bluuebunny/crossref_metadata_2025 huggingface dataset:
URM: A Unified RAM Management Scheme for NAND Flash Storage Devices
Authors: Li A. Xiaochang , Chen B. Jichen , Zhai C. Zhengjun , Feng D. Mingchen , Ye E. Xin
Published: June 2022

In NAND flash storage devices, the random access memory (RAM) is composed of a data buffer and mapping cache that play critical roles in storage performance. Furthermore, as the capacity growth rate of RAM chips lags far behind that of flash memory chips, determining how to take advantage of precious RAM is still a crucial issue. However, most existing buffer management studies on storage devices report performance degradation since these devices cannot refine reference regularities such as sequential, hot, or looping data patterns. In addition, most of these studies focus only on separately managing the data buffer or mapping cache. Compared with the existing buffer/cache management schemes (BMSs), we propose a unified RAM management (URM) scheme for not only the mapping cache but also the data buffer in NAND flash storage devices. URM compresses the mapping table to save memory space, and the remaining dynamic RAM space is used for the data buffer. For the data buffer part, we utilize the program counter‐technique in the host layer that provides automatic pattern recognition for different applications, in contrast to existing BMSs. The program counter‐technique in our design is able to distinguish four patterns. According to these patterns, the data buffer is divided into four size‐adjustable zones. Therefore, our approach is linked to multimodal data and used in a data‐intensive system. In particular, in URM, we use a multivariate classification to predict prefetching length in mapping buffer management. Our multivariate classification is transformed into multiple binary classifications (logistic regressions). Finally, we extensively evaluate URM using various realistic workloads, and the experimental results show that, compared with three data buffer management schemes, CFLRU, BPLRU, and VBBMS, URM can improve the hit ratio of data buffer and save response time by an average to 32% and 18%, respectively.
A Risk Feature Recognition Method of Cross-Border Financial Derivatives’ Transaction Based on Fuzzy Support Vector Machine
Authors: Xie ZhiShan
Published: June 2022

The traditional identification methods of transaction risk characteristics mostly use the amount of profit and cost to complete the risk assessment. The conflict between the two will lead to low stability. Therefore, the identification method of transaction risk characteristics of transnational financial derivatives is proposed. The fuzzy support vector machine method is used to identify the risk characteristics of cross-border financial derivatives transactions and compensate the errors in the identification process. The possibility index, severity index, and financial risk index of cross-border financial derivatives transaction risk are calculated, and the financial risks are screened and ranked. The distance from risk element to positive ideal solution and negative ideal solution is defined, and the joint identification model of transaction risk of transnational financial derivatives is constructed to realize the identification of transaction risk characteristics. The experimental results show that the designed method has good recognition effect and high recognition performance.
Life Prediction Model of Wooden Structure Based on Artificial Intelligence Algorithm
Authors: Cao Ningning
Published: June 2022

The ancient wooden structure in China is a precious architectural cultural heritage in history, which has high cultural and artistic value. However, due to the influence of its own material structure and the long-term preservation and maintenance process, due to long-term loads, earthquakes, fires, man-made damage, etc., ancient buildings have suffered more or less damage, which may cause sudden failure of the structure, which seriously affects the safety of the building structure. Therefore, the research on the prediction of the life of ancient wooden structures has guiding significance for sustainable development. This paper studies the life prediction of ancient buildings and introduces artificial intelligence algorithms. By comparing the old and new of the ancient building with the damage of the various structures of the ancient building, and using a variety of methods to find a more accurate method to predict the life of the ancient building, through various aspects of research and comparison, we have discovered the variation of wooden columns and beams. The coefficients are 22.97% and 22.54%, which affect the service life of wooden members. The residual strength ratios of the compressive design strength and flexural design strength of the new material and the old material are 60.42% and 26.67%, respectively.
Variations in 2D and 3D Models by a New Family of Subdivision Schemes and Algorithms for its Analysis
Authors: Hameed Rabia , Mustafa Ghulam , Majeed Rizwan
Published: June 2022

A new family of combined subdivision schemes with one tension parameter is proposed by the interpolatory and approximating subdivision schemes. The displacement vectors between the points of interpolatory and approximating subdivision schemes provide the flexibility in designing the limit curves and surfaces. Therefore, the limit curves generated by the proposed subdivision schemes variate in between or around the approximating and interpolatory curves. We also design few analytical algorithms to study the properties of the proposed schemes theoretically. The efficiency of these algorithms is analyzed by calculating their time complexity. The graphical representations and graphical properties of the proposed schemes are also analyzed.
Identification of Prognostic Biomarkers of Glioblastoma Based on Multidatabase Integration and Its Correlation with Immune-Infiltration Cells
Authors: Ding Wencong , Zhou Xian , Jiang Guoqiang , Xu Weiwei , Long Songkai , Xiao Fan , Liao Yongshi , Liu Jia
Published: June 2022

