bluuebunny/crossref_metadata_2025 huggingface dataset:
Dual Quaternion Based Close Proximity Operation for In-Orbit Assembly via Model Predictive Control
Authors: Sun Chuqi , Xiao Yan , Sun Zhaowei , Ye Dong
Published: November 2021

This paper studies the problem of guidance and control for autonomous in-orbit assembly. A six-degree-of-freedom (6-DOF) motion control for in-orbit assembly close proximity operation between a service satellite and a target satellite is addressed in detail. The dynamics based on dual quaternion are introduced to dispose the coupling effect between translation and rotation in a succinct frame, in which relevant perturbation and disturbance are involved. With the consideration of economical principle for fuel consume, a generic control system based on model predictive control (MPC) is then designed to generate a suboptimal control sequence for rendezvous trajectory considering actuator output saturation. The stability and robustness issues of the MPC-based control system are analyzed and proved. Numerical simulations are presented to demonstrate the effectiveness and robustness of the proposed control scheme, while additional comparisons for diverse horizons of the MPC are further conducted.
Human Falling Recognition Based on Movement Energy Expenditure Feature
Authors: Pan Daohua , Liu Hongwei
Published: November 2021

Falls in the elderly are a common phenomenon in daily life, which causes serious injuries and even death. Human activity recognition methods with wearable sensor signals as input have been proposed to improve the accuracy and automation of daily falling recognition. In order not to affect the normal life behavior of the elderly, to make full use of the functions provided by the smartphone, to reduce the inconvenience caused by wearing sensor devices, and to reduce the cost of monitoring systems, the accelerometer and gyroscope integrated inside the smartphone are employed to collect the behavioral data of the elderly in their daily lives, and the threshold analysis method is used to study the human falling behavior recognition. Based on this, a three-level threshold detection algorithm for human fall behavior recognition is proposed by introducing human movement energy expenditure as a new feature. The algorithm integrates the changes of human movement energy expenditure, combined acceleration, and body tilt angle in the process of falling, which alleviates the problem of misjudgment caused by using only the threshold information of acceleration or (and) angle change to discriminate falls and improves the recognition accuracy. The recognition accuracy of this algorithm is verified by experiments to reach 95.42%. The APP is also devised to realize the timely detection of fall behavior and send alarms automatically.
An Expert System for COVID‐19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques
Authors: Subramaniam Umashankar , Subashini M. Monica , Almakhles Dhafer , Karthick Alagar , Manoharan S.
Published: November 2021

The proposed method introduces algorithms for the preprocessing of normal, COVID‐19, and pneumonia X‐ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X‐ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X‐ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X‐ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X‐ray can improve the accuracy of results in COVID‐19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X‐ray image results in better accuracy in classification for all three classes (normal/COVID‐19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.
Do Environmental Regulations Improve Industrial Efficiency?
Authors: Li Peng , Shi Li-Li
Published: November 2021

This study was based on research on the impact of environmental regulation on industrial efficiency in 30 provinces from 2005 to 2017 in China. For the explained variables, the industrial efficiency of the DEA‐Malmquist method was utilized for the decomposition and measurement of overall factor productivity, and government environmental governance variables were added as instrumental variables for two‐stage least‐squares regression. In addition, environmental regulatory intensity and year were utilized as threshold variables for the threshold test. In the benchmark regression, environmental regulation harms regional industrial efficiency. However, according to the IV estimation of government environmental governance variables, environmental regulation has a positive effect on the transformation of regional industrial efficiency. The influence of environmental regulation on industrial efficiency will be first suppressed and then promoted with the gradual increase of regulatory intensity. Furthermore, there is significant spatial heterogeneity in the impact of environmental regulations.
A Classification Method of Network Ideological and Political Resources Using Improved SVM Algorithm
Authors: Wang WenXia
Published: November 2021

In order to improve the accuracy and efficiency of the classification of network ideological and political resources and promote the efficiency of ideological education, a research on the classification of network ideological and political resources based on the improved SVM algorithm is proposed. We analyze the characteristics and current situation of network ideological and political resources and conclude that the method elements are open and technical. The ontology elements are rich and shared, and the behavioral elements are autonomous and interactive. Three types of network ideological and political resources are proposed: the main resource, content resource, and means resource. The particle swarm algorithm is used to improve the SVM algorithm. In the process of constructing the SVM classifier, the fuzzy membership function is introduced, the classification problem of network ideological and political resources is converted into a secondary planning problem, and the accuracy of network ideological and political resources is finally realized. Simulation results show that the use of improved algorithms to classify network ideological and political resources can improve the accuracy and efficiency of network abnormal data classification.
Analysis of Data Encryption Algorithms for Telecommunication Network‐Computer Network Communication Security
Authors: He Yifeng , Ye Nan , Zhang Rui
Published: November 2021

