goods project higher water deficiency. Robust economic analyses rely on information about water supply and consumption across different production sectors, type of procurement source (public or private water supply), and water prices. Nevertheless, developing current and future economic water assessments and indicators is impeded by the absence of data. Despite the lack of official national statistics on water withdrawal and consumption, a small number of international and global databases have been constructed and attempt to combine available national water information into databases. Water databases do not commonly define and/or distinguish terms such as water use, water consumption, water supply, or water abstraction, and the associated aspects of water scarcity and sustainability. They comprise variable data quality, provided by numerous sources, and estimated values. This paper evaluates the current state of knowledge of national statistics, international and global water databases. We describe the data collection methods, identify basic concepts and definitions of water terms, followed by the criteria of consistent water databases. We inform about data availability across regions, and present the data content and definitions of national, international, and global water databases. The results show inconsistencies of data content and definitions, suggesting no evidence of data harmonization among databases. Therefore, our study cautions researchers to be careful when manipulating and comparing the available water data, especially when deriving policy recommendations or economic conclusions. In the long run, the headway of water research and political assessments depend on political enforcements to refine the meaningfulness of water data and support water collection, reporting, and monitoring. Alternatively, in the short- and medium-run, water data challenges can be addressed by joint research efforts for water data harmonization.
Novel Protein Therapeutics Created Using the Elastin-Like Polypeptide Platform
Authors: Bidwell Gene L.
Published: September 2021

Elastin-like polypeptides (ELPs) are bioengineered proteins that have a unique physical property, a thermally triggered inverse phase transition, that can be exploited for drug delivery. ELP-fusion proteins can be used as soluble biologics, thermally targeted drug carriers, self-assembling nanoparticles, and slow-release drug depots. Because of their unique physical characteristics and versatility for delivery of nearly any type of therapeutic, ELP-based drug delivery systems represent a promising platform for biologics development.
Regulation of solute carriers oct2 and OAT1/3 in the kidney: a phylogenetic, ontogenetic, and cell dynamic perspective
Authors: Pou Casellas Carla , Jansen Katja , Rookmaaker Maarten B. , Clevers Hans , Verhaar Marianne C. , Masereeuw Rosalinde
Published: September 2021

Over the course of more than 500 million years, the kidneys have undergone a remarkable evolution from primitive nephric tubes to intricate filtration-reabsorption systems that maintain homeostasis and remove metabolic end products from the body. The evolutionarily conserved solute carriers organic cation transporter 2 (OCT2) and organic anion transporters 1 and 3 (OAT1/3) coordinate the active secretion of a broad range of endogenous and exogenous substances, many of which accumulate in the blood of patients with kidney failure despite dialysis. Harnessing OCT2 and OAT1/3 through functional preservation or regeneration could alleviate the progression of kidney disease. Additionally, it would improve current in vitro test models that lose their expression in culture. With this review, we explore OCT2 and OAT1/3 regulation from different perspectives: phylogenetic, ontogenetic, and cell dynamic. Our aim is to identify possible molecular targets both to help prevent or compensate for the loss of transport activity in patients with kidney disease and to enable endogenous OCT2 and OAT1/3 induction in vitro in order to develop better models for drug development.
The physiological control of eating: signals, neurons, and networks
Authors: Watts Alan G. , Kanoski Scott E. , Sanchez-Watts Graciela , Langhans Wolfgang
Published: September 2021

During the past 30 yr, investigating the physiology of eating behaviors has generated a truly vast literature. This is fueled in part by a dramatic increase in obesity and its comorbidities that has coincided with an ever increasing sophistication of genetically based manipulations. These techniques have produced results with a remarkable degree of cell specificity, particularly at the cell signaling level, and have played a lead role in advancing the field. However, putting these findings into a brain-wide context that connects physiological signals and neurons to behavior and somatic physiology requires a thorough consideration of neuronal connections: a field that has also seen an extraordinary technological revolution. Our goal is to present a comprehensive and balanced assessment of how physiological signals associated with energy homeostasis interact at many brain levels to control eating behaviors. A major theme is that these signals engage sets of interacting neural networks throughout the brain that are defined by specific neural connections. We begin by discussing some fundamental concepts, including ones that still engender vigorous debate, that provide the necessary frameworks for understanding how the brain controls meal initiation and termination. These include key word definitions, ATP availability as the pivotal regulated variable in energy homeostasis, neuropeptide signaling, homeostatic and hedonic eating, and meal structure. Within this context, we discuss network models of how key regions in the endbrain (or telencephalon), hypothalamus, hindbrain, medulla, vagus nerve, and spinal cord work together with the gastrointestinal tract to enable the complex motor events that permit animals to eat in diverse situations.
Research on the Segmentation of Biomarker for Chronic Central Serous Chorioretinopathy Based on Multimodal Fundus Image
Authors: Xu Jianguo , Shen Jianxin , Jiang Qin , Wan Cheng , Yan Zhipeng , Yang Weihua
Published: September 2021