Background. Glioblastoma (GBM) is the most malignant of all known intracranial tumors; meanwhile, most patients have a poor prognosis. In order to improve the poor prognosis of GBM patients as much as possible, it is specifically significant to identify biomarkers related to the gene diagnosis and gene therapy. Methods. In this study, a total of 343 GBM specimens and 259 nontumor specimens were collected from four Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database; then, we analyzed the differentially expressed genes (DEGs) from the above data. Through Venn diagram analysis, 54 common upregulated DEGs and 22 common downregulated DEGs were triumphantly recognized. Results. On the basis of the degree of formation communication in protein-protein interaction network (PPIN), the 10 upregulated central genes were ranked, incorporating LOX, IGFBP3, CD44, TIMP1, FN1, VEGFA, POSTN, COL1A1, COL1A2, and COL3A1. By combining the expression levels and the clinical features of GBM, we found that four hub genes (TIMP1, FN1, POSTN, and LOX) were significantly upregulated and related to poor prognosis of GBM. Meanwhile, univariate and multivariate Cox regression analysis suggested that TIMP1 could be one of the independent prognostic factors for GBM patients. Furthermore, TIMP1 was particularly correlated with the immune marker of macrophage M1, macrophage M2, neutrophils, tumor-associated macrophage, and Tregs. We then analyzed the role of TIMP1 in GBM cancer cell lines by relevant experiments, which indicated that TIMP1 knockdown resulted in the decreased cell proliferation, migration, and invasion. Conclusions. Taken together, these findings demonstrated that TIMP1 might be a new biomarker to determine prognosis and immune infiltration of GBM patients.
Digital Movie Recommendation Algorithm Based on Big Data Platform
Authors: Miao Guojian , Gao Yin , Zhu Zhenshen
Published: June 2022

How to associate films with users among various film data and help users get useful information is a big problem we face. The recommendation system aims to provide users with accurate project recommendations, which can effectively solve the problem of information explosion caused by a large amount of data. Traditional recommendation systems are widely used in movie shopping. Aiming at this problem, this paper designs and develops a collaborative filtering recommendation algorithm based on big data platform. Firstly, the depth is deeper than the traditional automatic coding network, and the new activation function is used to generate the depth feature vector. Secondly, the model can describe both linear and nonlinear features of movie data, which further improves the extraction ability of nonlinear features. Experimental results show that the proposed algorithm is effective and can bring better user experience and economic benefits to consumers.
The Study of Properties and Structure of Polylactide–Graphite Nanoplates Compositions
Authors: Rogovina Svetlana , Lomakin Sergei , Usachev Sergey , Gasymov Miraga , Kuznetsova Olga , Shilkina Natalya , Shevchenko Vitalii , Shapagin Aleksey , Prut Eduard , Berlin Aleksander
Published: June 2022

Composites of polylactide containing graphite nanoplates as a filler in the concentration range 1–20 wt% were prepared in methylene chloride using the sonication technique. The thermal characteristics and phase transitions were studied by DSC and TGA methods. The temperatures and heats of glass transition, crystallization, and melting were determined, and the degree of crystallinity during primary and secondary heating was calculated. It is shown that the introduction of graphite nanoplates leads to an increase in the elastic modulus and a decrease in the breaking stress and elongation at break. These changes are especially pronounced at 20% GNP content in the composition, when the corresponding mechanical parameters are characteristics of brittle polymer systems. The study of the electrical properties of the composites showed that the percolation threshold in these materials is close to 7 wt%, which is significantly lower than in the case of spherical particles of comparable density. The SEM study of the filled composites showed a system of pores, which were apparently formed during the evaporation of solvent in the process of their preparation. Diverse structures of PLA/GNP composites films after hot pressure were established by the SEM method.
Monitoring and Analysis of Venture Capital and Corporate Fraud Based on Deep Learning
Authors: Zhang Ruijun , Zheng Lina
Published: June 2022

With the continuous expansion of global investment institutions, the development of the investment industry is gradually accelerating, but the risks behind the investment are also constantly increasing. Using the data of A-share companies in China’s capital market from 2010 to 2019, this paper studies the impact of venture capital on corporate fraud. Empirical results show that venture capital holdings reduce the probability and frequency of corporate fraud. These findings remain robust after mitigating endogeneity using PSM, Heckman’s two-step, one-step approach, suggesting a causal relationship between venture capital holdings and fraud reduction. Further research shows that the way venture capital holdings reduce corporate fraud is to suppress corporate fraud by improving the company’s internal and external information environment. Furthermore, venture capital holdings play an important role in the governance of corporate disclosure fraud and operational fraud, but not in the governance of TMT fraud. In addition, the venture capital has better inhibitory effects on the supervision and governance of the fraud frequency of nonstate-owned enterprises compared with state-owned enterprises. The results of this research imply that venture capital shareholding plays an important role in preventing corporate fraud. This study contributes to the researches about the value-added role of venture capital and reveals the governance effect of venture capital on corporate fraud. Besides, it provides the theoretical evidence for capitals to better serve the real economy.
High-Performance Computing of 3D Printing Processing Accuracy considering Cylindrical Coordinate Slicing Algorithm
Authors: Huang Bin , Xie Wenjun
Published: June 2022

