bluuebunny/crossref_metadata_2025 huggingface dataset:
On the energy density of linearly polarized, plane gravitational wave
Authors: Yang I-Ching
Published: March 2022

In this paper, the energy density of plane gravitational wave is studied by using Einstein and Møller’s prescription of energy–momentum pseudotensors. The linearly polarized plane gravitational wave solution of Einstein field equation, which has been defined by Bondi et al., is represented by four kinds of different coordinates. The energy distributions of gravitational wave solution in Einstein and Møller’s prescription are obtained. Particularly, the energy component is zero in null coordinates.
Deep Recurrent Encoder Network and Spark Model for Angiographic Disease Risk Classification
Authors: Vinoth R. , Ananth J. P.
Published: March 2022

Analysis and extraction of effective results from the medical big data is very complex due to the existence of large volume of data and it is also very difficult to classify the risk of angiographic diseases. The challenges faced by the conventional methods are complexity in classifying the data and performance degradation for a large-sized dataset. Hence, a hybrid approach named Deep Recurrent Encoder Network and Spark Model for Angiographic Disease Risk Classification (DRE-NET) is developed in this research to achieve accurate results for classifying the risk of angiographic diseases using Spark architecture. The developed Adaptive RCOA integrates Rider Optimization Algorithm (ROA) and Chicken Swarm Optimization (CSO) with the adaptive concept. The disease risk classification process is obtained by the Recurrent Neural Network (RNN) and Deep Stacked Autoencoder (DSAE) in such a way that the weights are optimally trained by the developed Adaptive RCOA. The optimal solution is obtained by evaluating the fitness function such that the fitness with minimal error value is considered the best solution. Moreover, the developed Adaptive RCOA-based RNN+DSAE attained better result with the metrics such as specificity, accuracy, and sensitivity with the values of 100%, 97.69%, and 99.19%, respectively, using the KDD Cup dataset.
An Expanded MLP Neural Network for Fast Angiodysplasia Lesions Segmentation in Capsule Endoscopy Images
Authors: Amiri Zahra , Hassanpour Hamid , Beghdadi Azeddine
Published: March 2022

In this paper, an expanded multilayer perceptron (EMLP) neural network is proposed to automatically segment angiodysplasia regions in wireless capsule endoscopy (WCE) images The main idea is to minimize the distance between the input image and the corresponding binary ground truth, i.e., the mask image. After the training phase, when a test image is given to the network, the lesion pixels will be close to “one” and the other pixels will be close to “zero” and finally, the lesion area can be segmented using thresholding. Since angiodysplasia lesions appear in images with different spectrums of red color, the classical MLP neural network cannot be trained with a wide range of red color, hence leads to undesirable network accuracy. To solve this problem, we proposed an EMLP neural network for image segmentation. In the EMLP neural network, neurons are divided into several groups, each of which is for learning a spectrum of the lesion. The EMLP is able to learn a wider range of red colors. The proposed method is able to segment WCE images containing angiodysplasia faster than the existing methods. Our investigation shows that our method also outperforms existing methods in terms of segmentation scores.
A Feature Pyramid Based Multi-stage Framework for Object Detection in Low-altitude UAV Images
Authors: Mittal Payal , Sharma Akashdeep , Singh Raman
Published: March 2022

This paper proposes a high-performance framework for accurate multi-stage object detection in low-altitude based UAV images. The proposed system employs a cascade style architecture with increasing thresholds for achieving accurate detection. The framework makes use of highly efficient Feature Pyramid Networks (FPNs) to detect objects of small sizes, and various scales which are the main challenge in low-altitude aerial images. FPNs aim to resolve scale variation problems in object detection by combining features of multiple levels. The experiments have been performed on a complex low-altitude aerial dataset VisDrone which has multiple categories of classes. The FPN-Cascade detector has been supported by slicing the data horizontally and vertically that resulted in an advancement of 8% mAP when compared with the base detector. The experiments compare the FPN-Cascade performance on the standard as well as augmented VisDrone dataset. A concrete methodology about the training process, hyperparameter tuning, and performance evaluation methods for Cascade RCNN on the VisDrone dataset is highlighted. The proposed framework achieves state of the art 30.04% mAP value on the VisDrone dataset.
Multi-class Skin Disease Classification Using Transfer Learning Model
Authors: Anand Vatsala , Gupta Sheifali , Koundal Deepika , Nayak Soumya Ranjan , Nayak Janmenjoy , Vimal S.
Published: March 2022