Nowadays, the development of information technology can be described as extremely rapid, especially with the widespread use of the Internet; the security of communications in the network has become an important issue we are facing. The purpose of this article is to solve the problems in today’s network security data encryption algorithms. Starting from the computer network communication security data encryption algorithm, we discuss the effects of several different encryption methods on improving network security. The research results show that it is known that the application of link encryption algorithm in network communication security encryption algorithm can increase the security index by 25%, the node encryption algorithm can increase the security index by 35%, and the end‐to‐end encryption algorithm can make the cyber activity safety index to increase by 40%. Among them, RSA algorithm and DES algorithm are two very representative algorithms; they represent different encryption systems. From the perspective of network data link, there are three methods of encryption algorithm, namely, link encryption algorithm, node encryption algorithm, and end‐to‐end encryption algorithm.
[Retracted] Research on the Application of Visual Sensing Technology in Art Education
Authors: Mao Wenli , Zhang Bingyu
Published: November 2021

It is essential to have a new understanding of the development of visual sensing technology in digital image art at this stage, in order to make traditional art education have new professional ability teaching. Based on the current research results in related fields, a three‐dimensional (3D) image visual communication system based on digital image automatic reconstruction is proposed with two schemes as the premise. In scheme 1, the hardware part is divided into two modules. The hardware used by the analysis of the 3D image layer module is the HUJ‐23 3D image processor. The acquisition of a 3D image layer module uses the hardware of a realistic infrared camera. The software of the system consists of two parts: a 3D image computer expression module and a 3D image reconstruction module. A simulation platform is established. The test data of 3D image reconstruction accuracy and visual communication integrity of the designed system show that both of them show a good trend. In scheme 2, regarding digital image processing, the 3D image visual perception reconstruction is affected by the modeling conditions, and some images are incomplete and damaged. The depth camera and image processor that can be used in the visual communication technology are selected, and their internal parameters are modified to borrow them in the original system hardware. Gaussian filtering model combined with scale‐invariant feature transform (SIFT) feature point extraction algorithm is adopted to select image feature points. Previous system reconstruction technology is used to upgrade the 3D digital image, and the feature point detection equation is adopted to detect the accuracy of the upgraded results. Based on the above hardware and software research, the 3D digital image system based on visual communication is successfully upgraded. The test platform is established, and the test samples are selected. Unlike the previous systems, the 3D image reconstruction accuracy of the designed visual communication system can be as high as 98%; the upgraded system has better image integrity and stronger performance than the previous systems and achieves higher visual sensing technology. In art education, it can provide a new content perspective for digital image art teaching.
The New Solitary Solutions to the Time-Fractional Coupled Jaulent–Miodek Equation
Authors: Shen Guiping , Manafian Jalil , Zia Syed Maqsood , Huy Dinh Tran Ngoc , Le Trung-Hieu
Published: November 2021

Here, two applicable methods, namely, the

tan

θ
/
2

-expansion technique and modified

exp

−
θ

ξ

-expansion technique are being applied on the time-fractional coupled Jaulent–Miodek equation. Materials such as photovoltaic-photorefractive, polymer, and organic contain spatial solitons and optical nonlinearities, which can be identified by seeking from energy-dependent Schrödinger potential. Plentiful exact traveling wave solutions containing unknown values are constructed in the sense of trigonometric, hyperbolic, exponential, and rational functions. Different arbitrary constants acquired in the solutions help us to discuss the dynamical behavior of solutions. Moreover, the graphical representation of solutions is shown vigorously in order to visualize the behavior of the solutions acquired for the mentioned equation. We obtain some periodic, dark soliton, and singular-kink wave solutions which have considerably fortified the existing literature on the time-fractional coupled Jaulent–Miodek equation. Via three-dimensional plot, density plot, and two-dimensional plot by utilizing Maple software, the physical properties of these waves are explained very well.
An Image Preprocessing Model of Coal and Gangue in High Dust and Low Light Conditions Based on the Joint Enhancement Algorithm
Authors: Li Na , Gong Xingyu
Published: November 2021