In view of the problems of low printing efficiency and low accuracy of 3D printers currently developed in the domestic market, based on fused deposition modeling technology, this paper constructs a processing data model with a cylindrical coordinate data structure by 3D printing and processing models according to the cylindrical coordinate slicing rules. The data model can be used to search in adjacent cylinders to obtain the cylindrical coordinate information of the printing model and use the depth-first traversal method of the slicing model to establish a cylindrical coordinate slicing function. Aiming at the problem of “pointcut” of the model cylinder in cylindrical coordinate slice, a high-performance calculation method of machining accuracy based on the cylindrical coordinate slice algorithm is proposed. This algorithm is used to perform 3D printing processing through high-performance computing of cylindrical coordinate slices. After obtaining the intersection points obtained according to the order of the cylindrical coordinate slices of the model, through the automatic generation of the section profile corresponding to the printed model in OpenGL software, the profile direction of the model section can be determined according to the first cylindrical coordinate slice data generated by cutting each contour. The software and hardware of the 3D printer control system are designed, and the actual model printing test is conducted at the same time. By debugging and testing each module of software and hardware, the system is guaranteed to run stably under scientific and reasonable design. Finally, the experimental analysis results show that the algorithm proposed in this paper can effectively reduce the topology time required for cylindrical coordinate slicing, and the operation is simple, stable, and reliable.
Personality Prediction with Hybrid Genetic Programming using Portable EEG Device
Authors: Bhardwaj Harshit , Tomar Pradeep , Sakalle Aditi , Sakalle Maneesha , Asthana Rishi , Bhardwaj Arpit , Ibrahim Wubshet
Published: June 2022

This work suggests a method to identify personality traits regarding the targeted film clips in real-time. Such film clips elicit feelings in people while capturing their brain impulses using the electroencephalogram (EEG) devices and examining personality traits. The Myers–Briggs Type Indicator (MBTI) paradigm for determining personality is employed in this study. The fast Fourier transform (FFT) approach is used for feature extraction, and we have used hybrid genetic programming (HGP) for EEG data classification. We used a single-channel NeuroSky MindWave 2 dry electrode unit to obtain the EEG data. In order to collect the data, thirty Hindi and English video clips were placed in a conventional database. Fifty people volunteered to participate in this study and willingly provided brain signals. Using this dataset, we have generated four two-class HGP classifiers (HGP1, HGP2, HGP3, and HGP4), one for each group of MBTI traits overall classification accuracy of the HGP classifier as 82.25% for 10-fold cross-validation partition.
The Approach of Induced Generalized Neutrosophic Cubic Shapley Choquet Integral Aggregation Operators via the CODAS Method to Solve Distance‐Based Multicriteria Decision‐Making Problems
Authors: Khan Majid , Gulistan Muhammad , Al-Shamiri Mohammed M.
Published: June 2022

This study aims to define a conjecture that can handle complex frames of work more efficiently that occurs in daily life problems. In decision‐making theory inter‐relation of criteria, weights and choice decision‐making method subject to the given circumstances which are an important component for appropriate decisions. For this, we define neutrosophic cubic Shapley–Choquet integral (NCSCI) measure; combinative distance‐based assessment selection (CODAS) is accomplished over NCSCI and is implemented over a numerical example of a company foreign investment model as an application in decision‐making (DM) theory. The neutrosophic cubic set (NCS) is a hybrid of the neutrosophic set (NS) and interval neutrosophic set (INS), which provides a better plate form to handle inconsistent and vague data more conveniently. The novel CODAS method is based on Shapley–Choquet integral and Minkowski distance which contain more information measures than usual criteria weights and distances. The weights of criteria are measured by Shapley–Choquet integral and distance is evaluated by Minkowski distance. The Choquet integral considers the interaction among the criteria, and Shapley considers the overall weight criteria. Motivated by these characteristics NCSCI, we defined two aggregation operators’ induced‐generalized neutrosophic cubic Shapley–Choquet integral arithmetic (IGNCSCIA) and operators’ induced‐generalized neutrosophic cubic Shapley–Choquet integral geometric (IGNCSCIG) operators. To find the distance between two NC values, Minkowski distance is defined to evaluate neutrosophic cubic combinative distance‐based assessment selection (NCCODAS). To examine the feasibility of the proposed method, an example of company investment in a foreign country is considered. To check, the validity of the method, the comparative analysis of the proposed method with other methods is conducted.
Investigation on Durability Behavior of Fiber Reinforced Concrete with Steel Slag/Bacteria beneath Diverse Exposure Conditions
Authors: Kumar M Madhan , Sivakumar Vidhya Lakshmi , Devi V Subathra , Nagabhooshanam N. , Thanappan Subash
Published: June 2022