The human body’s major organ is the skin, and it protects human beings from the outside environment. Detecting skin disease at an earlier stage is a big challenge because of the similar appearance of skin disease. Although skilled dermatologists find it challenging to forecast skin lesions due to lack of contrast between adjoining tissues. Therefore, there is a need for an automated system that can detect skin lesions timely and precisely. Recently Deep Learning (DL) has attained outstanding success in the diagnosis of various diseases. Thus, in this paper, a transfer learning-based model has been proposed with help of pre-trained Xception model. The Xception model was modified by adding layers such as one pooling layer, two dense layers and one dropout layer. A new Fully Connected (FC) layer changed the original Fully Connected (FC) layer with seven skin disease classes. The proposed model has been evaluated on a HAM10000 dataset with large class imbalances. The data augmentation techniques were applied to overcome the unbalancing in the dataset. The new results showed that the model has attained an accuracy of 96.40% for classifying skin diseases. The proposed model is working best on Benign Keratosis and the values of precision, sensitivity and F1 score are 99%, 97% and 0.98 respectively. This method can provide patients and doctors with a good notion of whether or not medical assistance is required, thus, avoiding undue stress and false alarms.
Chern degree functions
Authors: Lahoz Martí , Rojas Andrés
Published: March 2022

We introduce Chern degree functions for complexes of coherent sheaves on a polarized surface, which encode information given by stability conditions on the boundary of the [Formula: see text]-plane. We prove that these functions extend to continuous real valued functions and we study their differentiability in terms of stability. For abelian surfaces, Chern degree functions coincide with the cohomological rank functions defined by Jiang–Pareschi. We illustrate in some geometrical situations a general strategy to compute these functions.
On the level property of two-dimensional monomial ideals
Authors: Thuy Phan Thi
Published: March 2022

Let [Formula: see text] be an ideal of the form [Formula: see text] in a polynomial ring [Formula: see text] over a field [Formula: see text], where [Formula: see text] is a prime ideal generated by variables [Formula: see text] for [Formula: see text]. In this paper, we will characterize the level property of [Formula: see text] in the case [Formula: see text] takes a positive value [Formula: see text] or [Formula: see text]. In such a case, we also give an explicit formula for the last Betti number of this ring.
Characterization of Surface-Breaking Cracks on Tubular Structures Using Ultrasonic Phased Array with Rayleigh Waves
Authors: Xiao Jing , Hu Yue , Cao Shuai , Cui Fangsen
Published: March 2022

Surface-breaking cracks are typical defects in tubular structures. Compared with other types of defects such as internal voids, surface cracks often impose more serious threats to structural integrity. This study presents an approach to detect and characterize surface-breaking cracks on tubular samples through the use of ultrasonic phased array technology with surface acoustic waves (SAWs). A Rayleigh type surface wave is selected in our work as it is nondispersive and highly sensitive to surface and subsurface defects. Finite element (FE) analysis is used to simulate the interaction between Rayleigh waves and surface-breaking cracks with varying depths, inclined angles and profiles. The reflection coefficient of Rayleigh waves is calculated based on simulation results and fitted to a proper model. Crack depths and inclined angles can be evaluated from the fitted curves. Furthermore, a full matrix capture (FMC) data acquisition strategy is simulated in FE models with phased array to collect pulse-echo signals of Rayleigh waves. An array imaging algorithm is applied to FMC data and adapted to curved surface. The profile and location of surface cracks are reconstructed from imaging results. The configuration of phased array is optimized to increase the resolution of the method. The proposed approach is validated numerically and provides an efficient way to measure the length, depth and inclined angle of surface-breaking cracks on tubular component.
Property—A Metaphor Beyond Economics Exploring the Semiotics of Property Among the Nayars in Kerala, South India
Authors: LEKHA N. B.
Published: March 2022

Property is one of the important instrumental elements, in the empowerment of women. Matrilineal system in Kerala had privileged Nayar women to inherit property. Inheritance of property rights among Nayar women had undergone dramatic changes during and since the British period. It is difficult to understand the relationship between property and women empowerment as property relations are imbricate and constantly changing. In this paper, I explore how the nature of property influences women empowerment in different regions of Kerala. The sociological contours of property relations of Nayar women are explored to understand how property becomes an agent that helps women to exercise power in the family and bargain within matriliny. The material, cultural and political relations around property need to be reckoned in understanding the power dynamics within the gender relations in Nayar community in Kerala.
Revealing the Potency of Natural Compounds as Dual Kinase Blockers by Structure-Based Modeling and Extensive MD Simulation Strategy
Authors: Muthu Kumar Thirunavukkarasu , Ramanathan Karuppasamy
Published: March 2022

