bluuebunny/crossref_metadata_2025 huggingface dataset:
(Digital Presentation) Electrodeposition of Electrolytic Niobium Coatings on the Cryogyroscope Rotor
Authors: Okunev Maksim A , Dubrovskii Anton R , Makarova Olga V , Kuznetsov Sergey A
Published: September 2022

The discharge process of Nb(IV) complexes to Nb was studied by cyclic voltammetry. A galvanostatic electrolysis with the cathodic current density of 1.5·10-2 A cm-2 in the NaCl-KCl-NaF-K2NbF7 melt in contact with Nb was used for deposition of niobium coatings at temperature 1023 K, and time of process from 8 to 12 hours. The coatings were continuous and smooth due to the special design of the cathode. The rotation speed of the stirrer in all experiments did not change and was chosen equal to 35 rpm. Such conditions made it possible to obtain coatings with the thickness up to 150 μm. The main characteristics of the niobium coatings such as purity, roughness, nonsphericity and superconductivity were investigated.
(Digital Presentation) Modern State and Prospects of Electrochemical CO2 Conversion in Molten Salts
Authors: Novoselova Inessa , Omel'chuk Anatoliy
Published: September 2022

An innovative environmentally friendly electrochemical method with using molten salts for direct absorption and transformation of CO2 into solid carbon-containing materials of unique structure and morphology has been developed. The paper presents a review of literature data on electrochemical conversion of carbon dioxide in molten salts as well as the authors’ research results on the direct electrochemical reduction of CO2 and Li2CO3 in chloride melts into different carbonaceous materials with added value. They include: nanoscale solid carbon of various structures and morphologies (carbon nanotubes, fibers, honeycomb-like and scaly carbon nanostructures, etc.). Several variants of CO2 electrochemical conversion have been considered, the advantages and disadvantages of each variant have been analyzed. Conditions and mechanisms of electrochemical reduction of CO2 and Li2CO3 to solid carbon are discussed. Promising applications of electrolytic carbon nanomaterials for rechargeable ion batteries, supercapacitors and catalysts are emphasized.
Efficient Sensor Calibration Via Machine Learning-Based Resampling Methods
Authors: Ding Alison , Wang Jiacheng , Hang Yingjie
Published: September 2022

A sensor’s performance relies on the calibration model functionally relating the sensor’s response (denoted as y) to the target analyte concentration (denoted as x). However, classic statistical methods are not able to adequately quantify the uncertainty of the x concentration estimates. In this work, a machine learning (ML)-based computational resampling method was adapted to address the uncertainty quantification issues, based on which a two-stage procedure was developed for efficient sampling and modeling of the calibration data obtained via laboratory experiments. The outputs of the procedure allow for not only a single-value estimate, but also a high-likelihood range for the underlying analyte concentration in an unknown sample being tested. The calibration procedure was applied on a simulated biosensor, and evaluated through large amount of validation data. The sensor simulator was developed from a paper-based lateral flow strip (PLFS) designed for traumatic brain injury (TBI) diagnosis.
Enhanced Oxygen Evolution Activity Aroused from Interfacial Electron Transfer and Synergism in Co(OH)2/MIL-88A Heterostructure
Authors: Zhang Qingxiao , Han Pei , Zhang Hui , Yao Jixin , Wang Xinyu , Chen Dongmeng , Zuo Xueqin , Yang Qun , Jin Shaowei , Li Guang
Published: September 2022

The oxygen evolution reaction (OER) is a critical process in the operation of electrochemical water splitting. Metal-organic frameworks (MOFs) are applied in the field of OER electrocatalysis because of their unique structure, but the high impedance and unstable framework during pyrolysis remain unresolved. Here, MIL-88A (iron-based MOF) was modified by hydrolysis and loading Co(OH)2 to improve the catalytic activity. The electrochemical results showed that the Co(OH)2/MIL-88A achieved the optimized performance with the current density of 10 mA cm−2 at 278 mV (vs RHE). Density functional calculations (DFT) combined with characterization tests suggest that electrons transfer from Co(OH)2 to MIL-88A at the interface of the heterostructure, leading to a larger hole electron pair density. D-band theory calculations demonstrated that the synergistic effect of MIL-88A and Co(OH)2 in heterostructure strengthens the adsorption between active sites and adsorbate. The above proofs are the source of the excellent OER performance of the Co(OH)2/MIL-88A. The finding has certain reference significance for the heterogeneous treatment of iron-based MOF catalysts.
The Effects of NaCl on Hydrogen Permeation and Sulfide Stress Cracking Resistance of C110 High-strength Steel
Authors: Liu Lianlian , Chen Lin , Case Raymundo
Published: September 2022

