bluuebunny/crossref_metadata_2025 huggingface dataset:
The Survival Relationship between Preoperative Inflammation Markers and Patients with Special Pathological Types of Gastric Cancer
Authors: Han Ying , Zhu Ziyu , You Qi
Published: March 2022

Background. The preoperative PLR is closely associated with prognosis of gastric cancer. This aims to research whether the PLR could predict overall survival (OS) of gastric cancer (GC) patients with SRC component. Methods. The data were collected from Harbin Medical University Cancer Hospital between January 2001 and December 2013 in China. The patients were diagnosed with GC by pathologic examination, which contained SRC component in pathological organization. PLR is obtained from peripheral blood markers (platelets/lymphocytes). Results. There is a difference in OS between high PLR group and low group, which is verified by Kaplan–Meier analysis and log-rank tests (P < 0.001). Moreover, multivariate analysis prove PLR was independent prognostic factor for GC (HR = 1.384, 95% (CI): 1.048–1.828;P = 0.022). The preoperative PLR in stage I + II (P = 0.033), stage III (P < 0.001), SRC component lower than 50% (P < 0.001), SRC component equal to or higher than 50% (P = 0.044), and R0 resection (P < 0.001) GC are still effective. Conclusion. PLR is a simple, useful, and repeatable predictor of OS in gastric cancer of stages I–III with SRC component and may help clinicians identify patients with high risk and develop a more reasonable follow-up plan.
Construction of Personalized Learning Platform Based on Collaborative Filtering Algorithm
Authors: Zhang Qian
Published: March 2022

On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners’ urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners’ learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective.
Green Operations Management of Interbasin Water Transfer Project: An Extended Framework of Joint Pricing and Inventory Management
Authors: Wang Huimin , Xue Liuyu , Peng Jianhui , Liu Gaofeng , Chen Zhisong
Published: March 2022

To achieve sustainable operations management of an interbasin water transfer (IBWT) project, we proposed and analyzed decentralized and coordination decision models of water‐green‐degree (WGD) efforts for the IBWT project, based on the thinking of green supply chain within an extended framework of joint pricing and inventory management. Subsequently, detailed numerical and sensitivity analyses are carried out, which is followed by theoretical and practical insights. The following can be drawn from the research: (1) the IBWT project would achieve better operational performance through making WGD efforts and implementing green supply chain management. (2) Coordination strategy outperforms the decentralized one in terms of the operational performance in the management of IBWT green supply chain. (3) A revenue and cost sharing contract based on Nash bargaining could help promote coordination of the IBWT green supply chain.
A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model
Authors: Wang Hao , Li Ke , Xu Chi
Published: March 2022

The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve the recognition accuracy; the network architecture is adjusted to accelerate the training convergence speed and improve the recognition accuracy. The experimental results show that the A-ResNet model achieves a top-1 accuracy improvement of about 2% compared with the traditional ResNet network. Image recognition is one of the core technologies of computer vision, but its application in the field of tea is relatively small, and tea recognition still relies on sensory review methods. A total of 1,713 images of eight common green teas were collected, and the modeling effects of different network depths and different optimization algorithms were explored from the perspectives of predictive ability, convergence speed, model size, and recognition equilibrium of recognition models.
Location Allocation Collaborative Optimization of Emergency Temporary Distribution Center under Uncertainties
Authors: Ren Xiangyang , Tan Juan
Published: March 2022

Emergencies cause uncertainty in supply chain environment and risks of disruption. Mitigating such risks in emergency supply chain relies on efficient relief material distribution, and in the distribution logistics system, emergency facility location interacts with material allocation clearly. This paper aims to provide a collaborative optimization for the location allocation of temporary emergency distribution centers, with objectives of minimizing rescue time and maximizing demand satisfaction rate. A location allocation model of emergency logistics is formulated by considering uncertain demand and supply information at the response stage of disaster relief. The model is solved by a plant growth simulation algorithm. At last, the feasibility and effectiveness of the model and algorithm in practical application are verified by evaluating a case of COVID-19 prevention and control in Handan city. This paper provides references for decision makers to accomplish the location allocation of emergency facilities and material distribution when dealing with actual situations.
Progress of Statin Therapy in the Treatment of Idiopathic Pulmonary Fibrosis
Authors: Kou Leiya , Kou Pei , Luo Guangwei , Wei Shuang
Published: March 2022