Upregulation of MAPK pathway receptors has been majorly reported for the accumulation of tumor cells in non-small cell lung cancer (NSCLC) patients. Several scientific endeavors were committed to developing checkpoint inhibitors for MAPK intermediates. However, poor toxicity profiling and over-expression of other kinases diminish the treatment outcomes. Recently, dual-action drugs are developed to overcome the ineptness and drug resistance that typically occur when the usage of individual checkpoint inhibitors. Therefore, we screened 1574 natural compounds from the NPACT database using high-end computational tools against these attractive kinase targets (RAF and MEK). Initially, Glide docking and prime-MM/GBSA analysis yielded a total of seven hit compounds that showed better binding potency on both targets. The van der Waals interaction energy highly favors the binding of the compound with respective target receptors. Later, we used dynamics ensembles to examine the essential and stable interactions in the complex structures through 16 different MD simulations (50[Formula: see text]ns) cumulatively in both RAF and MEK systems. These extensive MD analyses resulted in two lead compounds such as NPACT00282 and NPACT01075 having the potential to make a stable complex with RAF and MEK receptors. Notably, the interaction of compounds with CYS532 and TYR125 in RAF and MEK, respectively, might play a crucial role in the compound’s binding process. Furthermore, these lead compounds exhibited good pharmacokinetic/dynamic characteristics. Collectively, we believe that these lead compounds are able to provide better therapeutic options and thus overcome the shortcomings in lung cancer treatment.
Topotactic Oxygen Release and Incorporation in AFeO3 with Fe4+, AFeO2.5 with Fe3+, and AFeO2 with Fe2+ (A = Ca and Sr): Dedicated to the Occasion of the 100th Birthday of Prof. John B. Goodenough
Authors: Shimakawa Yuichi , Goto Masato , Patino Midori Amano
Published: March 2022

Oxygen contents in perovskite-structure Fe oxides can change in accordance with the valence states of Fe, i.e., AFeO3 with Fe4+, AFeO2.5 with Fe3+, and AFeO2 with Fe2+ (A = Ca and Sr). AFeO3 has a fully oxygenated simple-perovskite structure, and the unusual high valence Fe4+ in AFeO3 is easily reduced to relatively stable Fe3+ by releasing oxygen. On the other hand, AFeO2 has an infinite-layer structure, and the unusual square-planar coordination of Fe2+ in AFeO2 changes to tetrahedral and octahedral Fe3+ by incorporating oxygen. Sample weight measurements by thermogravimetry and corresponding phase analysis with synchrotron X-ray diffraction data revealed that the difference in the A-site cation strongly influenced the oxygen release and incorporation behaviors. In ambient air, topotactic changes of AFe4+O3 → AFe3+O2.5 ← AFe2+O2 for both A = Ca and Sr can occur by releasing and incorporating oxygen in the perovskite structure frameworks. Nonstoichiometric phases with oxygen vacancies are present between SrFeO3 and SrFeO2.5.
Evaluation and Improvement of English-Speaking Instruction Based on PLS-SEM and Intelligent Speech System
Authors: Zhang Wei , Wang Bo , Zhou Lili
Published: March 2022

Technological progress has brought about significant changes in education, and the development of intelligent speech systems has provided a new technical support and a broader resource platform for teaching and evaluating college English speaking. Evaluation is the basis for optimizing college English-speaking instruction (ESI); this paper explores the influencing factors of ESI and their relationship through PLS-SEM. Students’ course cognition, learning behavior, knowledge mastery, and teacher-student communication have a significant positive relationship with course satisfaction and classroom effect. On this basis, we make suggestions for optimizing college ESI from three aspects: resource acquisition, platform utilization, and teaching evaluation in combination with an intelligent speech system.
Oral Sex following Abortion: Case Report of a Sexually Transmitted Infection of Group A Streptococcus Causing Peritonitis
Authors: Chan Kenneth L. , Haraoui Louis-Patrick , Demczuk Walter , Domingo Marc-Christian , Bergeron Eric
Published: March 2022

Group A Streptococcus (GAS) is a rare cause of peritonitis with only a few reports of disease associated with surgical abortion, vaginal delivery, or intrauterine devices, most of which are speculated to be in association with the female genital tract. Only a single case of GAS infection transmission through contemporary oral sex has been previously reported. We report a strange case of GAS peritonitis occurring after abortion and oral sex.
lncRNA ADAMTS9-AS1/circFN1 Competitively Binds to miR-206 to Elevate the Expression of ACTB, Thus Inducing Hypertrophic Cardiomyopathy
Authors: Feng Wei , Han Shuo
Published: March 2022

Hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease and can result in substantial disability. The current study explored the potentials of long noncoding RNA- (lncRNA-) circular RNA- (circRNA-) microRNA- (miRNA-) messenger RNA (mRNA) networks in HCM. Firstly, HCM-related microarray data were procured from the GEO database, with differentially expressed genes (DEGs) obtained. HCM-related target genes were retrieved in combination with GeneCards and CTD databases, and candidate target genes were subsequently obtained by intersection screening. Further, an interaction network diagram of candidate target genes was constructed using the STRING database, and the hub genes in the network were determined according to the core degree. The “ClusterProfiler” package of the R software was adopted for GO and KEGG analyses of candidate target genes, to analyze the potential molecular pathways in HCM. Next, upstream miRNA, lncRNA, and circRNA of ACTB were predicted with RNAInter, mirDIP, TargetScan, DIANA-LncBase, and StarBase databases, followed by construction of lncRNA/circRNA-miRNA-mRNA coexpression networks. ACTB, miR-206, circFN1, and ADAMTS9-AS1 expression in peripheral blood samples from HCM patients and normal healthy controls were detected using RT-qPCR. Moreover, rat cardiomyocyte cell lines H9c2 and HEK293 cells were selected for in vitro verification of competitive endogenous RNA (ceRNA) regulation mechanism. A total of 15 candidate target genes related to HCM were screened using the online databases. Further protein-protein interaction analysis identified ACTB as the hub gene for HCM. The targeted binding relationship between miR-206, miR-145-5p, miR-1-3p, and ACTB was found. Furthermore, ADAMTS9-AS1 and circFN1 were discovered as the upstream genes of miR-206. Moreover, ADAMTS9-AS1, circFN1, and ACTB were found to be poorly expressed, and miR-206 was highly expressed in HCM. In vitro experimentation further confirmed that ADAMTS9-AS1 and circFN1 could competitively bind to miR-206, thereby augmenting ACTB expression. Taken all, ADAMTS9-AS1/circFN1-miR-206-ACTB regulatory network may involve in HCM occurrence, providing a novel theoretical basis for in-depth understanding of mechanism of HCM.
Efficiency Comparison of New Adjusted Nonparametric and Parametric Statistics Interval Estimation Methods in the Simple Linear Regression Model
Authors: Sinsomboonthong Saichon , Sinsomboonthong Juthaphorn
Published: March 2022

In this research, the authors were interested in an efficiency comparison study of new adjusted nonparametric and parametric statistics interval estimation methods in the simple linear regression model. The independent variable and the error came from normal, scale-contaminated normal, and gamma distributions. Six point estimations were performed, for example, least squares, Bayesian, Jack knife, Theil, optimum-type Theil, and new adjusted Theil–Sen and Siegel methods in the simple linear regression model with 1,000 iterations. The criteria used to consider in this study were the coefficient of the confidence interval and the average width of the confidence interval used to compare and determine the optimal effectiveness for six interval estimations of the simple linear regression model. In the interval estimation for normal and scale-contaminated normal distributions ofβ0, the least squares method had the narrowest average width of confidence interval. For the interval estimation ofβ1, the Bayesian method had the narrowest average width of confidence interval in a small variance of 1, followed by the same of optimum-type Theil and new adjusted Theil–Sen and Siegel methods, and Theil method, respectively. In the interval estimation for gamma distribution ofβ1, the Bayesian method had the narrowest average width of confidence interval, followed by optimum-type Theil, new adjusted Theil–Sen and Siegel, and Theil methods, respectively. The optimum-type Theil method was good for medium sample size, while Theil and new adjusted Theil–Sen and Siegel methods were good for small and large sample sizes. Therefore, new adjusted Theil–Sen and Siegel method can be used in many situations and can be used in place of optimum-type Theil and Theil methods for nonparametric statistics interval estimation.
Forecast of Foreclosure Property Market Trends during the Epidemic Based on GA-BP Neural Network
Authors: Fang YaoChing
Published: March 2022

Due to the epidemic, foreclosure, as a special transaction commodity, has also been affected to a certain extent. This paper uses the sample data of foreclosure auctions combined with the genetic algorithm multi-layer feedforward neural network to apply in the forecast of the foreclosure market trend. First of all, to select the indicators that affect the price fluctuation of foreclosed houses, use factor analysis technology to reduce the dimensionality of the original data to obtain two common factors; then combine the BP neural network and the genetic algorithm to propose a GA-BP neural network The prediction model of the foreclosure housing market takes the influencing factors as the input of the model and the housing price as the output. Finally, the prediction results are compared with the multiple linear regression and the extreme learning machine, which shows that the GA-BP neural network model is in It is a good choice for research, and it provides an effective method for predicting housing prices.
Effects of Dietary Protein Level on the Microbial Composition and Metabolomic Profile in Postweaning Piglets
Authors: Gao Jing , Liu ZeMin , Wang ChenYu , Ma Li , Chen Yongzhong , Li TieJun
Published: March 2022

Since the human and porcine digestive systems have similar anatomical structures and physiological functions, pigs are a useful animal model for studying human digestive diseases. By investigating intestinal metabolites in piglets after weaning, this study attempted to identify the inherent connection between dietary protein levels and changes in the intestinal microbiota of piglets. Casein was employed as the only source of protein for the piglets in this study to avoid the influence of other protein sources. 14 weaning at 28-day-old piglets (

6.9
±
0.19

 kg) formed into two dietary groups: 17% casein fed group (LP) and 30% casein fed group (HP). Piglets were allowed to free food and water during the 2-week experiment. Throughout the trial, the piglets’ diarrhea index (1: no diarrhea and 3: watery diarrhea) and food intake were noted during the experiment. We discovered piglets fed a high-protein diet developed diarrhea throughout the duration of the research, whereas piglets fed a normal protein diet did not. In addition, the HP group had lower feed intake and body weight than the control group (