The lighting facilities are affected due to conditions of coal mine in high dust pollution, which bring problems of dim, shadow, or reflection to coal and gangue images, and make it difficult to identify coal and gangue from background. To solve these problems, a preprocessing model for low‐quality images of coal and gangue is proposed based on a joint enhancement algorithm in this paper. Firstly, the characteristics of coal and gangue images are analyzed in detail, and the improvement ways are put forward. Secondly, the image preprocessing flow of coal and gangue is established based on local features. Finally, a joint image enhancement algorithm is proposed based on bilateral filtering. In experimental, K‐means clustering segmentation is used to compare the segmentation results of different preprocessing methods with information entropy and structural similarity. Through the simulation experiments for six scenes, the results show that the proposed preprocessing model can effectively reduce noise, improve overall brightness and contrast, and enhance image details. At the same time, it has a better segmentation effect. All of these can provide a better basis for target recognition.
A Case Report of Femoral Hematoma following Obturator Nerve Block
Authors: Moriyama Kiyoshi , Moriyama Kumi , Kohyama Tomoki , Watanabe Kunitaro , Chinzei Mieko , Yorozu Tomoko
Published: November 2021

Background. When an obturator nerve block (ONB) is performed, the conventional landmark method or ultrasound-guided method is used. The major complications of this block are hematoma, but there are very few reports of its complications. We encountered massive bleeding and a huge hematoma after ONB. Case Presentation. A 95-year-old female underwent transurethral resection of the bladder tumor. Induction of anesthesia was accomplished via spinal anesthesia and right ONB using the landmark method. Postoperatively, subcutaneous bleeding was detected in the lower right interior thigh. Concentrated red cell transfusion was conducted to address the anemia. There was no subsequent expansion of the hematoma. It resolved on postoperative day (POD) 53. The hematoma was deemed to be inadvertently introduced due to an obturator artery puncture during the obturator nerve block. Conclusions. Close attention is necessary to avoid advancing the needle too deep into the obturator during obturator nerve block.
Community Detection Algorithm Based on Intelligent Calculation of Complex Network Nodes
Authors: Tian Yanjia , Feng Xiang
Published: November 2021

With the explosive development of big data, information data mining technology has also been developed rapidly, and complex networks have become a hot research direction in data mining. In real life, many complex systems will use network nodes for intelligent detection. When many community detection algorithms are used, many problems have arisen, so they have to face improvement. The new detection algorithm CS-Cluster proposed in this paper is derived by using the dissimilarity of node proximity. Of course, the new algorithm proposed in this article is based on the IGC-CSM algorithm. It has made certain improvements, and CS-Cluster has been implemented in the four algorithms of IGC-CSM, SA-Cluster, W-Cluster, and S-Cluster. The result of comparing the density value on the entropy value of the Political Blogs data set, the DBLP data set, the Political Blogs data set, and the entropy value of the DBLP data set is shown. Finally, it is concluded that the CS-Cluster algorithm is the best in terms of the effect and quality of clustering, and the degree of difference in the subgraph structure of clustering.
Nuclear Morphological Characteristics in Breast Cancer: Correlation with Hormone Receptor and Human Epidermal Growth Factor Receptor 2
Authors: Li Jiayu , Zhou Yehan , Li Yunzhu , Liu Yang
Published: November 2021

Background. Hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) are the common diagnostic/prognostic markers in breast cancer. Few articles have recently reported the correlation between cytology and molecular subtypes. We combined nuclear morphological characteristics with HR and HER2 status to observe the relationship and provide ideas for machine learning. Methods. We reanalyzed fine-needle aspiration cytology samples and core-needle puncture histological specimens from 142 patients with invasive breast cancer between March 2019 and December 2019, and the findings were compared with the two groups (HR+/HER2- and HR-/HER2+) following nuclear cytomorphological features: nuclear/cytoplasmic ratio, difference of nuclear size, nuclear pleomorphism, chromatin feature, nuclear membrane and nucleoli, and Nottingham grading. Results. Two groups were significantly associated with the difference of nuclear size, nuclear pleomorphism, and nucleoli (

P
<
0.001

) and consistent with histological grading (

P
<
0.001

). Moreover, nucleolar characteristics of size and number had obviously statistical significance (

P
<
0.001

). Multiple micro-nucleoli were frequently seen in the HR+/HER2- group compared with the HR-/HER2+ group which mostly were observed centered medium-large nucleoli. We described four interesting nuclear morphologies in the experiment. Conclusions. There were significant differences in nuclear characteristics between two groups. HR and HER2 status not only might be predicted in cytological samples, but some specific nuclear morphological features might have potential value to help us understand molecular function and predict more information.
Lidocaine Alleviates Sepsis‐Induced Acute Lung Injury in Mice by Suppressing Tissue Factor and Matrix Metalloproteinase‐2/9
Authors: Zheng Binbin , Yang Hongbo , Zhang Jianan , Wang Xueli , Sun Hao , Hu Fan , Li Qian , Jiang Liping , Su Yue , Peng Qilin , Tang Yulin , Liu Wen-Tao , He Xueming , Fan Yixin , Zhu Xia
Published: November 2021