Idiopathic pulmonary fibrosis (IPF) is a type of interstitial lung disease (ILD) characterized by the proliferation of fibroblasts and aberrant accumulation of extracellular matrix. These changes are accompanied by structural destruction of the lung tissue and the progressive decline of pulmonary function. In the past few decades, researchers have investigated the pathogenesis of IPF and sought a therapeutic approach for its treatment. Some studies have shown that the occurrence of IPF is related to pulmonary inflammatory injury; however, its specific etiology and pathogenesis remain unknown, and no effective treatment, with the exception of lung transplantation, has been identified yet. Several basic science and clinical studies in recent years have shown that statins, the traditional lipid-lowering drugs, exert significant antifibrotic effects, which can delay the progression of IPF and impairment of pulmonary function. This article is aimed at summarizing the current understanding of the pathogenesis of IPF, the progress of research on the use of statins in IPF models and clinical trials, and its main molecular targets.
Application of Regional Culture in Landscape Architecture Design under the Background of Data Fusion
Authors: Dai Yuxuan
Published: March 2022

With the rapid development of urban construction, the process of urban regeneration and new urban development is the process of fully integrating regional culture into garden design. From the current point of view, the landscape design of modern urban construction has become a problem that solves the necessity of thinking and detailed discussion. The purpose of this article is to study how to display the special signs of regional culture in the city and ultimately enable the symbiosis of regional culture and the surrounding environment. This article puts forward the importance of regional culture in landscape architectural design under the background of data fusion and how to let regional culture penetrate into landscape architectural design. Based on the algorithm of investigation method and data fusion, it can be learned from the research results of investigation method that human beings have never stopped exploring the regional culture. In today’s globalization, it highlights the characteristics of regional culture and advocates regional design and local culture. The combination of design is very necessary. In the experimental results of the data fusion algorithm, it can be known that when the evidence is the same, even if the weight adjustment is correct, the final conclusion is still close to the actual situation. It can be seen that, based on the data fusion algorithm, we can also know that regional cultural elements are becoming more and more important for landscape architectural design. In the experimental results of this article, in 2019, different age groups have the highest regional culture, which is as high as 38%, which is an increase of 12% over 2018. According to the data of the survey and research, it can be seen that the demand for regional culture for 15–30 years of age is between 30% and 50%, and the demand for regional culture for 50–75 years of age is between 53% and 70%. The demand for this age group is the highest. This shows that only by rationally using urban cultural resources, fully exploring the possibility of regionalization, and expanding the urban culture on the basis of the organic combination of the two can the regional cultural characteristics of the urban landscape be more and gradually clear. The spirit of the city is consolidated, thereby promoting the healthy development of the city. Only by digging the essence of regional culture can we create landscape design works with regional characteristics and significance.
Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques
Authors: Mushtaq Zaigham , Ramzan Muhammad Farhan , Ali Sikandar , Baseer Samad , Samad Ali , Husnain Mujtaba
Published: March 2022

Diabetes mellitus is a hyperglycemia-like chronic condition that is a troublesome disease. It is estimated that, according to the growing morbidity, by 2040, the world will cross 642 million diabetic patients. This means that each one of the ten adults will be diabetes-affected. Diabetes can also lead to other illnesses such as heart attacks, kidney damage, and even blindness. The prediction of diabetes in advance motivates us to develop a machine learning-based model. A dataset was obtained from the online repository for this work. The obtained dataset was imbalanced. An imbalanced dataset presents a challenge that is needed to be balanced for prediction using multiple machine learning like Tomek and SMOTE. These techniques remove necessary outliers that are incomplete in the provided dataset. These outliers are also managed using the IQR method. Additionally, this research employed a two-stage model selection methodology. In the first stage, logistic regression, Support Vector Machine, k-nearest neighbors, gradient boost, Naive Bayes, and Random Forests were applied to determine the efficiency of prediction based on patients’ preconditioning. At this stage, Random Forest was found to be the best with an accuracy of 80.7% after applying SMOTE oversampling technique to balance the dataset. In the second stage, three better-performing models were used by utilizing a voting algorithm. The results were encouraging, and the model obtained 82.0% accuracy with the default dataset and 81.7% accuracy with the balanced dataset. Naive Bayes Theorem, Gradient Boosting Classifier, and Random Forest were used as inputs to the voting algorithm.
On High-Dimensional Time-Variant Reliability Analysis with the Maximum Entropy Principle
Authors: Zhou Fuliang , Hou Yu , Nie Hong
Published: March 2022