P
<
0.05

). The HP diet influenced the content of short-chain and branched-chain fatty acids in the colon, including acetate and isovaleric acid. The ileal microbiota’s 16S rRNA gene was sequenced, and it was discovered that the relative abundance of gastrointestinal bacteria differed between the HP and control groups. Dietary protein levels influenced bile acid biosynthesis, alpha-linolenic acid metabolism, phospholipid biosynthesis, arachidonic acid metabolism, fatty acid biosynthesis, retinol metabolism, arginine and proline metabolism, pyrimidine metabolism, tryptophan metabolism, and glycine and serine metabolism, according to gas chromatography-mass spectrometry analysis. Furthermore, a correlation analysis of the pooled information revealed a possible link between intestinal metabolites and specific bacteria species. These findings demonstrate that weaned piglets’ microbiota composition and metabolites are modified by a high-protein diet and thus inducing severe postweaning diarrhea and inhibiting growth performance. However, the potential molecular mechanism of this regulation in the growth of piglets remains unclear.
Waypoint Tracking Control for Autonomous Mobile Sampling and Dissolved Oxygen Enrichment of Unmanned Surface Vehicle
Authors: Yuan Jian , Liu Hailin , Zhang Wenxia
Published: March 2022

An autonomous monitoring and control system of unmanned surface vehicle (USV) with mobile water quality monitoring, sampling, and oxygenation functions is constructed. The control hardware and monitoring configuration software of the system is designed, respectively, which can be installed on USV and its remote control and monitoring terminal. The kinematic modeling of USV, waypoint trajectory-tracking control, distributed controller, simulation of tracking control, and verification of software and hardware design are carried out. In order to reject the system noise and external noise, a states estimation method with fully observable states is considered in the control law design. The software and hardware are also implemented to verify the effectiveness of the monitoring platform. Through setting a series of monitoring target points and monitoring parameters in the configuration software of the remote user terminal or in the APP of the mobile user terminal, the USV can realize the automatic cruise monitoring using an autonomous navigation and tracking control algorithm, and quantitative water sampling collection. The reliability of the system is verified by the experiment of the shore test station, and the waypoint trajectory tracking and sensors data are replaying in a logview GUI of MOOS-Ivp and APP.
Dimension Measurement and Quality Control during the Finishing Process of Large-Size and High-Precision Components
Authors: Lv Fei , Hu Chang’an , Du Wenbo , Wang Xi
Published: March 2022

The accurate measurement and control of the geometric dimensions and shape errors of large-size and high-precision key components are key factor to ensure the machining quality of the equipment package. Aiming to address the urgent problems of poor measurement conditions, complicated error propagation, and difficulty in obtaining accurate measurement results, this research studied the measurement method and control of dimensional accuracy and geometric tolerance in the process of hole machining at room temperature, taking Metso MP 1250 cone level 6 precision point pair workpiece as the object. Through linear analysis and demonstration of quality problems in the existing processing technology, the influence of the expansion coefficient of the workpiece material, measurement error and other factors is analyzed. After correcting the temperature error model, many measurements were carried out in machining experiments, and the normal ability was analyzed by Minitab software. The experimental results showed that the workpiece temperature rises by 1°C, the workpiece will be deformed by 8.503 um. In the measurement process of high-precision and large-size components, the correction accuracy of the temperature error is affected by the combined influence of the model error, the material expansion coefficient error, and the temperature measurement error. The results of this study are of guiding significance for the measurement of the dimension and geometric tolerance precision of large-size and high-precision castings.
hAMSC Sheet Promotes Repair of Rabbit Osteochondral Defects
Authors: Zou Gang , Zhang Jun , Yang Qifan , Wang Xiaoyan , Sun Pengpeng
Published: March 2022

Osteochondral lesion is clinically common disease, which has been recognized as one of the contributing factors of significant morbidity. Although current treatments have achieved good outcomes, some undesirable complications and failures are not uncommon. Cell sheet technology (CST), an innovative technology to harvest seed cells and preserve abundant ECM, has been widely used in various tissue regeneration. For osteochondral lesion, many studies focus on using CST to repair osteochondral lesion and have achieved good outcomes. In the previous study, we have demonstrated that hAMSC sheet had a positive effect on osteochondral lesion. Therefore, this study is aimed at comparing the effect of noninduced hAMSC sheet with chondrogenically induced hAMSC sheet on osteochondral lesion and cartilage regeneration.
Scheduling and Optimization of Wireless Virtual Network Resources Based on Reinforcement Learning
Authors: Wang Junxiang
Published: March 2022