One of society's most perplexing concerns is trash management. Among them is steel slag, which is obtained from steel mills and is used in the building industry as a partial substitution ingredient in concrete. To ensure that the concrete lasts the desired service life without deteriorating, bacteria (Bacillus subtilis) are introduced to ensure that the construction performs as planned. The research is focused on the M30 grade concrete mix specified in the Indian Standard Code. Concrete specimens containing fiber, steel slag, and bacteria are subjected to a variety of environmental conditions, including extreme, extremely severe, severe, moderate, and mild. The ultrasonic pulse velocity, sorptivity, water absorption, rapid chloride penetration, and acid resistance characteristics of the fiber-reinforced bacterial concrete are compared to those of regular concrete specimens.
Neural Network Technology-Based Optimization Framework of Financial and Management Accounting Model
Authors: Zeng Yan
Published: June 2022

Traditional financial accounting has gradually evolved into management accounting in order to adapt to changing times and developments. To avoid being obliterated by the times, accountants must gradually improve their professional and comprehensive abilities in order to create greater value for businesses in the AI (Artificial Intelligence) era. This article presents an AI-based financial management optimization design and proposes an AI-based accounts receivable management optimization framework based on the existing information system. A typical financial distress early-warning model is built using the BPNN (BP Neural Network) model, and the training samples of listed companies’ financial data are processed iteratively using the neural network algorithm to realize the visual modeling of the object-oriented neural network and learn the training samples. Finally, the network’s ability to provide early warning is put to the test. The results show that BPNN’s prediction accuracy is significantly higher than that of other types, especially after years of data, with prediction results exceeding 90%. The results show that the BPNN-based financial early-warning method is feasible.
[Retracted] Enterprise Digital Transformation and Stock Price Crash Risk: Evidences from China
Authors: Song Xiaotong
Published: June 2022

With unstoppable rise of digital economy globally, digital transformation has become an inevitable choice for enterprises to survive and develop; both the public and the government are paying increasing attention to digital transformation. At the same time, the Chinese government takes more serious attitude towards systemic financial risks, emphasizing the importance of controlling systemic risks such as abnormal stock price in many public occasions. In this context, enterprise digital transformation and stock price crash risk have gained unprecedented attention. It is of great value for both industry and the government to analyze the influence and mechanism of enterprise digital transformation (EDT) on stock price crash risks. This study measures the degree/level of EDT of Chinese A‐share publicly owned enterprises through document study and crawler technology, examining the influence and mechanism of digital transformation on companies’ stock price crash risk. Conclusions of this paper may provide a theoretical and empirical basis for understanding digital transformation's consequences in capital market and provide a reference for the government and regulatory authorities to formulate support and disclosure policies for digital transformation.
Efficacy and Side Effects of Irinotecan Combined with Nedaplatin versus Paclitaxel Combined with Cisplatin in Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer and Tumor Marker Analysis: Based on a Retrospective Analysis
Authors: Jiang Yu , Song Baozhi , Chen Zhiqin
Published: June 2022

Objective. A case-control study was adopted to investigate the efficacy and side effects of irinotecan combined with nedaplatin (NP) versus paclitaxel combined with cisplatin for locally advanced cervical cancer (CC) neoadjuvant chemotherapy (NACT) and to analyze the changes in tumor marker levels. Methods. A total of 96 patients with locally advanced CC who were treated from October 2019 to October 2021 were enrolled in our hospital as the research subjects, and their clinical data were collected for retrospective analysis and grouped according to their treatment regimens. Among them, 53 patients received paclitaxel combined with cisplatin as the control group, and the other 43 patients received irinotecan combined with NP as the observation group. The clinical effectiveness of neoadjuvant chemotherapy and alterations in tumor markers (CEA, AFP, CA125, and SCCA) were compared between the two groups. The incidence of common chemotherapy side effects was observed and compared between the two groups, including nausea and vomiting, abdominal pain and diarrhea, liver function impairment, bone marrow suppression, transient hyperglycemia, rash, ECG abnormalities, peripheral neurotoxicity, and muscle aches and pains. Results. The clinical efficiency of neoadjuvant chemotherapy was 97.67% in the observation group and 81.13% in the control group, with no statistically significant difference between the groups (