Acute lung injury (ALI) is one of the fatal symptoms of sepsis. However, there were no effective clinical treatments. TF accumulation‐induced fibrin deposit formations and coagulation abnormalities in pulmonary vessels contribute to the lethality of ALI. Suppressor of cytokine signaling 3 (SOCS3) acts as an endogenous negative regulator of the TLR4/TF pathway. We hypothesized that inducing SOCS3 expression using lidocaine to suppress the TLR4/TF pathway may alleviate ALI. Hematoxylin and eosin (H&E), B‐mode ultrasound, and flow cytometry were used to measure the pathological damage of mice. Gelatin zymography was used to measure matrix metalloproteinase‐2/9 (MMP‐2/9) activities. Western blot was used to assay the expression of protein levels. Here, we show that lidocaine could increase the survival rate of ALI mice and ameliorate the lung injury of ALI mice including reducing the edema, neutrophil infiltration, and pulmonary thrombosis formation and increasing blood flow velocity. Moreover, in vitro and in vivo, lidocaine could increase the expression of p‐AMPK and SOCS3 and subsequently decrease the expression of p‐ASK1, p‐p38, TF, and the activity of MMP‐2/9. Taken together, our study demonstrated that lidocaine could inhibit the TLR4/ASK1/TF pathway to alleviate ALI via activating AMPK‐SOCS3 axis.
Track-Before-Detect Procedures in AM Radio-Based Passive Radar
Authors: Li Yang , Ma Hong , Wu Yuntao , Cheng Li
Published: November 2021

In amplitude modulation radio-based passive radar, the track-before-detect (TBD) procedures are performed to process long time observation data. This work mainly focuses on the tracking of the time-Doppler and time-azimuth traces of multiple target under real scenarios such as low signal-to-noise ratio, hybrid clutter, severe breaking points, and intersecting traces. A new original approach to deal with the TBD problem of this work is developed. Two types of linear equations according to the simplified radio wave propagation model are formulated, which is the key point of the proposed method. We make the theoretical analysis about how the linear equations can be used to track multiple target, and how multiple target’s constant velocities and initial positions can be estimated, which is an additional parameter estimation capability of the proposed method. Both the simulated data and real experimental data are performed with the proposed method and some conventional TBD methods. Several comparisons of the results are given to verify the effectiveness of the proposed method.
Network Attack and Defense Modeling and System Security Analysis: A Novel Approach Using Stochastic Evolutionary Game Petri Net
Authors: Wu Zenan , Tian Liqin , Zhang Yi , Wang Yan , Du Yuquan
Published: November 2021

At present, most network security analysis theory assumes that the players are completely rational. However, this is not consistent with the actual situation. In this paper, based on the effectiveness constraints on both sides with network attack and defense, with the help of stochastic Petri net and evolutionary game theory, the Petri net model of network attack and defense stochastic evolutionary game is reconstructed, the specific definition of the model is given, and the modeling method is given through the network connection relationship and attack and defense strategy set. Using this model, a quantitative analysis of network attack events is carried out to solve a series of indicators related to system security, namely, attack success rate, average attack time, and average system repair time. Finally, the proposed model and analysis method are applied to a classic network attack and defense process for experimental analysis, and the results verify the rationality and accuracy of the model and analysis method.
A Secure Recommendation System for Providing Context-Aware Physical Activity Classification for Users
Authors: Sengan Sudhakar , V Subramaniyaswamy , Jhaveri Rutvij H. , Varadarajan Vijayakumar , Setiawan Roy , Ravi Logesh
Published: November 2021

Advances in Wireless Body Area Networks, where embedded accelerometers, gyroscopes, and other sensors empower users to track real-time health data continuously, have made it easier for users to follow a healthier lifestyle. Various other apps have been intended to choose suitable physical exercise, depending on the current healthcare environment. A Mobile Application (Mobile App) based recommendation system is a technology that allows users to select an apt activity that might suit their preferences. However, most of the current applications require constant input from end-users and struggle to include those who have hectic schedules or are not dedicated and self-motivated. This research introduces a methodology that uses a “Selective Cluster Cube” recommender system to intelligently monitor and classify user behavior by collecting accelerometer data and synchronizing with its calendar. We suggest customized daily workouts based on historical user and related user habits, interests, physical status, and accessibility. Simultaneously, the exposure of customer requirements to the server is also a significant concern. Developing privacy-preserving protocols with basic cryptographic techniques (e.g., protected multi-party computing or HE) is a standard solution to address privacy issues, but in combination with state-of-the-art advising frameworks, it frequently provides far-reaching solutions. This paper proposes a novel framework, a Privacy Protected Recommendation System (PRIPRO), that employs HE for securing private user data. The PRIPRO model is compared for accuracy and robustness using standard evaluation parameters against three datasets.
Spatial Extent and Distribution of Ambient Airborne Particulate Matter (PM2.5) in Selected Land Use Sites in Nairobi, Kenya
Authors: Kiai Caroline , Kanali Christopher , Sang Joseph , Gatari Michael
Published: November 2021