With the booming and proliferation of 5G wireless network services in the future, a large number of wireless virtual network resources will emerge, and the densification and heterogeneity of the wireless communication networks that provide services for them will become the trend of development. To this end, a wireless virtual network resource scheduling model based on user satisfaction is constructed, and a reinforcement learning-based wireless virtual network resource scheduling mechanism, IRSUP, is proposed. IRSUP is designed with an intelligent optimization module for user service preferences to address the personalized needs of user service customization, and a reinforcement learning-based intelligent scheduling module is designed to address the challenge of joint optimization of multistar resources. Simulation results show that IRSUP can effectively improve resource scheduling rationality and link resource utilization and user satisfaction, among which service capacity is improved by 30% to 60% and user satisfaction is more than doubled.
Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models
Authors: Abdulkareem Karrar Hameed , Mostafa Salama A. , Al-Qudsy Zainab N. , Mohammed Mazin Abed , Al-Waisy Alaa S. , Kadry Seifedine , Lee Jinseok , Nam Yunyoung
Published: March 2022

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.
The Impact of Financial Development on Agricultural Enterprises in Central China Based on Vector Autoregressive Model
Authors: Kang Jian , Zhao Minjuan
Published: March 2022

China is a large developing country, and there is no doubt that it is a large developing agricultural country. Agriculture has played a vital role in the development of the national economy since ancient times, and it serves as the foundation of China’s national economy. As agricultural modernization is the fundamental way of agricultural development, its source of funds must come from rural finance. The financial support of rural finance for agricultural modernization is the material basis and prerequisite for ensuring the healthy and rapid development of agricultural modernization. If there is no financial support from rural finance, the process of agricultural modernization will be hindered, and it will not benefit our country’s socialist modernization. Although our country’s rural financial system has been constantly changing and improving, our country’s current financial system still has many shortcomings and defects. In the process of supporting agriculture, rural finance has insufficient capital supply, which has become a “bottleneck” in the development of agricultural modernization, and to a certain extent has severely restricted the development of agricultural modernization. On the basis of existing literature research, this article systematically studies the influence of rural financial market structure on the development of agricultural enterprises in central China. The content includes agricultural total factor productivity and agricultural emission reduction. When analyzing the impact of rural financial market structure on agricultural development, this article also pays attention to its specific impact on agricultural development under the conditions of different types of enterprises.
Efficacy of Chinese Medicine Injection for Cardiotoxic Injury of Anthracycline Chemotherapy Drugs: A Network Meta-Analysis of Randomized Controlled Trials
Authors: Li Mingxuan , Li Hongdian , Liu Hongxu , Lai Xiaolei , Xing Wenlong
Published: March 2022

Background. Chinese medicine injections (CMIs) are widely used in the prevention and treatment of cardiotoxicity caused by anthracycline chemotherapeutic drugs. However, it is uncertain that CMIs are more effective in combating the cardiotoxicity of anthracyclines. The aim of this Network Meta-analysis (NMA) was to compare the treatment effects of various CMIs in order to determine the best CMI for the prevention and treatment of cardiac damage from anthracyclines. Methods. The Chinese Journal Full Text Database (CNKI), Wanfang Database, Chinese Science and Technology Journal Full Text Database (VIP), Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, and Cochrane Library databases were searched to screen randomized controlled trials (RCTs) of CMIs against cardiotoxicity of anthracycline-based chemotherapeutic drugs. The search time frame was all from the establishment of the database to October 1, 2021. After independent screening of the literature, extraction of information and evaluation of the risk of bias of the included studies by two evaluators, mesh meta-analysis was performed using RevMan 5.3, Stata 15.1, and ADDIS 1.16.8 software. Results. A total of 33 studies including 2783 patients, including 1410 cases in the experimental group and 1373 cases in the control group were included, and six CMIs were extracted, namely, Shenfu injection, Shenmai injection, Shenqi Fuzheng injection, Shengmai injection, Xinmailong injection, and Haungqi injection. The results of the reticulated meta-analysis showed that in terms of ST-T segment (ECG change) change rate, Haungqi injection, Shenfu injection, and Xinmailong injection were superior. In terms of lowering CK-MB, Huangqi Injection and Shenqi Fuzheng injection were superior. In terms of improving Left ventricular ejection fraction (LVEF), Shenfu injection, Huangqi Injection, and Shengmai injection were more effective than other injections. In terms of improving LVEDD, Shengmai injection, Huangqi Injection, and Xinmailong injection have advantages. Conclusion. The six CMIs included in this study are effective against cardiotoxicity caused by anthracycline-based chemotherapeutic agents. Huangqi Injection and Shenfu injection are both superior in improving various outcome indicators. There is still a need for larger, high-quality randomized controlled trials (RCTs) to compare the various CMIs in a more refined way.
TSPO Ligands Protect against Neuronal Damage Mediated by LPS-Induced BV-2 Microglia Activation
Authors: Ma Baoyu , Liu Yongxin , Zhang Xiao , Zhang Rui , Zhang Zhenjiang , Zhang Zhihao , Liu Jie , Juan Zhaodong , Sun Xiaotong , Sun Lina , Huang Jingyao , Feng Jinpeng
Published: March 2022