The structural reliability analysis suffers from the curse of dimensionality if the associated limit state function involves a large number of inputs. This study develops a reliability analysis method that deals with high-dimensional inputs over time. The probability distribution of the structural response is reconstructed by the maximum entropy principle which is achieved by solving an optimization problem derived from the concept of relative entropy. The optimization problem is transformed into a convex one with respect to the orders of fractional moments and the Lagrange multipliers. Additionally, considering the associated computational issues, it is reformulated with side constraints on the parameters of the maximum entropy distribution. Then, a global optimization procedure is performed. The proposed method is successfully applied to the reliability analysis of a linear and a nonlinear structural system, which involves a large number of inputs deriving from the discretization of the input random processes.
Characteristic Analysis of Tai Ji Chuan Standard Action Based on and Perceptual IoT Network
Authors: Gala Hosig Jiri , Li Chunyan , Zheng Lu
Published: March 2022

Taijiquan is one of the first batches of national intangible cultural heritages, which has a history of more than 300 years. It has gradually gained global recognition for its unique healthcare and rehabilitation effects. Studies from countries around the world have shown that bad behaviors and lifestyles, such as smoking, alcoholism, overeating, lack of exercise, and irregular life patterns, have become one of the main causes of chronic, noncommunicable diseases. For example, a lack of exercise can lead to obesity, and obesity can cause various cardiovascular diseases, cerebrovascular diseases, and the occurrence of related chronic diseases. Taijiquan is traditional boxing with the core idea of Taiji and yin-yang dialectics in Chinese traditional Confucian and Taoist philosophy and pays attention to both internal and external cultivation and the combination of hardness and softness. It is the way of Taiji, the principle of nature, the body of Wushu, and the method of fitness. It is the crystallization of the wisdom of the Chinese nation. Taijiquan can improve the balance function of the human body. It not only has an obvious intervention effect on the prevention of falls in the elderly but also has a good rehabilitation training effect on some patients with motor dysfunction. It absorbed the essence of boxing and was organically combined with China’s traditional culture in the long-term development process, forming a philosophy boxing that combines health preservation, fitness, skill attack, self-cultivation, entertainment, and medical body. According to the demand analysis of the current situation of Taijiquan teaching, this paper analyzes the characteristics of Taijiquan standard movement based on perceptual IoT Network motion capture, and it explores the characteristics of Taijiquan cloud hand movement from the perspective of kinematics.
Association of Anthropometric Indices with Menstrual Abnormality among Nursing Students of Nepal: A Cross-Sectional Study
Authors: Amgain Kapil , Subedi Prativa , Yadav Gopal Kumar , Neupane Sujana , Khadka Sitaram , Sapkota Shubha Devi
Published: March 2022