Sulfide stress cracking (SSC) limits the application of high-strength low-alloy carbon steel C110 in the oil and gas industries. Chloride ions (Cl−) play an indispensable role in SSC. This study aims to evaluate the effects of NaCl on hydrogen permeation and SSC susceptibility by electrochemical techniques including hydrogen permeation test and electrochemical impedance spectroscopy (EIS), and the novel notched tensile slow strain rate test on carbon steel C110 under wet-H2S environments. The results show that the higher concentration of Cl− reduces hydrogen absorption and enhances SSC susceptibility. However, it is widely accepted that the reduction in hydrogen absorption results in an increase in SSC resistance. To figure out the role of Cl− in the SSC mechanism, the morphology of the side and the cross-sectional surface were observed. It was found that a higher pit density in a condition with a higher Cl− concentration leads to increased SSC susceptibility as pits act as crack initiation sites. The SSC mechanism transfers from hydrogen embrittlement to pit-induced cracking when the Cl− concentration in the solution increases.
The mammalian nodal action potential: new data bring new perspectives
Authors: Tanner Geoffrey R. , Tzingounis Anastasios V.
Published: September 2022

Historically, physiology courses have typically taught that action potential repolarization occurs exclusively due to the activation of delayed-rectifier voltage-gated potassium channels. Here, we review and highlight recent evidence that leak potassium channels of the two-pore domain (K2P) class may largely serve this repolarization role at mammalian nodes of Ranvier. We call for the inclusion of these ideas in physiology curricula at all levels, from high school to graduate school.
Realization Path of College Students’ Network Ideological and Political Teaching System in the New Media Environment
Authors: Sun Shucai
Published: September 2022

Network ideological and political education (This article is abbreviated as IPE) refers to the IPE method that uses Internet technology to conduct open teaching to students or audiences of IPE. New media refers to the use of digital technology to provide users with information and services through channels such as computer networks, wireless communication networks, satellites, as well as terminals including computers, mobile phones, and digital televisions. This paper mainly analyzed the realization path of college students’ network IPE system in the new media environment combined with big data technology. In this regard, this paper proposed the K-means clustering algorithm and the ant colony algorithm as the research methods of this paper. Through the questionnaire survey, it is found that the adaptive characteristics of freshman students are more obvious. There are 187 first-year students who are still in the adaptation stage, accounting for 93.5%. These students will have different degrees of difficulties in adapting to psychology, life, and study. These problems affect and restrict the direction and quality of college students’ daily life and self-development. In the new media environment, this paper conducted an experimental analysis on the online ideological education of college students. The experimental results have shown that the K-means clustering algorithm and the ant colony algorithm are effective in the research of the college students’ network IPE system.
Self-Responsive Electrospun Nanofibers Wound Dressings: The Future of Wound Care
Authors: Adamu Biruk Fentahun , Gao Jing , Gebeyehu Esubalew Kasaw , Beyene Kura Alemayehu , Tadesse Melkie Getnet , Liyew Erkihun Zelalem
Published: September 2022

Skin wound management is a very difficult undertaking in the medical field. There is no information available concerning the wound beneath the bandages. Electrospun nanofibrous wound dressings stand out for their resemblance to extracellular matrix (ECM), increased surface-to-volume ratio, porousness, and capacity to encapsulate or load medications, among other distinctive qualities. Traditional antibacterial loaded electrospun nanofibrous wound dressings do not indicate the state of the wound and constantly release antibacterial chemicals even when there are no bacteria in the wound area. As a result, dressings that can track the condition of the wound and dispense medications as needed are crucial. Self-responsive wound dressings can release medications based on bacterial, oxygen spectra, pH, or infection responsiveness, reducing the need for antibacterial agents. Self-responsive mats, which are wound dressings that can release medications based on response to bacteria, oxygen species, and pH or infections, are required to reduce the overuse of antibacterial agents. Self-responsive electrospun nanofibrous mats can be used to monitor the condition of a wound by altering its color in response to an infection or a change in the pH of the wound. Electrospun nanofibrous wound dressings that are stimulus-responsive (self-responsive) are discussed in this review paper. Self-responsive electrospun nanofibrous wound dressings that are triggered by pH, temperature, light, bacteria, and oxygen species are discussed in detail after self-responsive smart materials or polymers used for electrospun nanofibrous wound dressings are covered in the first section of the review.
The Predictive Value of Epicardial Fat Tissue Volume in the Occurrence and Development of Atrial Fibrillation: A Systematic Review and Meta-Analysis
Authors: Fan Qiankun , Zhan Yinge , Zheng Mingqi , Ma Fangfang , Ji Lishuang , Zhang Lei , Liu Gang
Published: September 2022