Neuroinflammation is a critical pathological process of neurodegenerative diseases, and alleviating the inflammatory response caused by abnormally activated microglia might be valuable for treatment. The 18 kDa translocator protein (TSPO), a biomarker of neuroinflammation, is significantly elevated in activated microglia. However, the role of TSPO in microglia activation has not been well demonstrated. In this study, we evaluated the role of TSPO and its ligands PK11195 and Midazolam in LPS-activated BV-2 microglia cells involving mitophagy process and the nucleotide-binding domain-like receptor protein 3 (NLRP3) inflammasome activation. In the microglia-neuron coculture system, the neurotoxicity induced by LPS-activated microglia and the neuroprotective effects of PK11195 and Midazolam were evaluated. Our results showed that after being stimulated by LPS, the expression of TSPO was increased, and the process of mitophagy was inhibited in BV-2 microglia cells. Inhibition of mitophagy was reversed by pretreatment with PK11195 and Midazolam. And the NLRP3 inflammasome was increased in LPS-activated BV-2 microglia cells in the microglia-neuron coculture system; pretreatment with PK11195 and Midazolam limited this undesirable situation. Lastly, PK11195 and Midazolam improved the cell viability and reduced apoptosis of neuronal cells in the microglia-neuron coculture system. Taken together, TSPO ligands PK11195 and Midazolam showed neuroprotective effects by reducing the inflammatory response of LPS-activated microglia, which may be related to the enhancement of mitophagy and the inhibition of NLRP3 inflammasome.
SPMSM Sensorless Control System Based on ASMO
Authors: Cao Yu , Liu Chuang
Published: March 2022

Aiming at the chattering problem of traditional sliding mode observer (SMO) in surface-mounted permanent magnet synchronous motor (SPMSM) control system when estimating rotor position and speed and adaptive sliding mode observer (ASMO) is designed in this paper. The observer can reduce chattering and avoid the introduction of the low-pass filter, which simplifies the system structure, considering the difficulty in determining the sliding mode gain of the existing sliding mode observer, and an adaptive law is designed to adjust the sliding mode gain with the change of the back electromotive force, to meet the operation requirements of the system and improve the control accuracy of the sensorless control system. Finally, the simulation is built through Matlab/Simulink platform. It is proved that the proposed control strategy can satisfy the system’s accuracy and simultaneously reduce the chattering and solve the problem that the gain is difficult to determine.
The Influence of Green Marketing on Brand Trust: The Mediation Role of Brand Image and the Moderation Effect of Greenwash
Authors: Wu Lanmeng , Liu Ziyang
Published: March 2022

For the purpose of understanding the effect of green marketing on brand image, this paper has added mediation variable namely brand image into the research model; meanwhile, in order to better discuss about this issue in the context of green marketing, the paper considers that greenwash is an indispensable element in adjusting green marketing’s effect on the brand. The paper has used the method of a questionnaire survey to distribute questionnaires on online social platforms. A total of 500 questionnaires have been distributed, and 415 valid questionnaires have been collected with an effective collection rate of 83%. In this paper, SPSS 24 and AMOS 23 are applied to verify its theoretical model and structural model. The result manifests that the design of the questionnaire is well reliable and highly effective; meanwhile, variables are significantly correlated. Besides, it concludes that the relationship between green marketing and brand trust differentiate in the following two dimensions: spontaneity and compulsion. Moreover, brand image plays an obviously intermediary role between green marketing and brand trust, which also proves that greenwash works as a significantly negative regulatory role.
An Adaptive Human Posture Detection Algorithm Based on Generative Adversarial Network
Authors: Xu Zhiming , Qu Wenzheng , Cao Hanhua , Dong Meixia , Li Danyu , Qiu Zemin
Published: March 2022

Human posture equipment technology has advanced significantly thanks to advances in deep learning and machine vision. Even the most advanced models may not be able to predict all body joints accurately. This paper proposes an adaptive generative adversarial network to improve the human posture detection algorithm in order to address this issue. GAN is used in the algorithm to detect human posture improvement. The algorithm uses OpenPose to detect and connect keypoints and then generates heat maps in the GAN system model. During the training process, the confidence evaluation mechanism is added to the system model. The generator predicts posture, while the resolver refines human joints over time. And, by using normalization technologies in the confidence evaluation mechanism, the generator can pay more attention to the prominent body joints, improving the algorithm’s body detection accuracy of nodes. In MPII, LSP, and FLIC datasets, the proposed algorithm has shown to have a good detection effect. Its positioning accuracy is about 95.37 percent, and it can accurately locate the joints of the entire body. Several other algorithms are outperformed by this one. The algorithm described in this article has the best simultaneous runtime in the LSP dataset.
Home-Made Membraneless Vaporization Gas-Liquid Separator for Colorimetric Determination of Ethanol in Alcoholic Beverages
Authors: Phouthavong Vanpaseuth , Inoue Hayato , Phomkeona Kesiny , Chounlamany Vanseng
Published: March 2022