P
>
0.05

). There was no significant difference in CEA, AFP, and CA125 between the two groups before and after chemotherapy, but the decrease of SCCA before and after chemotherapy was statistically significant. There was no significant difference in the incidence of liver function damage, myelosuppression, abnormal ECG, and rash between the two groups (

P
>
0.05

). There are statistically significant differences in the incidence of nausea and vomiting, transient hyperglycemia, peripheral neurotoxicity, and muscle aches between the observation and control groups (

P
<
0.05

). The incidence of nausea and vomiting, transient hyperglycemia, peripheral neurotoxicity, and muscle aches was higher in the control group than in the observation group, with statistically significant differences (

P
<
0.05

). The difference in the incidence of diarrhea and abdominal pain between the observation group and the control group was statistically significant (

P
<
0.05

), and the incidence of diarrhea and abdominal pain in the observation group was higher than that in the control group. Conclusion. Irinotecan in combination with nedaplatin can be an effective neoadjuvant chemotherapy regimen for advanced localized cervical cancer, particularly in patients with combined diabetes.
Studies of Phytochemicals, Antioxidant, and Antibacterial Activities of Pinus gerardiana and Pinus roxburghii Seed Extracts
Authors: Bhardwaj Kanchan , Sharma Rohit , Cruz-Martins Natália , Valko Marian , Upadhyay Navneet Kumar , Kuča Kamil , Bhardwaj Prerna
Published: June 2022

Pine seeds are considered as nonwood forest products (NWFP) with regularly increasing market’s demand. They can be eaten in various ways such as roasted or raw. In addition, they are included in various traditional dishes like in cookies, sauces, candies, cakes, breads, and other bakery items and, moreover, for medicinal purposes. GC‐MS study is performed to analyze the phytochemical compounds present in the seed extracts of Pinus roxburghii (Chir) and Pinus gerardiana (Chilgoza). In total, 25 compounds were identified each in Chir and Chilgoza. In Chir seeds, abundantly present compounds were 2,4‐di‐tert‐butylphenol (16.6%), followed by ç‐Terpinene (9.9%) and cyclohexanol, 4‐ethenyl‐4‐methyl‐3‐(1‐methylethenyl)‐, (1à,3à,4á) (9.8%), whereas in Chilgoza seeds, the maximum amount of compound was 1‐hexyl‐1‐nitrocyclohexane (17.3%), followed by phenol, 2,6‐bis(1,1‐dimethylethyl) (15.4%), and heptadecane, 2‐methyl (8.4%). The total phenolic content of Chir seed sample was 1536 ± 4.35 (mg GAE/100 g), whereas in the Chilgoza seed extract was 642.66 ± 2.08 (mg GAE/100 g). The application of RP‐HPLC‐DAD system revealed that Chir and Chilgoza seeds have maximum quantity of catechin (15.77 ± 0.16 μg/mg and 17.49 ± 0.32 μg/mg, respectively). Both Chir and Chilgoza seed extracts exhibited significant antioxidant (radical scavenging) potential, through H2O2 (618.94 ± 21.45 μg/mL and 575.16 ± 19.88 μg/mL) and DPPH (552.60 ± 13.03 μg/mL and 429.15 ± 3.80 μg/mL) assays, respectively. Additionally, a well‐known antibacterial potential was also found in both plants’ dichloromethane extracts, with 64 to 256 μg/mL of minimum inhibitory concentrations. As a whole, result shows the importance of both plants as a naturally occurring phytochemical source with significant antibacterial and antioxidant activity.
A BP Neural Network Algorithm for Multimedia Data Monitoring of Air Particulate Matter
Authors: Zhang Chunyi
Published: June 2022

In order to study a BP neural network algorithm for air particulate matter data monitoring, firstly, the monitoring data collected by particle sensor using the light scattering method are proposed. Then, based on the improved BP neural network method, the mapping relationship between the actual measured value of the sensor, weather and other influencing factors, and the standard value of the monitoring station is established, and the calibration model of air particulate matter is realized. Finally, through theoretical analysis and experimental comparison, the results show that the model based on BP neural network algorithm has good accuracy and generalization ability in the evaluation of air particulate index, which makes it possible to scientifically and accurately refine the evaluation and management of urban air particulate index. The experimental results show that the air particle calibration model based on the light scattering method and improved BP neural network algorithm is practical and effective.
Adoption of Compound Echocardiography under Artificial Intelligence Algorithm in Fetal Congenial Heart Disease Screening during Gestation
Authors: Han Guowei , Jin Tianliang , Zhang Li , Guo Chen , Gui Hua , Na Risu , Wang Xuesong , Bai Haihua
Published: June 2022