Introduction. Obesity has been reported to be linked with menstrual abnormalities including abnormality in cycle length, duration, and period blood loss. However, which anthropometric parameter is a better marker of menstrual abnormality is yet unknown. This study aims to explore the association of BMI, waist-hip ratio (WHR), and waist-height ratio (WHtR) with menstrual abnormalities. Methods. This was a cross-sectional study conducted among 240 nursing students on two nursing campuses of Nepal. Demographic and menstrual characteristics related data were collected from the participants via the face-to-face interview technique followed by anthropometric measurements. Binary logistic regression was used to study the association of BMI, WHR, and WHtR with menstrual characteristics. Univariable and multivariable regression models were calculated at 95% confidence interval and aPvalue of 0.05 using a Statistical Package for Social Sciences, IBM SPSS® v21 (IBM, Armonk, New York). Results. Out of 240 participants, 52 participants (21.67%) were underweight (<18.5 kg/m2), and 47 participants (19.58%) were either overweight (≥23 kg/m2) or obese (≥25 kg/m2). Overweight and obese BMI was associated with abnormality in menstrual cycle length (AOR = 4.24; 95% CI = 1.77–10.17), duration of the menstrual period (AOR = 2.52; 95% CI = 1.09–5.81), and periodic menstrual blood loss (AOR = 9.04; 95% CI = 3.55–23.01). Increase in WHtR (>0.5) was associated with abnormal cycle length (AOR = 3.09; 95% CI = 1.09–8.80) and abnormal period duration (AOR = 3.05; 95% CI = 1.10–8.44). An increase in WHR (>0.8) was associated with abnormal cyclical menstrual blood loss (AOR = 4.93; 95% CI = 1.55–15.71). Conclusions. Obesity predisposes to menstrual irregularities. BMI is a better predictor of menstrual irregularity as the increase in BMI is associated with abnormality in menstrual cycle length, period duration, and blood loss. Increased WHR was associated with periodic blood loss only. Increased WHtR was associated with abnormal cycle length and period duration, but not menstrual blood loss.
Structural Characteristics of Regenerated Roof and Distribution Law of Overburden Porosity in Downward Mining of a Bifurcated Coal Seam
Authors: Hu Ru , Wu Jiwen , Zhai Xiaorong , Shi Wenbao , Huang Kai
Published: March 2022

During downward mining of a bifurcated coal seam, the roof of the lower coal seam is relatively broken and difficult to control due to the mining influence of the upper coal seam. Roof accidents occur frequently during mining of the lower coal seam, reducing mining efficiency. How to ensure safe and efficient mining of the lower coal seam is a significant issue. In this paper, overlying strata migration and fracture characteristics of the lower coal seam, the structure and stability of the regenerated roof, and porosity and permeability characteristics of the overlying strata under the mining influence of the upper coal seam are studied by using similar simulation tests. Results show that the overburden structure of the lower coal seam is altered due to the mining influence of the upper coal seam, and the regenerated roof of the lower coal seam is divided into three structural types from top to bottom, namely: intact rock mass+block fracture rock mass+loose rock mass (type I structure); intact rock mass+block fracture rock mass+loose rock mass+cataclastic rock mass (type II structure); and intact rock mass+block fracture rock mass+loose rock mass+cataclastic rock mass+slab-rent rock mass (type III structure). The stability of each type of rock mass structure is evaluated, and the stability of three types of rock mass structures is III > II > I. The overburden porosity and slurry permeability coefficient are relatively large at the cutting hole and stopping line. The porosity of the caving zone within 70 m of the cut hole and stopping line is greater than 5%, and the permeability coefficient is greater than 0.1 m/s. Based on differences in the surrounding rock porosity and permeability characteristics, the grouting difficulty of overburden is divided into three types of areas: extremely easy grouting areas, easy grouting areas, and difficult grouting areas. The results of this paper can provide reference for the stability evaluation of the regenerated roof and the selection of grouting treatment parameters for the broken roof under similar conditions.
svBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network
Authors: Wang Shaoqiang , Li Jie , Haque A K Alvi , Zhao Haiyong , Yang Liying , Yuan Xiguo
Published: March 2022

Structural variation (SV) is an important type of genome variation and confers susceptibility to human cancer diseases. Systematic analysis of SVs has become a crucial step for the exploration of mechanisms and precision diagnosis of cancers. The central point is how to accurately detect SV breakpoints by using next‐generation sequencing (NGS) data. Due to the cooccurrence of multiple types of SVs in the human genome and the intrinsic complexity of SVs, the discrimination of SV breakpoint types is a challenging task. In this paper, we propose a convolutional neural network‐ (CNN‐) based approach, called svBreak, for the detection and discrimination of common types of SV breakpoints. The principle of svBreak is that it extracts a set of SV‐related features for each genome site from the sequencing reads aligned to the reference genome and establishes a data matrix where each row represents one site and each column represents one feature and then adopts a CNN model to analyze such data matrix for the prediction of SV breakpoints. The performance of the proposed approach is tested via simulation studies and application to a real sequencing sample. The experimental results demonstrate the merits of the proposed approach when compared with existing methods. Thus, svBreak can be expected to be a supplementary approach in the field of SV analysis in human tumor genomes.
Waste-Derived Cellulosic Fibers and Their Applications
Authors: Samrot Antony V. , Ngaakudzwe Khosa Tariro , Rajalakshmi D. , Prakash P. , Suresh Kumar S. , Chandramohan M. , Alex Anand D. , Lilly Mercy J. , Simon Yishak , Saigeetha S.
Published: March 2022