At present, laser surgery is one of the effective ways to treat the chronic central serous chorioretinopathy (CSCR), in which the location of the leakage area is of great importance. In order to alleviate the pressure on ophthalmologists to manually label the biomarkers as well as elevate the biomarker segmentation quality, a semiautomatic biomarker segmentation method is proposed in this paper, aiming to facilitate the accurate and rapid acquisition of biomarker location information. Firstly, the multimodal fundus images are introduced into the biomarker segmentation task, which can effectively weaken the interference of highlighted vessels in the angiography images to the location of biomarkers. Secondly, a semiautomatic localization technique is adopted to reduce the search range of biomarkers, thus enabling the improvement of segmentation efficiency. On the basis of the above, the low-rank and sparse decomposition (LRSD) theory is introduced to construct the baseline segmentation scheme for segmentation of the CSCR biomarkers. Moreover, a joint segmentation framework consisting of the above method and region growing (RG) method is further designed to improve the performance of the baseline scheme. On the one hand, the LRSD is applied to offer the initial location information of biomarkers for the RG method, so as to ensure that the RG method can capture effective biomarkers. On the other hand, the biomarkers obtained by RG are fused with those gained by LRSD to make up for the defect of undersegmentation of the baseline scheme. Finally, the quantitative and qualitative ablation experiments have been carried out to demonstrate that the joint segmentation framework performs well than the baseline scheme in most cases, especially in the sensitivity and F1-score indicators, which not only confirms the effectiveness of the framework in the CSCR biomarker segmentation scene but also implies its potential application value in CSCR laser surgery.
A Mathematical Model of COVID-19 with Vaccination and Treatment
Authors: Diagne M. L. , Rwezaura H. , Tchoumi S. Y. , Tchuenche J. M.
Published: September 2021

We formulate and theoretically analyze a mathematical model of COVID-19 transmission mechanism incorporating vital dynamics of the disease and two key therapeutic measures—vaccination of susceptible individuals and recovery/treatment of infected individuals. Both the disease-free and endemic equilibrium are globally asymptotically stable when the effective reproduction numberR0vis, respectively, less or greater than unity. The derived critical vaccination threshold is dependent on the vaccine efficacy for disease eradication wheneverR0v>1, even if vaccine coverage is high. Pontryagin’s maximum principle is applied to establish the existence of the optimal control problem and to derive the necessary conditions to optimally mitigate the spread of the disease. The model is fitted with cumulative daily Senegal data, with a basic reproduction numberR0=1.31at the onset of the epidemic. Simulation results suggest that despite the effectiveness of COVID-19 vaccination and treatment to mitigate the spread of COVID-19, whenR0v>1, additional efforts such as nonpharmaceutical public health interventions should continue to be implemented. Using partial rank correlation coefficients and Latin hypercube sampling, sensitivity analysis is carried out to determine the relative importance of model parameters to disease transmission. Results shown graphically could help to inform the process of prioritizing public health intervention measures to be implemented and which model parameter to focus on in order to mitigate the spread of the disease. The effective contact rateb, the vaccine efficacyε, the vaccination ratev, the fraction of exposed individuals who develop symptoms, and, respectively, the exit rates from the exposed and the asymptomatic classesσandϕare the most impactful parameters.
Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network
Authors: Hou Kai
Published: September 2021

The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation. The feedforward neural network with convolution operation and deep structure is an important method of deep learning. In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short‐Term Memory), and CNN. This study combines the principal component analysis method to predict and analyze the test results of students’ physical fitness standards. The innovation lies in the introduction of the function of the recurrent convolutional network and the use of principal component analysis to conduct qualitative research on seven evaluation indicators that reflect the three aspects of students’ physical health. The results of the study clearly show that there is a strong correlation between some indicators, such as standing long jump and sitting bends which may have a strong correlation. The first principal component eigenvalue has the highest contribution rate, which mainly reflects the five indicators of standing long jump, sitting forward bend, pull‐up, 50 m sprint, and 1000 m long‐distance running. This shows that the physical fitness indicators have a great impact on the physical health of students, which also reflects the current status of students’ physical fitness problems. The results of principal component analysis are scientific and reasonable.
Mango Grading System Based on Optimized Convolutional Neural Network
Authors: Zheng Bin , Huang Tao
Published: September 2021