Air pollution is one of the most important environmental and public health concerns worldwide. Urban air pollution has been increasing since the industrial revolution due to rapid industrialization, mushrooming of cities, and greater dependence on fossil fuels in urban centers. Particulate matter (PM) is considered to be one of the main aerosol pollutants that causes a significant adverse impact on human health. Low-cost air quality sensors have attracted attention recently to curb the lack of air quality data which is essential in assessing the health impacts of air pollutants and evaluating land use policies. This is mainly due to their lower cost in comparison to the conventional methods. The aim of this study was to assess the spatial extent and distribution of ambient airborne particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) in Nairobi City County. Seven sites were selected for monitoring based on the land use type: high- and low-density residential, industrial, agricultural, commercial, road transport, and forest reserve areas. Calibrated low-cost sensors and cyclone samplers were used to monitor PM2.5 concentration levels and gravimetric measurements for elemental composition of PM2.5, respectively. The sensor percentage accuracy for calibration ranged from 81.47% to 98.60%. The highest 24-hour average concentration of PM2.5 was observed in Viwandani, an industrial area (111.87 μg/m³), and the lowest concentration at Karura (21.25 μg/m³), a forested area. The results showed a daily variation in PM2.5 concentration levels with the peaks occurring in the morning and the evening due to variation in anthropogenic activities and the depth of the atmospheric boundary layer. Therefore, the study suggests that residents in different selected land use sites are exposed to varying levels of PM2.5 pollution on a regular basis, hence increasing the potential of causing long-term health effects.
Research on Surface Defect Detection of Rare‐Earth Magnetic Materials Based on Improved SSD
Authors: Zhang Bin , Fang Shuqi , Li Zhixi
Published: November 2021

In order to overcome the limitation of manual visual inspection of surface defects of rare‐earth magnetic materials and increase production efficiency of traditional rare‐earth enterprises, a detection method based on improved SSD (Single Shot Detector) is proposed. The SSD model is improved from two aspects for better performance in the detection of small defects. First of all, the multiscale receptive field module is embedded into the backbone network of the algorithm to improve the feature extraction ability of the model. Secondly, the interlayer feature fusion strategy of bidirectional feature pyramid in PANet (path aggregation network) is integrated into the model. In order to enhance the detection ability of the model, the high‐level semantic information is strengthened by an efficient channel attention mechanism. The detection speed of the improved SSD algorithm is 55FPS, and the mAP (mean Average Precision) is up to 83.65%, which is 3.41% higher than of the original SSD algorithm, and the ability to identify small defects is significantly improved.
Research on the Generation and Design Method of Residential Community Scheme Based on Improving Design
Authors: Li Chunyu , Wang Lei
Published: November 2021

Along with the urban renewal and development, the urban living environment has given rise to various problems that need to be solved. With an eye on the future development model of residential communities, an experimental preliminary design for the construction of architectural space, public space, and landscape space based on people’s actual needs is carried out in an attempt to alleviate the more urgent symbiotic relationship between people and urban environment. To this end, this paper proposes a planning and design generation framework for the constructed external spatial environment of building groups based on a recursive double-adversarial network model. Firstly, we extract the features of the constructed external spatial environment of the building group in depth and generate the expression feature map, which is used as a supervisory signal to generate an expression seed image of the constructed external spatial environment of the building group; then we use the generated seed image together with the constructed external spatial environment of the original target building group as the input to generate a feature-holding image as the output of the current frame, and the feature-holding image is also used as the input for the next. Finally, the seed image generation network and the feature-holding image generation network are recursively used to generate the next frame, and the video sequence of the expressions of the constructed external spatial environment of the building group with the same feature-holding expressions as the original input is recursively obtained several times. The experimental results on the building group database show that the proposed method can generate clear and natural video frames of the constructed external spatial environment of the building group, which can be gradually derived from the design of building units to the construction of the building group and penetrate into the planning and design of the external spatial environment in order to comprehensively improve the living environment of urban population and provide a design method and theoretical support for the design of future urban residential communities.
Analysis of the Effect of Intelligent Red Blood Cell Distribution Diagnosis Model on the Diagnosis and Treatment of Gastrointestinal Bleeding
Authors: Sun Xibin , Zhang Yuhong , Li Jiaxin , Zhang Bin , Jia Qian
Published: November 2021