The use of bio-based materials has become a focus of research nowadays. For the development of new generations of advanced resources, renewable and available resources must be combined with advanced technologies. Researchers have looked into biomass and waste cellulosic materials as sustainable sources for nano-crystalline cellulose extraction. Besides the different treatment methods suitable for various applications, this review aims to provide integrated details on the extraction methods and applications of cellulosic fibers and cellulose nanocrystals derived from wastes of different sources. There are numerous applications including building materials, electronics, furniture, automobiles, medical applications, sports goods, filtrations, water purification, and delivery systems of drugs which have been discussed.
Disastrous Mechanism of Water Burst by Karst Roof Channel in Rocky Desertification Mining Area in Southwest China
Authors: Suo Jie , Qin Qirong , Wang Wenqiang , Li Zhenhua , Huang Cunhan , Xu Youlin , Chen Zuguo
Published: March 2022

With the development of coal mining in rocky desertification mining area in Southwest China, water burst is becoming an important disaster in coal mine. In order to grasp the evolution characteristics of water gushing channels in coal mining in rocky desertification mining area, the 1402 working face in Xintian Coal Mine is taken as the research object, and the occurrence of aquifers on the roof of the working face is analyzed, and the water filling path of the aquifers is explored. Besides, the evolution characteristics of water passage in coal seam mining are comprehensively analyzed, by the methods of physical similarity simulation, numerical simulation, and microseismic monitoring. The results show that the key water resource is the atmospheric precipitation, which enters the mine through the original karst fissure and mining-induced fissure. With the continuous advance of working face, the fracture height of overburden increases gradually. Specifically, when the advancement distance of working face exceeds 135 m, the water-conducting cracks in the overlying strata develop to the bottom boundary of the Yulongshan limestone aquifer, and then, the mining-induced fracture and aquifer are conducted; when the working face advances 190 m, the overall overburden mining fissure is divided into fissure opening zone and fissure closed zone. Meanwhile, most of the microseismic events occur in the middle part of the karst roof, and the maximum height of microseismic event is 40 m away from the bottom boundary of the Yulongshan limestone, during the advancing process of the working face. When the mining fissure is connected with the original karst fissure, atmospheric precipitation enters the aquifer through the original karst fissure and enters the gob of working face through the mining fissure. The research results provide the references for prediction and prevention for the water burst disaster in rocky desertification mining area in Southwest China.
Application of Machine Learning and Internet of Things Techniques in Evaluation of English Teaching Effect in Colleges
Authors: Shang Wen-lan
Published: March 2022

College English classroom teaching evaluation is one of the key issues discussed by all schools at present. First of all, teachers and students are highly concerned about English learning in the compulsory course of English examination, but there are also many problems. The distance network teaching with computer technology as the main body develops rapidly, a large number of video and audio teaching resources network through the network transmission to present in front of learners, through the network transmission of video and audio aided, expand the audience of network teaching, is conducive to the realization of digitalization, information, lifelong, new education goals. Firstly, this article summarizes the problems of poor effect, uneven performance, and mismatch between evaluation and teaching in college English at present. Then, based on the previous works, the author designed a machine learning-based Internet of things technology and verifies its feasibility. The data of teaching experiments show that the writing performance of the students in the lower section of the experimental class is better than that of the students in the middle and high sections. Among them, significant progresses have been made in grammar, length, expression, and structure, which has optimized students’ preclass preview efficiency, autonomous learning motivation, quality of homework completion, and afterclass reflection behavior. Finally, the author summarized the shortcomings of this research and puts forward the prospect of relevant research, in order to provide reference value for the national college English blended teaching and promotes the efficient implementation and vigorous development of college English teaching. In the future, the evaluation index system under the pressure of performance can be further studied.
Association of AGTR1 A1166C and CYP2C9∗3 Gene Polymorphisms with the Antihypertensive Effect of Valsartan
Authors: Liu Yi , Kong Xiaomu , Jiang Yongwei , Zhao Meimei , Gao Peng , Cong Xiao , Cao Yongtong , Ma Liang
Published: March 2022