In order to achieve the accuracy of mango grading, a mango grading system was designed by using the deep learning method. The system mainly includes CCD camera image acquisition, image preprocessing, model training, and model evaluation. Aiming at the traditional deep learning, neural network training needs a large number of sample data sets; a convolutional neural network is proposed to realize the efficient grading of mangoes through the continuous adjustment and optimization of super-parameters and batch size. The ultra-lightweight SqueezeNet related algorithm is introduced. Compared with AlexNet and other related algorithms with the same accuracy level, it has the advantages of small model scale and fast operation speed. The experimental results show that the convolutional neural network model after super-parameters optimization and adjustment has excellent effect on deep learning image processing of small sample data set. Two hundred thirty-four Jinhuang mangoes of Panzhihua were picked in the natural environment and tested. The analysis results can meet the requirements of the agricultural industry standard of the People’s Republic of China—mango and mango grade specification. At the same time, the average accuracy rate was 97.37%, the average error rate was 2.63%, and the average loss value of the model was 0.44. The processing time of an original image with a resolution of 500

×

374 was only 2.57 milliseconds. This method has important theoretical and application value and can provide a powerful means for mango automatic grading.
Dynamic Facial Expression Recognition Using Sparse Reserved Projection Algorithm for Low Illumination Images
Authors: Li Hui
Published: September 2021

In this paper, a novel approach for facial expression recognition based on sparse retained projection is proposed. The locality preserving projection (LPP) algorithm is used to reduce the dimension of face image data that ensures the local near-neighbor relationship of face images. The sparse representation method is used to solve the partial occlusion of human face and the problem of light imbalance. Through sparse reconstruction, the sparse reconstruction information of expression is retained as well as the local neighborhood information of expression, which can extract more effective and judgmental internal features from the original expression data, and the obtained projection is relatively stable. The recognition results based on CK + expression database show that this method can effectively improve the facial expression recognition rate.
Application of Improved Boosting Algorithm for Art Image Classification
Authors: Wu Yue
Published: September 2021

In the field of computer science, data mining is a hot topic. It is a mathematical method for identifying patterns in enormous amounts of data. Image mining is an important data mining technique involving a variety of fields. In image mining, art image organization is an interesting research field worthy of attention. The classification of art images into several predetermined sets is referred to as art image categorization. Image preprocessing, feature extraction, object identification, object categorization, object segmentation, object classification, and a variety of other approaches are all part of it. The purpose of this paper is to suggest an improved boosting algorithm that employs a specific method of traditional and simple, yet weak classifiers to create a complex, accurate, and strong classifier image as well as a realistic image. This paper investigated the characteristics of cartoon images, realistic images, painting images, and photo images, created color variance histogram features, and used them for classification. To execute classification experiments, this paper uses an image database of 10471 images, which are randomly distributed into two portions that are used as training data and test data, respectively. The training dataset contains 6971 images, while the test dataset contains 3478 images. The investigational results show that the planned algorithm has a classification accuracy of approximately 97%. The method proposed in this paper can be used as the basis of automatic large-scale image classification and has strong practicability.
A Review on Olefin Metathesis Reactions as a Green Method for the Synthesis of Organic Compounds
Authors: Abera Tsedalu Atitegeb
Published: September 2021

Olefin metathesis is a metal-mediated C-C bond exchange by which the two fragments within the olefin precursor are redistributed as a result of breaking the double bond to obtain a new product. Currently, most of the synthetic organic compounds, polymers, drugs, plastics, and other synthetic materials are synthesized via the application of olefin metathesis reactions. In this review, different types of olefin metathesis reactions with their plausible mechanisms and their application in synthetic organic chemistry have been discussed.
The Knockdown of Nrf2 Suppressed Tumor Growth and Increased the Sensitivity to Lenvatinib in Anaplastic Thyroid Cancer
Authors: Gong Zhongqin , Xue Lingbin , Wei Minghui , Liu Zhimin , Vlantis Alexander C. , van Hasselt C. Andrew , Chan Jason Y. K. , Li Dongcai , Zeng Xianhai , Tong Michael C. F. , Chen George G.
Published: September 2021