In order to explore the role of red blood cell distribution width in the diagnosis and treatment of gastrointestinal bleeding, this paper applies map feature recognition technology to red blood cell distribution broadband and constructs an intelligent red blood cell distribution width diagnosis model. To extract the content-level features of the image safely and effectively, this paper introduces the mechanism of jitter quantization to extract the content-level features at the lowest frequency of the image. In addition, this article employs an experimental approach to investigate the function of red blood cell distribution width in the diagnosis and management of gastrointestinal bleeding in the elderly. Finally, this article establishes an experimental group and a control group and then performs a research study using real-life hospital case studies. According to the statistical findings, the red blood cell distribution width index may play a significant role in the diagnosis and management of gastrointestinal bleeding, particularly in the case of severe bleeding.
An Optimized Machine Learning and Big Data Approach to Crime Detection
Authors: Palanivinayagam Ashokkumar , Gopal Siva Shankar , Bhattacharya Sweta , Anumbe Noble , Ibeke Ebuka , Biamba Cresantus
Published: November 2021

Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning‐based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.
Shikonin Promotes Apoptosis and Attenuates Migration and Invasion of Human Esophageal Cancer Cells by Inhibiting Tumor Necrosis Factor Receptor-Associated Protein 1 Expression and AKT/mTOR Signaling Pathway
Authors: Huang Jingrong , Zhao Li , Gong Chengxian , Wang Yi , Qu Yinzong , Ji Chunyan , Yang Jianmei
Published: November 2021

The aim of this study was to investigate the anticancer effects of shikonin on esophageal cancer (EC) cells and explore the underlying molecular mechanism by identifying dysregulation in shikonin-induced tumor necrosis factor receptor-associated protein 1 (TRAP1) expression. The 3-(4, 5-dimethylthiazol-2-Yl)-2, 5-diphenyltetrazolium bromide assay and EDU assay were performed for cell viability determination. The reactive oxygen species level and mitochondrial membrane potential were evaluated using flow cytometry. The protein expression was detected using Western blot. In addition, cell migration and invasion were estimated. These results demonstrated that shikonin inhibited EC cell growth in a concentration-dependent manner and induced apoptosis through activation of the intracellular apoptotic signaling pathway. Moreover, TRAP1 downregulation promoted shikonin-induced reactive oxygen species release, whereas TRAP1 upregulation blocked it. Meanwhile, shikonin significantly promoted mitochondrial depolarization, accompanied by a large release of cytochrome C. Conversely, shikonin significantly decreased adenosine 5′-triphosphate release, demonstrating a significant intervention in the process of the glucose metabolism. In addition, not only shikonin but also short hairpin RNA (shRNA)-TRAP1 inhibited EC cell migration and invasion. shRNA-TRAP1 enhanced the inhibitory effect of shikonin on matrix metalloproteinase (MMP)2 and MMP9 expression. More interestingly, we demonstrated that shRNA-TRAP1 played a synergistic role in shikonin-mediated regulation of protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling. Collectively, shikonin promoted apoptosis and attenuated migration and invasion of EC cells by inhibiting TRAP1 expression and AKT/mTOR signaling, indicating that shikonin may be a new drug for treating EC.
A Proposal Mathematical Model for the Vaccine COVID-19 Distribution Network: A Case Study in Mexico
Authors: Soria-Arguello Isidro , Torres-Escobar Rafael , Pérez-Vicente Hugo Alexer , Perea-Rivera Tomas Guillermo
Published: November 2021

Coronavirus disease 2019 (COVID-19) has been the most recent disease, with millions of deaths worldwide. Fortunately, vaccines have been developed to immunize the population. However, the distribution of the vaccine is also a significant challenge. Generally, each country defines its strategy to bring it closer to the target population, considering the adequate transport for their conservation, depending on the type of vaccines defined. In this work, a mathematical model is proposed for the distribution of different COVID-19 vaccines in Mexico. Our results suggest an efficient strategy to meet demand in a given period.
Inhibition of miR-15a-5p Promotes the Chemoresistance to Pirarubicin in Hepatocellular Carcinoma via Targeting eIF4E
Authors: Zhang Ying , Tie Qingsong , Bao Zhiwei , Shao Zhi , Zhang Lan
Published: November 2021