Background. Atrial fibrillation (AF) is one of the most common arrhythmias in clinical practice. Although fat is currently considered to be a risk factor for AF and a pathogenic link between epicardial fat tissue (EFT) and AF has been speculated, there are currently few clinical studies and literature data domestically or abroad. Objective. This study conducted a meta-analysis of observational case series studies to verify the relationship between atrial fibrillation and EFT and to strengthen the predictive value of EFT in the occurrence, development, and postablative recurrence of AF. Methods. We conducted a systematic search of the literature in electronic databases until December 2021 and supplemented this through manual searches of individual studies, reviewed articles, and reference lists in conference proceedings. This study conducted a meta-analysis to compare the differences between different populations, such as healthy participants and AF patients, healthy subjects and AF subtype cases, and paroxysmal and persistent AF with AF recurrence and without AF recurrence after ablation. Results. Following the retrieval of 828 articles, only 22 articles were selected as research results. Accordingly, the meta-analysis results show that the volume of EFT in AF is greater than that in healthy subjects (MD = 39.34 ml, 95% CI = 27.11, 51.58); persistent AF is greater than paroxysmal AF (MD = 14.37 ml, 95% CI = 7.46, 21.27); and recurrence after ablation is greater than without recurrence (MD = 14.37 ml, 95% CI = 7.46, 21.27). Conclusion. The results of this study further confirm the connection between EFT and AF and that EFT has a certain predictive value for the occurrence and development of AF.
Study on Very Fast Transient Overvoltage Characteristics of Gas-Insulated Switchgear Substation Based on Hybrid Reactive Power Compensation
Authors: Wen Caiquan , Yang Zheng , Lu Yitao , Huang Yongfeng , Lin Hanliang , Zhang Zhiyuan , Chen Taiyu , Liu Yingnan
Published: September 2022

In recent years, gas-insulated switchgear (GIS) substations have become widely used in power systems. Due to their compact structure, extremely high oscillation frequencies are prone to extremely high oscillation transient overvoltages or very fast transient overvoltages (VFTOs) when the isolating switch or circuit breaker is operated. The characteristics of these special fast transient processes in GIS and the effect of hybrid reactive power compensation (HRPC) on these fast transient processes were studied. A simulation of the GIS model was established, and based on the transient model of HRPC and the theory of the transmission line, the effects of VFTO produced by HRPC switching operation and the parameters of HRPC on VFTO were studied and the differences in the characteristics of VFTOs in substations with or without HRPC. Based on the conclusions of our study on the influence of HRPC on VFTO, the inhibitory effects of a ferrite magnet ring and an inhibitor busbar on VFTO were verified.
Preparation of Drug‐Loaded Albumin Nanoparticles and Its Application in Cancer Therapy
Authors: Meng Run , Zhu Huimin , Wang Ziwei , Hao Shilei , Wang Bochu
Published: September 2022

Albumin is derived from plasma and it is the most abundant protein in plasma, which is an ideal material for the preparation of nanoparticles because of its good biocompatibility, noncytotoxicity, nonimmunogenicity, biodegradability, and so on. Besides, albumin can enhance the targeting of drugs, reduce the toxicity of free drugs, and enhance the water solubility of hydrophobic drugs, etc. Drug delivery systems based on albumin nanoparticles are widely used in the medical field. At present, the main methods of preparing albumin nanoparticles are desolvation, self‐assembly, thermal gelation, spray‐drying, double emulsification, emulsification, Nab‐technology, pH coacervation, and so on. Due to the differences of principle and preparation conditions, these methods show different advantages and disadvantages. This review systematically summarizes the latest research progress of albumin nanoparticles about its methods of preparation in past five years, and it also introduces the latest applications in cancer therapy, existing difficulties. Thus, this review can fill the two gaps that few articles focus comprehensively on the application of albumin nanoparticles in tumor therapy and no article clearly points out the difficulties faced in current research of albumin nanoparticles.
Optimizing Extractability, Phytochemistry, Acute Toxicity, and Hemostatic Action of Corn Silk Liquid Extract
Authors: Abudayeh Zead Helmi , Karpiuk Uliana , Kyslychenko Viktoriia , Abualassal Qais , Hassouneh Loay Khaled , Qadus Sami , Talhouni Ahmad
Published: September 2022

The technological parameters and quality indicators of corn silk were evaluated in this study: specific density, volumetric density, bulk density, porosity, spatial layer, free volume of the layer, the absorption coefficient, weight loss on drying, and extractives. The technology for obtaining a liquid extract of corn silk was developed. The most effective extractant was discovered to be 40% ethanol, with an extraction time of 120 minutes. The qualitative composition and quantitative content of the major groups of biologically active substances (BAS) in the obtained liquid extract were determined. The qualitative composition of the main groups of BAS was determined by conventional chemical reactions. This extract contained free reduced sugars, glycosides (bound reduced sugars), phenols, tannins, flavonoids, saponins, and hydroxycinnamic acids. The quantitative content of phenolic compounds was performed by UV-vis-spectrophotometry. Total phenols, tannins, flavonoids, and hydroxycinnamic acids had quantitative contents of 8.25 ± 0.33%, 1.4 ± 0.03%, 2.20 ± 0.06%, 3.30 ± 0.13%, respectively. The acute toxicity study was carried out with a single intragastric administration to outbred unanesthetized white rats of both sexes. Duration of observation of animals was 14 days. It was revealed that corn silk extract at doses of up to 5.0 ml/kg is safe. A single injection of a liquid extract has no effect on internal organs when compared to a control group. Corn silk liquid extract’s hemostatic efficacy was assessed using blood clotting time, prothrombin time, and blood clot retraction index. The corn silk liquid extract reduces blood coagulation time, decreases prothrombin time, and increases the blood clot retraction index. According to these findings, corn silk liquid extract is rich in phytochemicals and possesses a potential therapeutic effect on bleeding disorders. Furthermore, it could be used in the pharmaceutical sciences industry to develop medicines for testing in the treatment of various diseases.
Downregulated PRNP Facilitates Cell Proliferation and Invasion and Has Effect on the Immune Regulation in Ovarian Cancer
Authors: Hu Kuan , Zhang Xiaofang , Zhou Lei , Li Juanni
Published: September 2022