Background. The differences in the antihypertensive treatment with angiotensin type II receptor blockers (ARBs) may be attributed to polymorphisms in genes involving drug-targeted receptor and drug metabolism. The present study aimed to investigate whether the antihypertensive effect of the ARB drug valsartan was associated with angiotensin II type 1 receptor (AGTR1) gene polymorphism (A1166 C) and cytochrome P450 enzyme 2C9 (CYP2C9) gene polymorphism (CYP2C9∗3). Methods. 281 patients with hypertension who received valsartan monotherapy in the past month were included in this retrospective study. Polymerase chain reaction-melting curve analysis was performed to genotype the AGTR1 and CYP2C9 gene polymorphisms. Based on the systolic blood pressure (SBP) and diastolic blood pressure (DBP) at the time of visit, the patients were divided into well-controlled group (n = 144, SBP/DBP <140/90 mmHg) and poorly controlled group (n = 137, SBP/DBP ≥140/90 mmHg). Results. Older age, decreased history of drinking, a higher proportion of mild-to-moderate hypertension, lower alanine aminotransferase levels, and higher high-density lipoprotein cholesterol levels were observed in the well-controlled group than the poorly controlled group. Higher frequencies of the C allele and AC + CC genotype of AGTR1 A1166C were detected in the well-controlled than the poorly controlled patients (

P

 = 0.005 and

P

 = 0.006). After adjustment for demographic and environmental factors, the CC + AC genotype of AGTR1 A1166C was markedly linked to better hypertension control with valsartan treatment compared to the AA genotype (odds ratio: 2.836, 95% confidence interval: 1.199–6.705,

P

 = 0.018). No significant difference was observed in the allele or genotype distribution of CYP2C9∗3 polymorphism between well-controlled and poorly controlled patients. Conclusions. The current data suggested that the AGTR1 A1166 C polymorphism may be associated with the antihypertensive effect of valsartan, and carriers with AC and CC genotypes may have a better antihypertensive efficacy response to valsartan treatment.
Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China
Authors: Wang Jun , Rong Wei , Zhang Zhuo , Mei Dong
Published: March 2022

The bond market is an important part of China’s capital market. However, defaults have become frequent in the bond market in recent years, and consequently, the default risk of Chinese credit bonds has become increasingly prominent. Therefore, the assessment of default risk is particularly important. In this paper, we utilize 31 indicators at the macroeconomic level and the corporate microlevel for the prediction of bond defaults, and we conduct principal component analysis to extract 10 principal components from them. We use the XGBoost algorithm to analyze the importance of variables and assess the credit debt default risk based on the XGBoost prediction model through the calculation of evaluation indicators such as the area under the ROC curve (AUC), accuracy, precision, recall, and F1-score, in order to evaluate the classification prediction effect of the model. Finally, the grid search algorithm and

k

-fold cross-validation are used to optimize the parameters of the XGBoost model and determine the final classification prediction model. Existing research has focused on the selection of bond default risk prediction indicators and the application of XGBoost algorithm in default risk prediction. After optimization of the parameters, the optimized XGBoost algorithm is found to be more accurate than the original algorithm. The grid search and

k

-fold cross-validation algorithms are used to optimize the XGBoost model for predicting the default risk of credit bonds, resulting in higher accuracy of the proposed model. Our research results demonstrate that the optimized XGBoost model has a significantly improved prediction accuracy, compared to the original model, which is beneficial to improving the prediction effect for practical applications.
Earnings Management Behavior of Enterprise Managers Based on Evolutionary Game Theory
Authors: Wang Yang , Li Anqi , Liu Jiahuan
Published: March 2022