Chemoresistance has become a primary hurdle in the therapeutic outcome of hepatocellular carcinoma. Substantial evidences have demonstrated that microRNAs (miRNAs) are closely associated with the chemoresistance of hepatocellular carcinoma (HCC). Our investigation is aimed at testifying the influence of microRNA-15a-5p (miR-15a-5p)/eukaryotic translation initiation factor 4E (eIF4E) on hepatocellular carcinoma resistance to pirarubicin (THP). In our study, miR-15a-5p expression was increased in THP-treated HepG2 cells. Downregulation of miR-15a-5p blocked cell growth and elevated cell apoptosis of HepG2 cells treated with THP. Moreover, eIF4E was verified as a direct target of miR-15a-5p by binding its 3

′

-UTR, which was confirmed by luciferase report experiment. Additionally, eIF4E was negatively associated with the miR-15a-5p expression in HepG2 cells. Mechanically, eIF4E was proven as a specific downstream of miR-15a-5p and mediated the effects of miR-15a-5p on cell viability and apoptosis of HepG2 cells treated with THP. These findings supported that miR-15a-5p facilitated THP resistance of hepatocellular carcinoma cells by modulating eIF4E, thus providing an experimental basis that miR-15a-5p might act as a novel diagnostic target in hepatocellular carcinoma resistance to THP.
Effect of Turbine Weight on the Seismic Response of a Wind Turbine‐Monopile System Located in Liquefied Multilayer Soil
Authors: Tirandazian Mehran , Nouri Gholamreza
Published: November 2021

The core objectives of sustainable development are to develop access to renewable, sustainable, reliable, and cost‐effective resources. Wind is an essential source of renewable energy, and monopile wind turbines are one method proposed for harnessing wind power. Offshore wind turbines can be vulnerable to earthquakes and liquefaction. This numerical study defined the effects of wind turbine weight on the seismic response of a wind turbine‐monopile system located in liquefied multilayered soil with layer thicknesses of 5, 10, 15, and 20 m using four far‐field records. OpenSees PL analysis indicated that if the liquefied sand had a lower density or a thickness of more than 10 m, then an increase in the earthquake acceleration beyond 0.4 g caused the pile to float like liquefied soil and to lose its vertical bearing capacity. Moreover, increasing the wind turbine power from 2 to 5 kW had no significant effect on the soil‐structure interaction response. As the earthquake acceleration increased, the bending moment of the pile‐column also increased as long as liquefaction did not occur and the pile‐column deformation remained rotational‐spatial in shape. As the acceleration and liquefaction increased and the pile began to float in response to its transverse motion, there was no significant difference in the pile‐column displacement along the length, but there was a decrease in the pile‐column bending moments. As this phenomenon increased and the pile continued to float, transformation of the pile increased the difference between the displacement of the pile‐column along its length and further increased the bending moments. These results were derived from multiple correlation analysis, the bending moment relations, and lateral displacement of the pile‐column of the wind turbine.
Time to Develop and Predictors for Incidence of Tuberculosis among Children Receiving Antiretroviral Therapy
Authors: Kebede Fassikaw , Kebede Tsehay , Kebede Birhanu , Abate Abebe , Jara Dube , Negese Belete , Shaweno Tamrat
Published: November 2021

Infection by the human immune deficiency virus (HIV) is the strongest risk factor for latent or new infection of tuberculosis (TB) through reduction of CD4 T-lymphocytes and cellular immune function. Almost one-third of deaths among people living with HIV are attributed to tuberculosis. Despite this evidence, in Ethiopia, there is a scarcity of information regarding the incidence of tuberculosis for children living with HIV. Thus, this study assessed time to develop and predictors for incidence of tuberculosis in children attending HIV/AIDS care in public hospitals: North West Ethiopia 2021. Methods. A facility-based retrospective cohort study was conducted among 421 seropositive children on antiretroviral therapy in two hospitals between January 1, 2011 and December 31, 2020. EPI-DATA version 3.2 and STATA/14 software were used for data entry and analysis, respectively. Tuberculosis-free survival time was estimated using the Kaplan-Meier survival curve. Bivariate and multivariable Cox regression model was fitted to identify predictors at a

P

value <0.05 within 95% CI. Results. In the final analysis, a total of 421 seropositive children were included, of whom, 64 (15.2%) developed tuberculosis at the time of follow-up. The mean (±SD) age of the children was

10.62
±
3.32

years, with a median (IQR) time to develop TB that was 23.5 (

IQR
=
±
19

) months. This study found that the incidence of tuberculosis was 5.9 (95% CI: 4.7; 7.6) per 100 person-years (PY) risk of observation. Cases at baseline not taking cotrimoxazol preventive therapy (CPT) (