Background. Ovarian cancer (OC) seriously threatens women’s life. Ferroptosis plays an essential role in the initiation and development of OC. However, more molecular targets and mechanisms for ferroptosis in OC remain to be further elucidated. Methods. Several OC datasets were integrated in this study and three candidate genes including PRNP were further screened out as the ferroptosis-related gene which was differentially expressed in OC. Then, comprehensive evaluations concerning gene expression, clinical implication, in vitro validation of expression and functional experiments, prediction of downstream molecules and related signal pathways, and immune-modulating function were performed. Results. PRNP was the only downregulated ferroptosis-related gene with prognostic value for OC patients. The decreased mRNA and protein expression was verified in OC tissues and cell lines. PRNP was significantly correlated with cancer stages, primary therapy outcomes, and age in OC patients. Moreover, we found that overexpression of PRNP inhibited the proliferation, migration, and invasion ability of OC cells through in vitro experiments. PRNP was enriched to the Ras signaling pathway. PRNP expression was positively correlated with the infiltration of immune cells, such as mast cells, T effector memory cells, plasmacytoid DC cells, NK cells, and eosinophils. In addition, the association of PRNP with other immune signatures was also found. Conclusion. This study demonstrated for the first time showed that ferroptosis-related gene PRNP exerted a tumor suppressive role in OC and the aberrant expression and function of PRNP making it a potential novel biomarker for OC diagnosis, prognosis, and response to immunotherapies.
Evaluation of the Efficiency of Marine Science and Technology, Green Efficiency of the Marine Economy, and Comprehensive Efficiency in China
Authors: Wang Lingling , Li Mingquan
Published: September 2022

Based on the unexpected output network SBM-DEA model, this study calculates the efficiency of marine science and technology, green efficiency of the marine economy, and comprehensive efficiency in 11 coastal provinces and cities in China from 2006 to 2017. The influencing factors of two-stage efficiency and comprehensive efficiency are tested by a panel tobit model. The results show that (1) the marine comprehensive efficiency in China from 2006 to 2017 is low, with an average of 0.581. The comprehensive efficiency has not achieved DEA effectiveness, because the efficiency of the first stage is less than 1. The green efficiency of the second stage is generally better than that of the first stage; and (2) among the influencing factors, the development level of the marine economy has a significant positive effect on the second-stage efficiency and comprehensive efficiency. Finally, the author suggests optimizing the input-output efficiency of marine scientific and technological resources, accelerating the transformation of marine scientific and technological achievements, and continuing to promote the development of the marine economy to improve the comprehensive efficiency of the marine economy.
A Novel Hybrid Deep Learning Approach to Code Generation Aimed at Mitigating the Real-Time Network Attack in the Mobile Experiment Via GRU-LM and Word2vec
Authors: Cheon Minjong , Ha Hyodong , Lee Ook , Mun Changbae
Published: September 2022

As the use of devices in mobile environments increases, network attacks such as DDoS have a malicious attempt to flood the network's regular traffic to overload the target and surrounding infrastructure. This research proposed machine learning and deep learning approaches to dealing with DDoS attacks, and the results are described as follows. First, this research successfully detected DDoS attacks through an LGBM with a 100% accuracy score. Second, the proposed model (GRU-LM), which consists of a trained Word2vec layer with the Python dataset, is far more effective than the standard GRU model. Since Python is quite similar to English, language model-based GRU yields superior results. Various preprocessing steps were performed through the NLTK package, and each number was assigned to the tokenized one for constructing the GRU language model. The result reveals that the proposed model achieved an accuracy score of 87% for predicting the following words in the source code, while the rest achieved below 30% accuracy. This conclusion is significant because its relatively simple and light structure overcomes tradeoff problems between time and accuracy and is adaptable to the mobile setting. Discovering traffic patterns for the underlying data of DDOS assaults and retrieving them using statistical data analysis is the value of this research. Furthermore, since public cloud application vulnerability assaults are rising due to expanding cloud infrastructure, this finding could be used in such attacks.
Comparative Study on EDM Parameter Optimization for Adsorbed Si3N4–TiN using TOPSIS and GRA Coupled with TLBO Algorithm
Authors: Srinivasan V. P. , Sandeep Ch. , Shanthi C. , Bejaxhin A. Bovas Herbert , Anandan R. , Abisha Meji M.
Published: September 2022