Today, earnings mismanagement in China’s enterprises has become a serious problem as managers conduct financial fraud by means of earnings management, hindering China’s overall economic development. Upon shareholders’ requirements and investors’ concerns, managers should disclose real financial information. The essay analyzes the revenue function generated by the manager and the shareholder through an evolutionary theory model where the managers team of the enterprise and shareholders are both game parties. After building the model, the essay utilizes Python to stimulate the theoretical model to analyze both parties’ behavior to explain the process of evolutionary game theory.
A Data Augmentation Method for Prohibited Item X-Ray Pseudocolor Images in X-Ray Security Inspection Based on Wasserstein Generative Adversarial Network and Spatial-and-Channel Attention Block
Authors: Liu Dongming , Liu Jianchang , Yuan Peixin , Yu Feng
Published: March 2022

For public security and crime prevention, the detection of prohibited items in X-ray security inspection based on deep learning has attracted widespread attention. However, the pseudocolor image dataset is scarce due to security, which brings an enormous challenge to the detection of prohibited items in X-ray security inspection. In this paper, a data augmentation method for prohibited item X-ray pseudocolor images in X-ray security inspection is proposed. Firstly, we design a framework of our method to achieve the dataset augmentation using the datasets with and without prohibited items. Secondly, in the framework, we design a spatial-and-channel attention block and a new base block to compose our X-ray Wasserstein generative adversarial network model with gradient penalty. The model directly generates high-quality dual-energy X-ray data instead of pseudocolor images. Thirdly, we design a composite strategy to composite the generated and real dual-energy X-ray data with background data into a new X-ray pseudocolor image, which can simulate the real overlapping relationship among items. Finally, two object detection models with and without our data augmentation method are applied to verify the effectiveness of our method. The experimental results demonstrate that our method can achieve the data augmentation for prohibited item X-ray pseudocolor images in X-ray security inspection effectively.
Effect of Fentanyl as an Adjuvant to Brachial Plexus Block for Upper Extremity Surgeries: A Systematic Review and Meta-Analysis of RCTs
Authors: Song Liangsong , Tan Shulian , Chen Qingmin , Li He
Published: March 2022

Objective. To assess if the addition of fentanyl to brachial plexus block has an impact on anesthetic outcomes and complication rates in patients undergoing upper extremity surgeries. Methods. We explore the PubMed, Embase, ScienceDirect, CENTRAL, and Google Scholar databases for all randomized controlled trials (RCTs) comparing adjuvant fentanyl with placebo/no drug for patients undergoing upper extremity surgery under brachial plexus block. Outcomes assessed were onset, duration of sensory and motor anesthesia, complications, and postoperative analgesia scores. Meta-analysis was conducted utilizing a random-effects model. The risk of bias was assessed using the Cochrane Collaboration’s risk of bias assessment tool 2. Certainty of evidence was assessed using GRADE. Subgroup analysis was conducted depending upon the approach of brachial plexus block and type of local anesthetic. Results. Twelve RCTs with 660 patients were included. Addition of fentanyl had no effect on onset of sensory anesthesia (11 studies; MD: 0.48; 95% CI: −1.81, 0.85; I2 = 96%;p=0.48) but significantly shortened onset of motor anesthesia (8 studies; MD: −2.36; 95% CI: −3.99, −0.74; I2 = 96%;p=0.48). Duration of sensory anesthesia (9 studies; MD: 82.81; 95% CI: 41.81, 123.81; I2 = 99%;p<0.0001) and motor anesthesia (7 studies; MD: 93.41; 95% CI: 42.35, 144.46; I2 = 99%;p=0.0003) was significantly increased with addition of fentanyl. The certainty of evidence-based on GRADE was deemed to be moderate for both onset and duration of anesthesia. The incidence of overall complications (nausea/vomiting and pruritis) was significantly higher in the fentanyl group (7 studies; OR: 2.14; 95% CI: 1.04, 4.40; I2 = 8%;p=0.04) but with low certainty of evidence. Conclusions. Adjuvant fentanyl with brachial plexus block improves the onset of motor anesthesia but not sensory anesthesia. The duration of both sensory and motor anesthesia is significantly prolonged with fentanyl by around 83–93 minutes. However, clinicians should be aware that complications such as nausea/vomiting and pruritis are increased twofold with the addition of the drug. Current evidence is limited risk of bias in the RCTs and high heterogeneity in the meta-analyses.
Structure of Soluble Dietary Fiber from Fresh Rice at the Medium-Milk Stage and Improvement of Insulin Resistance in HepG2 Cells
Authors: Zhang Aiwu , Li Ren , Wang Qi , Zhao Guoliang , Zhang Linyan , Jiang Peng , Liu Nian , Li Zhijiang
Published: March 2022