Papillary thyroid cancer can dedifferentiate into a much more aggressive form of thyroid cancer, namely into anaplastic thyroid cancer. Nrf2 is commonly activated in papillary thyroid cancer, whereas its role in anaplastic thyroid cancer has not been fully explored. In this study, we used two cell lines and an animal model to examine the function of Nrf2 in anaplastic thyroid cancer. The role of Nrf2 in anaplastic thyroid cancer was investigated by a series of functional studies in two anaplastic thyroid cancer cell lines, FRO and KAT‐18, and further confirmed with an in vivo study. The impact of Nrf2 on the sensitivity of anaplastic thyroid cancer cells to lenvatinib was also investigated to evaluate its potential clinical implication. We found that the expression of Nrf2 was significantly higher in anaplastic thyroid cancer cell line cells than in papillary thyroid cancer cells or normal control cells. Knockdown of Nrf2 in anaplastic thyroid cancer cells inhibited their viability and clonogenicity, reduced their migration and invasion ability in vitro, and suppressed their tumorigenicity in vivo. Mechanistically, knockdown of Nrf2 decreased the expression of Notch1. Lastly, knockdown of Nrf2 increased the sensitivity of anaplastic thyroid cancer cells to lenvatinib. As knockdown of Nrf2 reduced the metastatic and invasive ability of anaplastic thyroid cancer cells by inhibiting the Notch 1 signaling pathway and increased the cancer cell sensitivity to lenvatinib, Nrf2 could be a promising therapeutic target for patients with anaplastic thyroid cancer.
Morning Serum Cortisol as a Predictor for the HPA Axis Recovery in Cushing’s Disease
Authors: Cui Q. , Liu D. , Xiang B. , Sun Q. , Fan L. , He M. , Wang Y. , Zhu X. , Ye H.
Published: September 2021

Background. The suppressed hypothalamic-pituitary-adrenal (HPA) axis after successful surgery for Cushing’s disease (CD) will recover in almost all patients. We aimed to identify the predictive factors for HPA axis recovery in CD patients with postoperative remission. Design and Methods. This observational retrospective cross-sectional study enrolled 69 CD patients with postoperative remission in Huashan Hospital from 2015 to 2019. All subjects had a detailed clinical evaluation. The low-dose ACTH stimulation test (LDT) was conducted as the gold standard for assessing the HPA axis function. Results. Peak cortisol in LDT was found only to be positively correlative with morning serum cortisol (MSC) (

ρ
=
0.451

,

p
<
0.001

). The MSC was higher (

p
<
0.001

), and the median postoperative course was significantly longer (

p
=
0.025

) in the patients with the recovered HPA axis function compared with unrecovered patients. The AUC value of MSC for predicting the recovery of the HPA axis was 0.701, and the optimal cutoff was 6.25 μg/dl (sensitivity 85.19% and specificity 47.62%). Other useful cutoff values were 10.74 μg/dl (specificity 100%) and 4.18 μg/dl (sensitivity 100%). Besides, combined with the postoperative course, the AUC values were higher than MSC alone (0.935 vs. 0.701,

p
<
0.001

). Conclusions. MSC is a viable first-step diagnostic predictor for HPA axis recovery in CD patients with postoperative remission. For the patients with cortisol levels between 4.18 and 10.74 μg/dl, a confirmatory test should be conducted. When the MSC level was 10.74 μg/dl or greater, the replacement therapy could be discontinued.
Asymmetric Truncated Hankel Operators: Rank One, Matrix Representation
Authors: Rahmani Firdaws , Lu Yufeng , Li Ran
Published: September 2021

Asymmetric truncated Hankel operators are the natural generalization of truncated Hankel operators. In this paper, we determine all rank one operators of this class. We explore these operators on finite-dimensional model spaces, in particular, their matrix representation. We also give their matrix representation and the one for asymmetric truncated Toeplitz operators in the case of model spaces associated to interpolating Blaschke products.
Redox‐Dependent Effects in the Physiopathological Role of Bile Acids
Authors: Orozco-Aguilar Josué , Simon Felipe , Cabello-Verrugio Claudio
Published: September 2021

Bile acids (BA) are recognized by their role in nutrient absorption. However, there is growing evidence that BA also have endocrine and metabolic functions. Besides, the steroidal‐derived structure gives BA a toxic potential over the biological membrane. Thus, cholestatic disorders, characterized by elevated BA on the liver and serum, are a significant cause of liver transplant and extrahepatic complications, such as skeletal muscle, central nervous system (CNS), heart, and placenta. Further, the BA have an essential role in cellular damage, mediating processes such as membrane disruption, mitochondrial dysfunction, and the generation of reactive oxygen species (ROS) and oxidative stress. The purpose of this review is to describe the BA and their role on hepatic and extrahepatic complications in cholestatic diseases, focusing on the association between BA and the generation of oxidative stress that mediates tissue damage.
Improving the Performance of Deep Learning Model‐Based Classification by the Analysis of Local Probability
Authors: Jin Guanghao , Hu Yixin , Jiao Yuming , Wen Junfang , Song Qingzeng
Published: September 2021