Electrical discharge machining is a thermo-physical-based material removal technique. 25 combinations of process variables were formulated with the aid of Taguchi technique for EDM of adsorbed Si3N4–TiN. Machining variables like pulse current, pulse-on time, pulse-off time, dielectric pressure, and spark gap voltage varied, and impact of each variables on the performance metrics (MRR, EWR, SR, ROC, θ, CIR, and CYL) was assessed. MCDM strategies like grey relational analysis and TOPSIS are utilized to find out the ideal arrangement of machining parameters to achieve most acute productivity of the multitude of reactions. Likewise, metaheuristic algorithm in particular GRA combined with teaching-learning-based optimization algorithm is utilized for getting global optimized input factors. Important factors like pulse current, pulse-on time, and spark gap voltage characteristically affect the outputs. It is recognized that the pulse-on time and the pulse current are the most significant input factors than others. The ideal machining parameters in view of GRA and TOPSIS techniques for acquiring better output factors are I, 12 amps; PON, 7  μsec; POFF, 4  μsec; DP, 12 kg/cm2; and SV, 36 volts.
Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network
Authors: Chen Xihua , Yang Xing
Published: September 2022

The inspection of some substances usually includes two levels. One is the detection of the physical properties of the substance, which can be carried out through a series of physical detection methods and corresponding physical experiments. In the process of chemical detection, the color change after a chemical reaction is an extremely identifying optical property feature. In today’s increasingly mature Internet technology and related computer technology, the combination of this important identification chemical reaction and the former makes the chemical detection method visualized. The biggest difficulty in the application of this technology is to divide the color units produced by the chemical reaction in the contrast color sensor, which directly affects the identification process of the chemical reaction in the subsequent process. In order to better solve this problem, this paper will use a deep convolutional neural network to process the segmentation process of color units. And it is realized by image semantic processing of colorimetric sensor array and deep convolutional neural network processing of imaging. And through the experimental experiments based on convolutional neural network image segmentation processing, the results show that the efficiency of extracting features corresponding to different layers in the convolutional neural network is that the extraction efficiency of feature 1 and feature 2 is higher in the processing of 4 layers. They achieve 79.11%, 76.13%, 77.61%, 91.11% 92.31%, 91.05%, 91.03%, and 91.03%, respectively, and the extraction rate for feature 3 at layer 4 reaches 96.19%. It can be known from the above results that the segmented part of the image generated by the colorimetric sensor array processed by the deep convolutional neural network will be more conducive to the final color unit identification.
Machine Learning-Based Prediction Study of Hematoma Enlargement in Patients with Cerebral Hemorrhage
Authors: Tingting Zhao , Kailun Chen , Yiqing Lu
Published: September 2022

The enlarged hematoma is closely related to the poor neurological prognosis of patients with cerebral hemorrhage (intracerebral hemorrhage, ICH). Therefore, it is of great clinical significance to accurately predict whether ICH patients. In this study, we explored the predictive ability of a two-model machine learning, machine learning (ML) method to predict hematoma expansion. This method features nonenhanced CT (noncontrast computed tomography, NCCT) in ICH patients. The image information is combined with multiple clinical data for the prediction of hematoma enlargement. We retrospectively collected 140 ICH patients (including 58 patients with hematoma enlargement) from our hospital in 2021, and obtained a total of 5616 NCCT hematoma images (including 2635 images of hematoma enlargement) and 10 clinical data for each patient. The dual-model ML method used in this study contains 2 steps. The first step is to use a single-model predictor based on deep convolutional neural network (DCNN), which uses only the patients. The baseline NCCT images were performed for the prediction of hematoma enlargement. To select an appropriate DCNN model, we simultaneously compared the prediction performance of the three DCNN models, including ResNet34 (residual neural network with 34 layers), VGGNet (visual geometry group network), and GoogLeNet (Google inception network). In this step, we also explored whether the method of hematoma segmentation could improve the prediction outcome. The second step is to use the dual-model predictor based on multilayer perception (MLP), where the results of the single-model predictor in step 1 are combined with multiple clinical data of the patient to predict the final result. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each model, and were predicted using the subject operating characteristic curve (the receiver operating characteristic, ROC) and area under curve (AUC) to evaluate prediction performance. The experimental results show that the ML method proposed in this study can comprehensively analyze the patient NCCT image information and clinical data, which can achieve 86.5% accuracy and have relatively equal sensitivity and specificity. Therefore, this ML method can be used as a predictive tool to effectively identify people at high risk of hematoma expansion. This study can make an effective prediction of hematoma expansion in patients with clinical cerebral hemorrhage, which can better treat patients, improve the doctor-patient relationship, reduce complications, etc.
Marine Predators Algorithm Based on Adaptive Weight and Chaos Factor and Its Application
Authors: Liang Shujun , Pan Youmei , Zhang Huanlong , Zhang Jie , Wang Fengxian , Chen Zhiwu
Published: September 2022

Aiming at the problems of uneven distribution of initialized populations and unbalanced exploration and exploitation leading to slow convergence, low convergence accuracy, and easy to fall into local optimality of marine predators algorithm (MPA), a marine predators algorithm based on adaptive weight and chaos factor is proposed (ACMPA), the algorithm is applied to the traveling salesman problem (TSP), and the shortest path planning and research are carried out for the traveling salesman problem. Firstly, the improved adaptive weight strategy is used to balance the exploration and exploitation stage of the algorithm and improve the convergence accuracy of the algorithm. Secondly, the chaos factor is used to replace the random factor, and the ergodicity of the chaos factor is used to make it easier for predators to jump out of local optimization and enhance the optimization ability of the algorithm. Finally, 10 benchmark test functions, the CEC2015 test set, and the CEC2017 test set are used to evaluate the effectiveness of the ACMPA. The results show that, compared with the other four intelligent optimization algorithms, the improved ACMPA achieves better results in both mean and standard deviation, and the algorithm has a better effect on the shortest path problem.
Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies
Authors: Humayun Mamoona , Afsar Sadia , Almufareh Maram Fahaad , Jhanjhi N. Z. , AlSuwailem Mashayel
Published: September 2022