This research was aimed at exploring the diagnostic and screening effect of composite echocardiography based on the artificial intelligence (AI) segmentation algorithm on fetal congenital heart disease (CHD) during pregnancy, so as to reduce the birth rate of newborns with CHD. A total of 204 fetuses with abnormal heart conditions were divided into group II, group C (optimized with the AI algorithm), and group W (not optimized with the AI algorithm). In addition, 9,453 fetuses with normal heart conditions were included in group I. The abnormal distribution of fetal heart and the difference of cardiac

Z

score between group II and group I were analyzed, and the diagnostic value of group C and group W for CHD was compared. The results showed that the segmentation details of the proposed algorithm were better than those of the convolutional neural network (CNN), and the Dice coefficient, precision, and recall values were higher than those of the CNN. In fetal CHD, the incidence of abnormal ultrasonic manifestations was ventricular septal defect (98/48.04%), abnormal right subclavian artery (29/14.22%), and persistent left superior vena cava (25/12.25%). The diagnostic sensitivity (75.0% vs. 51.5%), specificity (99.6% vs. 99.2%), accuracy (99.0% vs. 98.2%), negative predictive value (88.5% vs. 78.5%), and positive predictive value (99% vs. 57.7%) of echocardiography segmentation in group C were significantly higher than those in group W. To sum up, echocardiography segmented by the AI algorithm could obviously improve the diagnostic efficiency of fetal CHD during gestation. Cardiac ultrasound parameters of children with CHD changed greatly.
Value of MSCT plus MRI in the Detection of Colon Cancer
Authors: Zhang Jingni , Bao Yu
Published: June 2022

Colon cancer is a common digestive system malignancy with nonspecific early symptoms, which necessitates better early detection methods. The present study was conducted to assess the accuracy and clinical value of multislice spiral CT (MSCT) plus magnetic resonance imaging (MRI) for colon cancer. Between January 2019 and July 2020, 100 patients with pathologically confirmed colon cancer treated in Wuxi People’s Hospital were assessed for eligibility and recruited. All eligible patients received MRI and MSCT without any treatment prior to scanning. The accuracy of preoperative diagnosis and staging of colon cancer by MRI, MSCT, and MRI plus MSCT were analyzed using postoperative pathological results as the gold standard, and consistency analysis was performed. The receiver operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was obtained to analyze the preoperative diagnostic value. The accuracy of MRI, MSCT, and MRI plus MSCT was 98.00%, 96.00%, and 100% with good consistency (Kappa = 0.732, 0.703, and 0.756). The AUC of MRI, MSCT, and MRI plus MSCT was 0.889, 0.861, and 0.903, respectively. The preoperative diagnostic accuracy of MRI for colon cancer at T1, T2, T3, and T4 stages was 76.92%, 82.61%, 73.47%, and 86.67%, respectively, those of MSCT was 53.85%, 69.57%, 63.27%, and 40.00%, respectively, and those of MRI plus MSCT was 100.00%, 95.65%, 95.92%, and 86.67%, respectively. Consistency analysis yielded good consistency for the diagnosis of the T-staging of colon cancer (Kappa = 0.754, 0.731, 0.776). MSCT plus MRI yielded higher accuracy, specificity, and sensitivity in the detection of colon cancer versus the standalone MRI or MSCT, which demonstrated great potential in the early detection of colon cancer with a high clinical value.
A Reactive Power Compensated Control Scheme for Solar-Assisted EV Fast-Charging Applications
Authors: Mishra Anjanee K , Shukla Saurabh , Singh Bhim , Al Durra Ahmad
Published: June 2022

The main objective of this work is to develop an efficient reactive power compensated control technique for a fast-charging scheme for electric vehicle(s) (i.e., level-3 charging). The developed charging technique has the four-quadrant power flow operation with the simultaneous assurance of the compensated reactive power control. The developed charging infrastructure scheme involves a solar panel and 3-phase grid to charge the E-mobility. A DC-DC boost converter is used to achieve maximum power tracking (MPPT) of a solar PV array, and a 3-phase grid-tied bidirectional voltage source converter (VSC) is utilized to provide the bulk of power to charge the EV. The 3-phase VSC has multiple functionalities including grid side power quality (PQ) improvement with reactive power compensation, seamless flow of power from the grid to EV, and solar to grid or battery to grid (V2G) operation. This scheme also facilitates tariffs earned by discharging solar energy to the grid with additional benefits of reactive power compensation. An arrangement is also made to tackle grid failure conditions during battery discharging mode by connecting a load for ancillary purposes. The effectiveness of this charging scheme is first examined in MATLAB/Simulink environment and then validated on developed hardware.
Spatial Interaction Effect of Nonpoint Source Pollution and Economic Growth on Agriculture Using Kuznets Curve and Grey Correlation Analysis
Authors: Tian Jilin , Liu Yinong
Published: June 2022