Generally, the performance of deep learning‐based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L‐PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state‐of‐the‐art methods.
Emergence of Hidden Attractors through the Rupture of Heteroclinic‐Like Orbits of Switched Systems with Self‐Excited Attractors
Authors: Escalante-González R. J. , Campos Eric
Published: September 2021

This work is dedicated to the study of an approach that allows the generation of hidden attractors based on a class of piecewise‐linear (PWL) systems. The systems produced with the approach present the coexistence of self‐excited attractors and hidden attractors such that hidden attractors surround the self‐excited attractors. The first part of the approach consists of the generation of self‐excited attractors based on pairs of equilibria with heteroclinic orbits. Then, additional equilibria are added to the system to obtain a bistable system with a second self‐excited attractor with the same characteristics. It is conjectured that a necessary condition for the existence of the hidden attractor in this class of systems is the rupture of the trajectories that resemble heteroclinic orbits that join the two regions of space that surround the pairs of equilibria; these regions resemble equilibria when seen on a larger scale. With the appearance of a hidden attractor, the system presents a multistable behavior with hidden and self‐excited attractors.
Double Regression‐Based Sparse Unmixing for Hyperspectral Images
Authors: Zhang Shuaiyang , Hua Wenshen , Li Gang , Liu Jie , Huang Fuyu , Wang Qianghui
Published: September 2021

Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity. To solve this problem, we propose a novel double regression‐based sparse unmixing model (DRSUM), which can obtain better unmixing results with lower computational complexity. DRSUM decomposes the complex objective function into two simple formulas and completes the unmixing process through two sparse regressions. The unmixing result of the first sparse regression is added as a constraint to the second. DRSUM is an open model, and we can add different constraints to improve the unmixing accuracy. In addition, we can perform appropriate preprocessing to further improve the unmixing results. Under this model, a specific algorithm called double regression‐based sparse unmixing via K‐means (DRSUMK−means) is proposed. The improved K‐means clustering algorithm is first used for preprocessing, and then we impose single sparsity and joint sparsity (using l2,0 norm to control the sparsity) constraints on the first and second sparse unmixing, respectively. To meet the sparsity requirement, we introduce the row‐hard‐threshold function to solve the l2,0 norm directly. Then, DRSUMK−means can be efficiently solved under alternating direction method of multipliers (ADMM) framework. Simulated and real data experiments have proven the effectiveness of DRSUMK−means.
Level Detection Equipment for Measuring the Influence of Different Leveling Accuracies on Linear Error
Authors: Chen Shao-Hsien , Ji Chi-Li
Published: September 2021

This study developed a level detection equipment which is used in computer numerical control (CNC) machine tool to determine the impact of leveling accuracy on rectilinear motion accuracy. When the CNC precision machine tool has accuracy deterioration under external load or internal stress, mainly caused error is leveling error, this research and development equipment can immediate to analyze and measurement. The allowable error of leveling accuracy can be obtained after experimental validation. The kinematic error relatively increases with leveling error. When the leveling accuracy is within the allowable error, the kinematic error relatively decreases. The main kinematic error items measured in this study include EXX, EBY, EAX, and EYY. The level detection equipment is developed in this study, and the fuzzy regression analysis is used for modeling. The model that has high accuracy in the test of the X‐axis is R2 = 0.9764 and P = 0.0506, and Y‐axis is R2 = 0.9756 and P = 0.0524. In terms of filtering, Kalman filtering is used for signal processing, the measured values and X‐axis and Y‐axis after filtering are improved by 94.1% and 86.2%, respectively, the repeatability of this system is about A grade capability of precision (Cp), resolution is ±0.0001°, and the stability is at least B grade capability of accuracy (Ca). This equipment has the advantages of low cost, high precision, and 2‐axis measurement. This machine tool which has the straightness increases with X and Y axes’ leveling accuracy errors, when the X/Y leveling accuracy is within ±0.01 mm/m, and there is the best straightness and conforms to the ISO230 standard (Lee et al., 2020).
Aerobics Image Classification Algorithm Based on Modal Symmetry Algorithm
Authors: Chen Xiaohua , Sheng Qiang , Singh Bhupesh Kumar
Published: September 2021