The expansion of technology in metropolitan centers draws people to cities, which causes excessive traffic on the roadways during peak hours. This exacerbated the traffic situation, resulting in a delay, a loss of resources, and a waste of time. Like any other metropolitan metropolis, Riyadh, Saudi Arabia, has everyday traffic congestion during business hours. The current traffic management has made many attempts to ease traffic congestion in cities; despite these measures, the problem has not been solved adequately. To handle this road congestion, there is a need to appropriately store the big data collected by traffic sensors and utilize it for efficient traffic management employing cutting-edge technology. This study provides an architecture for a smart traffic management system that uses modern technologies such as the Internet of Things (IoT), cloud computing, 5G, and big data to aid conventional traffic management systems and efficiently handle the stated problem. The proposed technique has the potential to reduce traffic congestion significantly. Our proposed solution encourages mobility by using roadside messaging agents to offer real-time traffic information on traffic congestion and unexpected traffic incidents. Citizens will save time by getting these early warning messages, particularly during peak hours. As part of the suggested method, each signal dashboard gets traffic information. A case study is used in the research to evaluate alternative solutions to traffic congestion. The case study results show that the proposed strategy outperforms the present options.
Computer Network Intrusion Anomaly Detection Based on Rough Fourier Fast Algorithm
Authors: Duan Xindong
Published: September 2022

Since there is a close relationship between network information security attack events and time complexity, it is necessary to count the degree of correlation between the current connection record and the connection record within a certain period of time before. Only in this way can the relationship between network connection data and network information security attack events be better reflected. In this paper, a rough Fourier fast algorithm based on rough set theory is proposed. Based on the characteristic attributes of the intrusion detection data set with the most value of character attributes as the data division basis, the computer network intrusion anomaly detection data set is intelligently divided into small data sets, so as to carry out attribute reduction. The network intrusion detection rule update experiment adopts the misuse detection method, extracts some samples from the KDD99 data set for training, obtains the computer network intrusion detection rules in the hierarchical decision table, and uses the incremental learning algorithm to update the rules, compared with the intrusion detection rules expressed by the decision table to test the feasibility and effectiveness of the rule update, and compared with the improved RSDB, RE-RFE algorithm, and KNN algorithm to evaluate the effect of the rough Fourier fast detection model applied to the problem of network intrusion anomaly detection. Using the attribute subset reduced by the rough Fourier algorithm to perform classification and modeling of computer network intrusion anomaly detection is significantly reduced, and the average time is reduced by 0.09 seconds, and this paper lays a good foundation for the application of network security intrusion detection algorithm.
Utilization of Vermiculite for Solving Hematite Sagging in Water-Based Drilling Fluids
Authors: Basfar Salem , Ahmed Ashraf , Elkatatny Salaheldin
Published: September 2022

The sagging phenomenon of solids, such as hematite, in drilling fluids is commonly experienced when drilling at elevated temperature conditions presenting operational and well control issues. Thus, we report herein the addition of vermiculite to water-based drilling fluid with the objective of improving fluid stability by hematite sagging mitigation. Different quantities of vermiculite clay (3–6 lb/bbl) were used and the sagging tendency was evaluated for the base and vermiculite-containing drilling fluids under static and dynamic conditions. From the sag measurements, the optimum vermiculite content was determined, and the influence on the key drilling fluid properties was studied by conducting the rheology, viscoelasticity, filtration, density, and alkalinity measurements. To simulate the practical elevated temperature, the tests were performed at temperature up to 250°F. The outcomes of this study revealed that the addition of vermiculite to the base drilling fluid significantly improved fluid stability. The content of 5 lb/bbl vermiculite was sufficient to mitigate the hematite sagging at elevated temperatures and drive the sag tendency factors to safe values. The ability of vermiculite to improve fluid stability was confirmed with the enhancement of rheological and viscoelastic properties, since the recommended vermiculite content resulted in a 24% reduction in plastic viscosity, 9% increment on yield point, and improved storage and loss modulus with better gelling structure. Also, the filtration properties were improved with 5 lb/bbl vermiculite, as the filtrated volume was reduced from 6.2 to 5 cm3 and formulated a preferred filter cake with less weight and thickness. In addition, the small amount of vermiculite did not change the drilling fluid weight, and the pH was slightly dropped but within the practical alkalinity range.
Matrix Metalloproteinases and Stress Hormones in Lung Cancer Progression
Authors: Gonzalez-Avila Georgina , Sommer Bettina , Garcia-Hernandez A. Armando , Ramos Carlos , Delgado Javier , Vazquez Lilia , Gonzalez Rosa A. , Sandoval Cuauhtemoc , Flores-Soto Edgar
Published: September 2022