AHR
=
2.5

; 95% CI, 1.4-4.7,

P
<
0.021

), being severely stunted (

AHR
=
2.9

: 95% CI, 1.2-7.8,

P
<
0.03

), and having low hemoglobin level (

AHR
=
4.0

; 95% CI, 2.1-8.1,

P
<
0.001

) were found to be predictors of tuberculosis. Conclusion. A higher rate of tuberculosis incidence was reported in our study as compared with previous studies in Ethiopia. Cases at baseline not taking cotrimoxazol preventive therapy (CPT), being severely stunted, and having low hemoglobin (≤10 mg/dl) levels were found to be at higher risk to developed TB incidence.
The Challenges of Ethical Review in Clinical Research of Traditional Chinese Medicine
Authors: Zhang Jie , Zhang Zong-Ming
Published: November 2021

The ethical review of TCM (traditional Chinese medicine) clinical research in China is highly consistent with that of Western medicine, but it lacks the characteristics and culture of TCM. Compared with modern medicine, TCM has its own characteristics, such as the theory of Yin-Yang and the five elements, the treatment of syndrome differentiation, and the compatibility of prescriptions. These characteristics determine the ethical particularity of TCM clinical research. This article discusses the challenges in the ethical review of TCM clinical research, such as scientific design, efficacy evaluation, risk assessment, informed consent, and placebo use. We propose opportunities and difficulties in the ethical review of TCM clinical research and provide some relevant suggestions.
Research on Local Topology Tracking of Power Grid Based on Graph Theory
Authors: Zhou Sujing
Published: November 2021

Power network topology identification, judgment, and tracking are the basic functional components of power system guarantee system and security management system. They can provide basic network structure data for other application software programs of power system. However, the traditional power grid topology method is not easy to implement and provides less relevant data that can be accurately analyzed, so that relevant personnel cannot fully understand the state of the power grid and give accurate commands, resulting in serious power accidents. Therefore, this paper proposes the research of power grid local topology tracking based on graph theory and constructs the power grid local topology tracking algorithm based on graph theory. The experimental results show that the local topology tracking algorithm based on graph theory can track the local topology of power grid quickly and effectively. Compared with the traditional method based on priority search, although the first power grid topology takes a relatively long time, it greatly improves the search and processing time after each time and has high efficiency in local topology. This shows that the local topology tracking algorithm based on graph theory needs less computation when carrying out the local topology of power grid. At the same time, the theory of power grid local topology tracking algorithm based on graph theory is relatively simple and easy to time, which is more practical than the traditional method.
Computed Tomography Image Based on Intelligent Segmentation Algorithm in the Diagnosis of Ovarian Tumor
Authors: Zhu Ling , He Yucheng , He Nan , Xiao Lanhua
Published: November 2021

This study was to explore the application of computed tomography (CT) images based on intelligent segmentation algorithms in the analysis of ovarian tumors, so as to provide a theoretical basis for clinical diagnosis of ovarian tumors. In this study, 100 patients with ovarian tumors were selected as the research objects and performed CT imaging examinations; a convolutional neural networks (CNN) algorithm model was constructed and applied to CT diagnostic image segmentation of patients with ovarian tumors, so as to analyze the effectiveness of the proposed algorithm for CT image segmentation. As a result, the image was segmented three times under the CNN algorithm, and the numbers of true positives (TP) were 50, 49, and 50, respectively; the numbers of false positives (FP) were 1, 2, and 1, respectively; the numbers of false negatives (FN) were 2, 3, and 2, respectively; and the numbers of true negatives (TN) were 47, 46, and 47, respectively. Thus, there was no great difference in the three measured values

P
≥
0.05

. The accuracy of the CNN algorithm was 0.97, 0.95, and 0.97, respectively, for the three times of segmentation; the precision was 0.98, 0.96, and 0.98, respectively; the recall was 0.96, 0.94, and 0.96, respectively. Thus, the accuracy, precision, and recall of the three measurements were not greatly different

P
≥
0.05

. In addition, the F1 values of three measurements were 0.97, 0.94, and 0.97, respectively, which all were close to 1, showing no statistically great difference

P
≥
0.05

. The segmentation accuracy, precision, and recall of the algorithm in this study were greatly greater than the SE-Res Block U-shaped CNN algorithm, and the density peak clustering algorithm, and the differences were statistically significant

P

<

0.05

. In short, the CNN algorithm showed high accuracy, precision, recall, and comprehensive evaluation values for CT image segmentation, which made the diagnosis of malignant or benign ovarian tumors more effective and provided reliable theoretical guidance for clinical analysis of ovarian tumors.