This study used the environmental Kuznets curve to assess the link between regional growth in the economy and several types of agricultural nonpoint source pollution. To protect the environment in China, prevention of nonpoint source pollution and agricultural control are essential. In agriculture, nonpoint source pollution is mostly produced by improper usage of agricultural production components such as chemical fertilizers and plastic films. This paper aims to investigate the interacting effects of agricultural nonpoint source pollution and the economics of agricultural growth space to achieve synchronized growth of environmental atmosphere and agricultural economy. To evaluate agricultural nonpoint source pollution completely, the production coefficient technique and the equal standard pollution load approach are utilized. The Kuznets curve is used to verify the interaction impact between agricultural nonpoint sources and each person’s agricultural gross production value. Grey correlation analysis was utilized for further verification to increase the efficacy of verification of interaction effect between the two parameters. Experiments show that the proposed method has a good fitting effect and is easily implementable. It serves as an effective resource for future agricultural research and encourages the green development of agriculture.
Design of Teaching Quality Analysis and Management System for PE Courses Based on Data-Mining Algorithm
Authors: Li Sen , Yanrui Luo
Published: June 2022

Advances in network technology have led to extensive information technology construction work in all walks of life; universities, as a key component of national development, cannot be overlooked in this regard. In today’s universities, the Web-based integrated academic management information system is widely used, promoting higher education management system innovation and improving the management level of education departments and teaching management. The traditional management mode is incapable of locating “knowledge” in the mountains of student transcripts, and the original management mode must be improved. In business, finance, insurance, marketing, and other fields, digital exploration technology is widely used. This article describes the design approach for a data mining-based analysis and management system for PE course teaching quality, as well as the application of information technology and data mining technology in PE by combining actual PE teaching in schools, with the goal of realizing a data mining-based PE performance management system to serve PE teaching in schools and improve PE teaching quality. The results show that the time required to find frequent itemsets using a traditional algorithm running on a single machine, as well as the time required to scan the database several times for frequent itemset search in a distributed cluster of 20 computing nodes, is significantly longer than that required by the data mining algorithm. As a result, the proposed sports performance management system is functional, simple, and scalable, with each functional module operating independently and cooperatively, reflecting the concept of “high cohesion and low coupling.”
Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning
Authors: Abd Elazeem Abd Elazeem M. , El-Wahed Khalifa Hamiden Abd , Pamucar Dragan , Kacem Amina Hadj , Afifi W. A.
Published: June 2022

The purpose of this study is to achieve a novel and efficient method for treating the interval coefficient linear programming (ICLP) problems. The problem is used for modeling an uncertain environment that represents most real-life problems. Moreover, the optimal solution of the model represents a decision under uncertainty that has a risk of selecting the correct optimal solution that satisfies the optimality and the feasibility conditions. Therefore, a proposed algorithm is suggested for treating the ICLP problems depending on novel measures such as the optimality ratio, feasibility ratio, and the normalized risk factor. Depending upon these measures and the concept of possible scenarios, a novel and effective analysis of the problem is done. Unlike other algorithms, the proposed algorithm involves an important role for the decision-maker (DM) in defining a satisfied optimal solution by using a utility function and other required parameters. Numerical examples are used for comparing and illustrating the robustness of the proposed algorithm. Finally, applying the algorithm to treat a Solid Waste Management Planning is introduced.
Real-Time Controllable Optimization Algorithm for Correlated Big Data in Cloud Computing Environment
Authors: Li Rutao , Pu Zaiyi
Published: June 2022

The era of big data (BD) has arrived. How to train models to find correlations in data and help people make decisions has become a major research topic and direction. As an elastic and scalable distributed computing mode (refers to a whole consisting of multiple interconnected computers that cooperate to perform a common or different task in a set of system software environments, with minimal reliance on centralized control processes, data, and hardware), cloud computing can provide powerful computing and storage capabilities and has been widely used in BD query and difficult processing. This paper aims to study the algorithm in the environment of cloud computing. Different from the traditional research algorithms, the relational BD algorithm is controllable in real time. Moreover, it has been optimized and upgraded on the previous real-time controllable algorithm. Also, it performs serial and parallel simulation tests on the algorithm. When the optimal situation of the parallel algorithm is obtained, the test results show that the relevant mining time of the optimized algorithm is significantly shorter than the traditional data mining time under the same dataset. The traditional mining time is about 3.5 times the data mining time of this paper, and the running power consumption of the optimization algorithm is reduced to 20 W.
Correlation between Serum Levels of Vitamin A and Vitamin D with Disease Severity in Tic Disorder Children
Authors: Wang Helin , Yang Yali , Zhou Dandan , Bai Chengjie , Shi Minqiang
Published: June 2022