Several matrix metalloproteinases (MMPs) and psychological stress are associated with poor cancer prognosis. The current work goal was to determine MMPs’ and stress hormones’ blood concentrations from lung adenocarcinoma (LAC) patients. Patients were divided into the following groups: tobacco smokers (TS), wood smoke-exposed (W), passive smokers (PS), TS exposed to wood smoke (TW), and patients with no recognizable risk factor (N). MMPs, tissue inhibitors of metalloproteinases (TIMPs), adrenaline, noradrenaline, and cortisol blood concentrations were measured by ELISA. Zymography and Western blot assays were performed to determine MMP-2 and MMP-9 active and latent forms. MMP-2, MMP-3, MMP-9, and TIMP-1 blood concentrations, and MMP-9 gelatinase activity were augmented, while MMP-12, MMP-14, and TIMP-2 were diminished in LAC patients. Cortisol was increased in LAC samples. Adrenaline concentrations were higher in W, TS, and TW, and noradrenaline was increased in W and N groups. Positive correlations were observed among cortisol and TIMP-1 (

r

s

=
0.392

) and TIMP-2 (

r

s

=
0.409

) in the W group and between noradrenaline and MMP-2 (

r

s

=
0.391

) in the N group. MMPs’ blood concentration increments can be considered as lung cancer progression markers. Although stress hormones were also augmented, only weak correlations were observed between them and MMPs and TIMPs.
Effects of Exercise or Mechanical Stimulation on Bone Development and Bone Repair
Authors: Song Lidan
Published: September 2022

The development and regeneration of the bone are tightly regulated by mechanical cues. Multiple cell types, including osteoblasts, osteocytes, osteoclasts, mesenchymal stem cells (MSCs), and recently found skeletal stem cells (SSCs), are responsible for efficient bone development and injury repair. The immune cells in the environment interact with bone cells to maintain homeostasis and facilitate bone regeneration. Investigation of the mechanism by which these cells sense and respond to mechanical signals in bone is fundamental for optimal clinical intervention in bone injury healing. We discuss the effects of exercise programs on fracture healing in animal models and human patients, which encouragingly suggest that carefully designed exercise prescriptions can improve the result of fracture healing during the remodeling phase. However, additional clinical tracing and date accumulation are still required for the pervasive application of exercise prescriptions to improve fracture healing.
Application of Embedded Intelligent Target Detection System in Basketball Movement Test
Authors: Zhang Chi , Wu Yuanyuan
Published: September 2022

With the development of society, basketball has been deeply loved by people all over the world. Meanwhile, basketball has become one of the most watched events in the world. In order to adapt to the development of basketball and make people better understand the laws of basketball, this paper aims to study the application of embedded intelligent object detection system in basketball sports test and use the embedded intelligent object detection system to separate basketball sports. Compared with the general computer processing system, the embedded system has great differences. It cannot realize the large-capacity storage function because there is no matching large-capacity medium. Strengthen the learning of basketball theoretical knowledge through learning rules. Firstly, the research background of basketball and the research significance of moving target detection are introduced, and the embedded Linus operating system is introduced, including hardware design and software design. Then, four moving target detection algorithms are proposed. Object detection, also called object extraction, is an image segmentation based on the geometric and statistical features of objects, and its accuracy and real-time performance are an important capability of the entire system. Experiments show that when using the YCrCb domain detection algorithm, the average time-consuming is 292 ms, and the conversion frame rate is about 3.5fps. The YCrCb domain detection algorithm has good detection effect and high efficiency.
A Review of Security and Privacy Concerns in the Internet of Things (IoT)
Authors: Aqeel Muhammad , Ali Fahad , Iqbal Muhammad Waseem , Rana Toqir A. , Arif Muhammad , Auwul Md. Rabiul
Published: September 2022

The recent two decades have witnessed tremendous growth in Internet of things (IoT) applications. There are more than 50 billion devices connected globally. IoT applications’ connectivity with the Internet persistently victimized them with a divergent range of traditional threats, including viruses, worms, malware, spyware, Trojans, malicious code injections, and backdoor attacks. Traditional threats provide essential services such as authentication, authorization, and accountability. Authentication and authorization are the process of verifying that a subject is bound to an object. Traditional authentication and authorization mechanisms use three different factors to identity a subject to verify if the subject has the right capability to access the object. Further, it is defined that a computer virus is a type of malware. Malware includes computer viruses, worms, Trojan horses, spyware, and ransomware. There is a high probability that IoT systems can get infected with a more sophisticated form of malware and high-frequency electromagnetic waves. Purpose oriented with distinct nature IoT devices is developed to work in a constrained environment. So there is a dire need to address these security issues because relying on existing traditional techniques is not good. Manufacturers and researchers must think about resolving these security and privacy issues. Most importantly, this study identifies the knowledge and research gap in this area. The primary objective of this systematic literature review is to discuss the divergent types of threats that target IoT systems. Most importantly, the goal is to understand the mode of action of these threats and develop the recovery mechanism to cover the damage. In this study, more than 170 research articles are systematically studied to understand security and privacy issues. Further, security threats and attacks are categorized on a single platform and provide an analysis to explain how and to what extent they damage the targeted IoT systems. This review paper encapsulates IoT security threats and categorizes and analyses them by implementing a comparative study. Moreover, the research work concludes to expand advanced technologies, e.g., blockchain, machine learning, and artificial intelligence, to guarantee security, privacy, and IoT systems.
SAR Antenna Pattern Measurement by Internal Calibration Method for GF-3 Satellite
Authors: Mi Jian , Sun Hongbing , Xing Ying , Zhao Xuhao , Li Jianxin
Published: September 2022