This work utilized the simplicity of a so-called membraneless vaporization (MBL-VP) unit as a gas separator for the colorimetric determination of ethanol in alcoholic beverages. A beverage sample with a volume of 1 mL was directly injected into a small container which was hung from a lid inside a closed 40 mL reused glass bottle without pretreatment such as distillation. An acidified potassium dichromate (Cr2O72−) acceptor solution, preadded to the glass bottle, was reduced to Chromium (III) ion by the diffusion of vaporized ethanol from the sample. After 5 min, the absorbing solution was collected for colorimetric detection at 590 nm. The unit manually quantifies ethanol in the range 1.0–90% (v/v) with satisfactory interday precision but without matrix effect (recovery 89−109%). The method was validated with the conventional distillation/pycnometer method which showed no significant difference of ethanol contents between those two methods and the declared values of 12 alcoholic beverages, indicating sufficient accuracy. Analyses of alcoholic beverages using this method were successful with benefits of simplicity, cheapness, and less energy consumption.
Antimicrobial Analysis of Biosynthesized Lectin-Conjugated Gold Nanoparticles
Authors: Alnashiri Hassien M. , Aldakheel Fahad M. , Binshaya Abdulkarim S. , Alharthi Nahed S. , Ahmed Musthaq
Published: March 2022

To enhance the bioactivity of molecules through nanoparticles is being tested which has potential use in sustained-release drug delivery systems and to enhance the therapeutic effectiveness of drugs. Our current investigation s is to conjugate lectin to that of a gold nanoparticle (GNP) surface without disturbing the bioactive properties and enhances the antibacterial activity of lectin. Au-lectin nanoparticles were checked for their hemagglutination activity, characterized by transmission electron microscopy (TEM) and UV-visible spectrophotometer. The antibacterial effect of nanoparticle lectin, Au salt nanoparticle, and conjugated Au-lectin was estimated by Kirby-Bauer disc method; MICs were determined by microbroth dilution and compared with ciprofloxacin. These tests were done using known species of bacterial strain of multidrug resistant. The hemagglutination activity of lectin was improved to fourfold after purification. Lectin and Au nanoparticles combined had a significant effect on the inhibition of bacterial growth. No significant differences were observed in the inhibition zone diameters from killed bacteria and its supernatant towards any of the tested organisms. Lectin-conjugated gold particles showed good efficacy as antimicrobial agents and the nanoparticle-killed bacteria to work against the viable population of the same bacterium and/or other bacterial species too.
Application and Analysis of Taekwondo Techniques, Tactics, and Movement Trajectories Based on Multi-Intelligent Decision Making
Authors: Lu Weian , Lin Xiaotao
Published: March 2022

The aim of this study is to compare the trajectory of cross-kick technical movements of excellent taekwondo players in different weight classes and to investigate the relationship between weight class differences and the characteristics of cross-kick technical movements and the extent to which they affect the speed of movements. To this end, an artificial intelligence system is proposed for taekwondo field decision making, combining computer and applied mathematics knowledge. The angles, angular velocities, and moments of the joints of the lower limbs, as well as the movement time, displacement, and speed of the lower limbs, of the athletes in the three weight classes were significantly different (

P
<
0.05

).
Understanding of Immune Escape Mechanisms and Advances in Cancer Immunotherapy
Authors: Aktar Nasrin , Yueting Chen , Abbas Muhammad , Zafar Hajra , Paiva-Santos Ana Cláudia , Zhang Qin , Chen Tingting , Ahmed Moudud , Raza Faisal , Zhou Xiaohui
Published: March 2022

Tumor immune escape has emerged as the most significant barrier to cancer therapy. A thorough understanding of tumor immune escape therapy mechanisms is critical for further improving clinical treatment strategies. Currently, research indicates that combining several immunotherapies can boost antitumor efficacy and encourage T cells to play a more active part in the immune assault. To generate a more substantial therapeutic impact, it can establish an ideal tumor microenvironment (TME), encourage T cells to play a role, prevent T cell immune function reversal, and minimize tumor immune tolerance. In this review, we will examine the mechanisms of tumor immune escape and the limits of tumor immune escape therapy, focusing on the current development of immunotherapy based on tumor immune escape mechanisms. Individualized tumor treatment is becoming increasingly apparent as future treatment strategies. In addition, we forecast the future research direction of cancer and the clinical approach for cancer immunotherapy. It will serve as a better reference for researchers working in cancer therapy research.