There exist large numbers of methods/algorithms which can be used for the classification of aerobic images. While the current method is used to classify the aerobics image, it cannot effectively remove the noise in the aerobics image. The classification time is long, and there are problems of poor denoising effect and low classification efficiency. Therefore, the aerobics image classification algorithm based on the modal symmetry algorithm is proposed. The method of nonlocal mean filtering based on structural features is used to denoise the aerobics image, and the pyramid structure of the image is introduced to decompose the aerobics image. According to the denoising and decomposition results, the enhancement of aerobics image is realized by the logarithmic image processing (LIP) model and gradient sharpening method. Finally, the aerobics image after the enhancement is classified by a modal symmetry algorithm. Experimental results show that the proposed method has a good denoising effect and high classification efficiency, which shows that the algorithm has significant effectiveness and high application performance.
Investigation on the Lightning Location and Warning System Using Artificial Intelligence
Authors: Shi Tianru , Hu Danhui , Ren Xiang , Huang Zeqi , Zhang Yaodong , Yang Jianlan
Published: September 2021

An in‐depth study on a lighting location system is conducted in this paper. Firstly, the history and application of this system are summarized. The overall structure is detailed, including the detection principle of the lightning location, the orientation method, the detection circuit, the method of discriminating cloud flash and ground lightning signal, the error analysis, the guideline for station deployment, the preprocessing of the central station, and the function and structure of data server and user interface. The development of a lightning monitoring system in China is presented, and the construction of a new generation of a lightning location system in the Hubei Province power grid is introduced. Through the collection of measured data, the performance of the lightning location system in the lightning accident inspection rate, lightning location, and lightning situation statistics are analyzed. Artificial intelligence algorithms are applied in the lightning warning system. The new system has a high predicting accuracy.
Phytochemistry and Pharmacology of Thymus broussonetii Boiss
Authors: Naceiri Mrabti Hanae , Doudach Latifa , El Menyiy Naoual , Bourhia Mohammed , Mohammad Salamatullah Ahmad , Reda Kachmar Mohamed , Belmehdi Omar , El Moudden Hamza , Naceiri Mrabti Nidal , Harhar Hicham , El Omari Nasreddine , Bouyahya Abdelhakim
Published: September 2021

Thymus broussonetii Boiss (T. broussonetii) is a rare medicinal and aromatic plant. It is widely used in traditional medicine to treat several diseases, including diarrhea, fever, cough, irritation, skin diseases, rheumatism, respiratory ailments, influenza, and digestion problems. In this review, we have critically summarized previous data on T. broussonetii about its phytochemistry, botanical and geographical distribution, toxicological investigation, and pharmacological properties. Using scientific research databases such as Wiley Online, SciFinder, ScienceDirect, PubMed, SpringerLink, Web of Science, Scopus Wiley Online, and Google Scholar, the data on T. broussonetii were collected and discussed. The presented data regrouped bioactive compounds and biological activities of T. broussonetii. The findings of this work showed that essential oils and extracts of T. broussonetii exhibited numerous pharmacological activities (in vitro and in vivo), particularly antibacterial, antifungal, antioxidant, anticancer, anti-inflammatory, insecticidal, antipyretic, antinociceptive, and immunological and behavioral effects. While toxicological studies of T. broussonetii essential oils and extracts are lacking, modern scientific tools revealed the presence of different classes of secondary metabolites such as terpenoids, alkaloids, flavonoids, tannins, coumarins, quinones, carotenoids, and steroids. T. broussonetii essential oils, especially from the aerial parts, exhibited potent antibacterial, antifungal, and antioxidant effects. An in-depth toxicological investigation is needed to validate the efficacy and safety of T. broussonetii extracts and essential oils and their secondary metabolites. However, further pharmacokinetic and pharmacodynamic studies should be performed to validate its bioavailability.
Numerical Scheme for Finding Roots of Interval-Valued Fuzzy Nonlinear Equation with Application in Optimization
Authors: Elmoasry Ahmed , Shams Mudassir , Yaqoob Naveed , Kausar Nasreen , Gaba Yaé Ulrich , Rafiq Naila
Published: September 2021

In this research article, we propose efficient numerical iterative methods for estimating roots of interval-valued trapezoidal fuzzy nonlinear equations. Convergence analysis proves that the order of convergence of numerical schemes is 3. Some real-life applications are considered from optimization as numerical test problems which contain interval-valued trapezoidal fuzzy quantities in parametric form. Numerical illustrations are given to show the dominance efficiency of the newly constructed iterative schemes as compared to existing methods in literature.
Robust Regression-Ratio-Type Estimators of the Mean Utilizing Two Auxiliary Variables: A Simulation Study
Authors: Zaman Tolga , Dünder Emre , Audu Ahmed , Alilah David Anekeya , Shahzad Usman , Hanif Muhammad
Published: September 2021