GF-3 is the first C-band multipolarization synthetic aperture radar (SAR) satellite in China. The SAR system is equipped with an active phased array antenna to electronically generate various antenna beams. The accuracy of SAR antenna patterns is of main importance for precise SAR image processing. The traditional method for antenna pattern measurement is the near-field method but it is unable to satisfy the demand for fast measuring in-orbit. In order to measure the SAR antenna pattern quickly with high accuracy, we propose the internal calibration method. The internal calibration uses calibration signals which are routed along the nominal signal path in SAR systems, thus monitoring the gain and phase variations for each T/R channel is possible. This paper will focus on this method, including its principle, operation steps, and results, compared to the near-field method. The internal calibration method provides a valuable solution for SAR antenna pattern measurement.
Evaluation of Pelvic Floor Dysfunction by Pelvic Floor Ultrasonography after Total Hysterectomy for Cervical Cancer
Authors: Liu Dan-dan , Xin Jing , Liu Wei , Zhang Yan-feng , Li Peishan
Published: September 2022

Objective. To study the value of pelvic floor ultrasonography in evaluating pelvic floor dysfunction (PFD) after total hysterectomy for cervical cancer. Methods. All the enrolled patients were given 4D pelvic floor ultrasound examination before and after surgery. The results of ultrasonic examination and the parameters of four-dimensional ultrasonic examination before and after surgery were analyzed, and the quality of life of the patients before and after surgery was evaluated. Results. Postoperatively, the posterior angle of bladder and urethra, the rotation angle of urethra, the decreased value of bladder neck, and the distance between bladder neck and pubic symphysis were (

122.60
±
9.53

)°, (

136.47
±
14.67

)°, (

58.90
±
18.19

)°, (

18.14
±
7.32

) mm, and (

2.76
±
0.46

) cm, significantly greater than the preoperative (

89.90
±
9.59

)°, (

107.30
±
9.96

)°, (

27.59
±
10.96

)°, (

13.27
±
5.69

) mm, and (

2.24
±
0.21

) cm (

P
<
0.05

). Postoperative detrusor muscle thickness, bladder neck movement, residual urine volume, and bladder rotation angle (

4.48
±
0.82

) mm, (

0.64
±
0.17

) cm, (

12.82
±
2.69

) ml, (

12.11
±
2.43

)° were significantly higher than those of preoperative (

3.70
±
0.64

) mm, (

0.43
±
0.18

) cm, (

4.83
±
1.07

) ml, (

4.30
−
1.19

)° (

P
<
0.05

). The scores of emotional function, psychological function, social function, and physiological function were (

2.35
±
0.75

) points, (

2.45
±
0.66

) points, (

2.30
±
0.77

) points, and (

2.19
±
0.71

) points, significantly higher than those of (

1.01
±
0.50

) points, (

1.25
±
0.54

) points, and (

1.00
±
0.57

) points before surgery, (

1.05
±
0.46

) (

P
<
0.05

). Conclusions. The application of pelvic floor ultrasonography to detect pelvic floor dysfunction after total hysterectomy can clearly display the anatomical structure of the pelvic floor, which is conducive to disease prevention and treatment. Four-dimensional pelvic floor ultrasound can clearly show the postoperative pelvic floor function, which is worthy of clinical promotion and reference.
[Retracted] Evaluation of Multimedia Courseware‐Assisted Teaching Effect of Medical Images Based on the Deep Learning Algorithm
Authors: Chen Yu-Na , Zhang Xuesen
Published: September 2022

In order to improve the dynamic evaluation ability of medical image multimedia courseware‐assisted teaching effect, the evaluation of medical image multimedia courseware‐assisted teaching effect based on a deep learning algorithm is proposed. The statistical data analysis model of medical image multimedia courseware‐assisted teaching effect is established to estimate its utilization rate and scale parameters. Based on the prediction of spatial attribute parameters, the classification big data mining model of medical image multimedia courseware‐assisted teaching is constructed by using the deep learning algorithm, mining association rules and frequent item sets that can dynamically reflect the quality of medical image multimedia courseware‐assisted teaching, and extracting the statistical feature of the dataset of constraint indicators of medical image multimedia courseware‐assisted teaching effect to improve the teaching quality of medical imaging course. The simulation results show that this method has a better precision delivery effect, higher dynamic matching degree of teaching evaluation parameters, more than 90% reliability, and better clustering of statistical eigenvalues.