Purpose. To explore and analyze the correlation between serum vitamins A and D levels and disease severity in children with tic disorder. Methods. A total of 59 children with tic disorders treated in the Linping Hospital of Traditional Chinese Medicine from April 2018 to May 2021 were selected as the observation group. 55 healthy children with matched age and sex who underwent physical examination were selected as the control group. Children in the observation group were subdivided to the mild group (n = 35) and moderate-to-severe group (n = 24) according to the Yale comprehensive tic severity scale. Afterwards, they were grouped into the temporary group (n = 25), persistent group (n = 22), and Tourette’s syndrome group (n = 12) according to their clinical characteristics and course of disease. The severity and serum vitamins A and D levels were collected and compared, and then, Spearman correlation analysis was performed to analyze the correlation between the severity and serum vitamins A and D levels. Results. Compared with the control group, the serum vitamin A and D levels in the observation group were lower. Compared with the mild group, the serum vitamins A and D levels were lower in the moderate-to-severe group. Spearman correlation analysis revealed a significant negative correlation between disease severity and serum vitamins A and D levels. Regarding the serum D levels, the temporary group > the persistent group > Tourette’s syndrome group. There was no significant difference in serum vitamin A levels among the three groups. Conclusion. Vitamins A and D deficiency in children is strongly associated with tic disorders, and vitamins A and D demonstrate a negative correlation with the severity of tic disorders. Vitamin D is also linked to the clinical type of tic disorder.
Research on Digital Steganography and Image Synthesis Model Based on Improved Wavelet Neural Network
Authors: Li Xujie , Yao Rujing , Lee Jonghan
Published: June 2022

Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved.
The Application of Semantic Analysis Technology in the Analysis of Chinese and Korean Literature Mutual Translation
Authors: Zhao Yueqi
Published: June 2022

In order to improve the effect of the mutual translation of Chinese and Korean literature, this article combines the semantic analysis technology to analyze the mutual translation of Chinese and Korean literature. Moreover, this article explores the analysis of Chinese and Korean literature mutual translation through intelligent semantic analysis methods and analyzes the theoretical basis of densely connected networks based on the semantic analysis and verification methods of densely connected networks and multilayer attention. Finally, this article implements semantic matching verification through the densely connected BiLSTM structure and adds a multilayer attention mechanism to the network to better interact with sentences to obtain the semantic relationship of sentences. The simulation analysis shows that the Chinese and Korean literature mutual translation system based on semantic analysis proposed in this article has a good translation effect and can effectively promote Sino-Korean literature exchanges.
Knowledge about Obstetric Danger Signs and Related Factors in Reproductive-Age Women in the Southeast Zone of Tigray, 2021: A Cross-Sectional Study
Authors: Asfaha Berhane Teklay , Gebremariam Shewit Hailu , Gebremariam Gebremedhin Kinfe , Weldemariam Ataklti Gebretsadik
Published: June 2022

Background. Pregnancy complications are the major health problems among women in developing countries. Globally, around 295,000 women die from pregnancy-related causes annually and 86% of these maternal deaths happen in developing countries. Objective. To assess knowledge of obstetric danger signs among reproductive-age women living in southeastern zone of Tigray Region, Ethiopia, 2021. Methods. A community-based quantitative cross-sectional survey was undertaken in southeastern zone of Tigray. A multistage random sampling technique was implemented to select total participants of 410 reproductive-age women. Two districts were randomly selected, and from those districts, 12 kebeles were selected randomly, and the calculated sample size (410) was proportionally allocated to each selected kebel. The data were collected by using face-to-face interview with a structured questionnaire from January 20 to February 20/2021 after ensuring that all requirements of ethical considerations were fulfilled. The collected data were entered into EpiData version 4.2 and then exported to SPSS version 20 for analysis. Descriptive statistics with frequency, percentage, table and graph, and cross-tabulation were used for presentation of result. Bivariable and multivariable analyses were used to examine the association. Odds ratios with 95% confidence interval and

P

value < 0.05 were used to determine the statistical association. Result. Four hundred ten reproductive-age women participated in the study making a response rate of 100%. Leakage of fluid per vagina was the most commonly mentioned obstetric danger signs (61%). Overall, one hundred seventy-two (42%) had good knowledge on obstetric danger sign. Educational status of the mother (AOR (

95
%
CI
=
2.7

(1.189-6.24))), site of delivery (AOR (

95
%
CI
=
2.2

(1.6-3.432))), and having history of an ANC follow-up (AOR (

95
%
CI
=
2.4

(1.13-5.6))) were found to be independent predictors of knowledge of women about the obstetric danger sign. Conclusion and Recommendation. Educational status of the mother site of delivery and having history of an ANC follow-up were independently associated with knowledge of women about obstetric danger signs. Thus, provision of the Information, Education and Communication targeting women, family, and the general community on obstetric danger signs and associated factors was recommended.