Many authors defined the modified version of the mean estimator by using two auxiliary variables. These proposed estimators highly depend on the calculated regression coefficients. In the presence of outliers, these estimators do not give satisfactory results. In this study, we improve the suggested estimators using several robust regression techniques while obtaining the regression coefficients. We compared the efficiencies between the suggested estimators and the estimators presented in the literature. We used two numerical examples and a simulation study to support these theoretical results. Empirical results show that the modified ratio estimator performs well in the presence of outliers when adopting robust regression techniques.
Characterization of Integrons and Quinolone Resistance in Clinical Escherichia coli Isolates in Mansoura City, Egypt
Authors: Abdel-Rhman Shaymaa H. , Elbargisy Rehab M. , Rizk Dina E.
Published: September 2021

Escherichia coli is a common pathogen in both humans and animals. Quinolones are used to treat infections caused by Gram-negative bacteria, but resistance genes emerged. Only scarce studies investigated the association between plasmid-mediated quinolone resistance (PMQR) genes and integrons in clinical isolates of E. coli. The current study investigated the prevalence of quinolone resistance and integrons among 134 clinical E. coli isolates. Eighty (59.70%) isolates were quinolone-resistant, and 60/134 (44.77%) isolates were integron positive with the predominance of class I integrons (98.33%). There was a significant association between quinolone resistance and the presence of integrons (

P
<
0.0001

). Isolates from Urology and Nephrology Center and Gastroenterology Hospital were significantly quinolone-resistant and integron positive (

P
≤
0.0005

). Detection of PMQR genes on plasmids of integron-positive isolates showed that the active efflux pump genes oqxAB and qepA had the highest prevalence (72.22%), followed by the aminoglycoside acetyltransferase gene (aac(6′)-Ib-cr, 66.67%) and the quinolone resistance genes (qnr, 61.11%). Amplification and sequencing of integrons’ variable regions illustrated that no quinolone resistance genes were detected, and the most predominant gene cassettes were for trimethoprim and aminoglycoside resistance including dfrA17, dfrB4, and dfrA17-aadA5. In conclusion, this study reported the high prevalence of PMQR genes and integrons among clinical E. coli isolates. Although PMQR genes are not cassette-born, they were associated with integrons’ presence, which contributes to the widespread of quinolone resistance in Egypt.
Research on PSD Replication for Multiaxial Hydraulic Vibration Test System Based on FXLMS Algorithms
Authors: Cheng Yunbin , Li Bin , Wang Jun , Li Heliang
Published: September 2021

The multiaxial electrohydraulic vibration test system can not only simulate the multiexcitation on multidimensional vibration environment but also meet the vibration test requirements of high thrust, large displacement, low frequency, etc. In order to eliminate the effects of nonlinear factor and system noise and achieve a more accurate control result, a control algorithm based on the frequency‐domain filtered‐x least mean square adaptive algorithm (FXLMS) is proposed to achieve the power spectral density (PSD) replication. The main idea is to use the frequency‐domain FXLMS algorithm to adjust the controller adaptively corresponding to the transfer function uncertainty and changes of the plant, which are typically caused by time‐varying parameters in electrohydraulic actuators and system noise. The details and implantation steps of the proposed algorithm are analysed for the single‐input single‐output electrohydraulic vibration test system. The proposed algorithm and control strategy are then extended to the multiaxial electrohydraulic vibration test system. Eventually, some experimental targets for performing the PSD replication test on a two‐exciter system are carried out, in which the results show that the proposed algorithm is valid and meets the test standards..
Survival Status and Predictors of Mortality among Multidrug-Resistant Tuberculosis Patients in Saint Peter’s Specialized Hospital, Addis Ababa, Ethiopia
Authors: Muluneh Mikyas Arega , Zeru Abayneh Birlie , Derseh Behailu Tariku , Molla Kebede Abebaw
Published: September 2021

Background. Multidrug-Resistant Tuberculosis (MDR-TB) is tuberculosis that is resistant to at least both rifampicin and isoniazid. The World Health Organization as reported in 2019 revealed that Ethiopia is among the 20 countries with the highest estimated numbers of incident MDR-TB cases. However, supporting evidence is limited in the study area after the Ethiopian national strategic plan for tuberculosis prevention and control is started. Objective. To determine survival status and predictors of mortality among multidrug-resistant tuberculosis patients treated in Saint Peter’s Specialized Hospital at Addis Ababa, Ethiopia, 2020. Methods. An institutional retrospective cohort study was conducted using all MDR-TB patients who were enrolled in Saint. Peter’s Specialized Hospital from January 01, 2015, to December 31, 2017. A pretested data extraction form that had 5 items for sociodemographic and 15 items for the measurement of clinical characteristics of 484 MDR-TB patients was used. STATA software version 14.2 was used for data cleaning and analysis. A variable that fitted in the bivariable Cox proportional hazard model at

p

value <0.25 was used in the final multivariable Cox proportional hazard model, and independent predictors of time to event were determined at a

p

value of 0.05. Result. A total of 484 patients were followed up for 5,078 person-months. Among the total patients, nearly half, 238 (48.8%), were males. The median age of patients was 30 years (interquartile range (IQR), 24–39), and 56 (11.6%) were aged between 1 and 19 years. During the follow-up period, 315 (65.1%) patients were cured, 125 (25.8%) completed treatment, 24 (5%) died, and 20 (4.1%) were lost to follow-up. The overall cumulative probability survival of the patients at the end of treatment was 94.85% (95% confidence interval (CI): 92.38%–96.53%). The independent predictors of time to death were being anemic (AHR = 3.65; 95% CI: 1.36, 9.79), having clinical complication (AHR = 3; 95% CI: 1.2, 7.5), and being HIV infected (AHR = 5.8; 95% CI: 2.2, 15.7). Conclusions. MDR-TB patients’ survival rate was high in St Peter’s Specialized Hospital. MDR-TB patients with anemia, HIV coinfection, and clinical complications had higher risk of mortality. So, prevention and controlling of anemia, HIV/AIDS, and clinical complications will reduce the mortality of MDR-TB patients.
Comparing the Coagulation Performance of Rice Husk, Cypress Leaves, and Eucalyptus Leaves Powders with That of Alum in Improving the Turbidity and pH of Some Local Water Sources in Bamenda, Cameroon
Authors: Tsamo Cornelius , Zama Eric Fru , Yerima Ngu Elton , Mandela Fuh Ajingne Nelson
Published: September 2021

In this study, the efficiencies of the use of rice husk, cypress, and Eucalyptus leaves biocoagulants as alternative to alum as chemical coagulant in reducing turbidity and stabilizing the pH of slaughterhouse wastewater and three other local drinking water sources were investigated. Two systems were used: one involving individual coagulants and the second involving mix alum and biocoagulant with fixed alum dose of 0.05 g and varying amounts of each biocoagulant type (0.05 to 5 g). Turbidity reduction, coagulation activity, and pH variation were used to characterize each system. Results show reduction in turbidity is higher in mixed coagulants than with individual coagulants. At 5 g each of alum, rice husk, cypress, and Eucalyptus leaves, the corresponding turbidity values, 8.3, 13.6, 14.5, and 20.3 NTU, were obtained compared to 7.3 with 0.05 alum and 5 g rice husk mixture, 8.7 NTU with 0.05 g alum and 5 g cypress leaves mixture, and 16.1 NTU with 0.05 g alum and 5 g Eucalyptus leaves mixture, for 37.3 NTU initial turbidity. The used biocoagulants individually show insignificant effect on the pH of coagulation-treated water. Alum has an insignificant effect up to 2 g. 0.05 g of each biocoagulant stabilizes the pH between 6.57 and 7.34 against 4.14 for alum. 0.05 g alum/0.05 g biocoagulant stabilized the pH of water between 6.32 and 7.41. The coagulation activities for individual systems follow the order alum > rice husk > cypress > Eucalyptus, and for mixed systems, alum/rice husk > alum/cypress > alum/Eucalyptus. Water with turbidity and pH values within the World Health Organization’s guideline value of < 5 NTU and 6.5–8.5, respectively, were obtained using studied low cost and locally available biocoagulants.
Short Text Paraphrase Identification Model Based on RDN‐MESIM
Authors: Li Jing , Zhang Dezheng , Wulamu Aziguli
Published: September 2021

In the rapid development of various technologies at the present stage, representative artificial intelligence technology has developed more prominently. Therefore, it has been widely applied in various social service areas. The application of artificial intelligence technology in tax consultation can optimize the application scenarios and update the application mode, thus further improving the efficiency and quality of tax data inquiry. In this paper, we propose a novel model, named RDN‐MESIM, for paraphrase identification tasks in the tax consulting area. The main contribution of this work is designing the RNN‐Dense network and modifying the original ESIM to adapt to the RDN structure. The results demonstrate that RDN‐MESIM obtained a better performance as compared to other existing relevant models and archived the highest accuracy, of up to 97.63%.