Fresh rice (Oryza sativa ssp. japonica) at the medium-milk stage is rich in soluble dietary fiber (SDF), leading to potential effects on type 2 diabetes (T2D). In this study, we analyzed each monosaccharide, the relative molecular weight, and the molecular linkages of fresh rice (grain, stem, husk, and leaf) SDF. The examined fresh rice leaf SDF consisted of 31.9% glucose, 35.7% galactose, 15.3% arabinose, 11.7% mannose, and a small amount of rhamnose and xylose. The molecular weight (

M

w

) was

232.5
±
19.1
×

10

4

 
u

, and the radius of gyration (

R

g

) was

298.8
±
25.4
 
nm

. The glycosidic bonds consisted mainly of Araf and Glc/Gal and included 1,3-Araf, 1,5-Araf, and 3,5-Araf glycosidic bonds. The effect of fresh rice leaf SDF on insulin resistance (IR) in HepG2 cells showed that it could significantly enhance glucose consumption (

P
<
0.05

). It also decreased the malondialdehyde (MDA) content (

P
<
0.05

) and increased the total superoxide dismutase (T-SOD) and glutathione peroxidase (GSH-Px) activities (

P
<
0.05

) in a dose-dependent manner. Therefore, fresh rice leaf SDF might be a good dietary supplement for treating T2D.
Rumdul (Sphaerocoryne affinis) Antioxidant Activity and Its Potential for Parkinson’s Disease Treatment
Authors: Nghi Ngo Binh Thao , Uyen Tran Thuc , Anh Huynh Man , Linh Dao My , Thao Dang Thi Phuong
Published: March 2022

Parkinson’s disease (PD) is an age-related neurodegenerative disorder characterized by progressive deterioration of motor function and loss of dopaminergic neurons in the substantia nigra. Although PD is more common in people over 60 years old, people with young-onset PD tend to increase recently. Up to now, there is no cure for PD; therapies mainly focus on reducing symptoms and improving patient quality of life. Thus, the requirement of exploring new medications is needed. There is a strong relationship between oxidative stress and PD. Therefore, antioxidant compounds have been considered as a novel therapy for PD. In this study, we indicated a new potential candidate for PD treatment, rumdul fruit (Sphaerocoryne affinis—a member of the Annonaceae family), due to evaluating its activities on the fly model of Parkinson. Our experimental results showed that rumdul fruit water extract (RFWE) has a strong antioxidant capacity with IC50 value in DPPH assay which was

85.62
±
1.05

 μg/mL. The use of RFWE at concentrations of 3, 6, and 12 mg/mL could strongly ameliorate the locomotor disabilities and dopaminergic neuron degeneration. Although the RFWE at high concentrations like 12 mg/mL and 18 mg/mL could induce some side effects on fly development and viability, our data strongly demonstrated that RFWE effectively rescued PD phenotypes on the fly model. Although component in the plant extract, as well as the molecular mechanism helping to recover the phenotype, has not been elucidated yet, the research contributed strong scientific evidence for further research on applying rumdul as a novel natural source for PD treatment.
Contemporary Urban Space Philosophy in China Using Lightweight Deep Learning Model-Under Ecological Ethics
Authors: Cheng Haiyan
Published: March 2022

It aims to improve the construction of ecological civilization and promote the common development of urban and ecology. Firstly, contemporary ecological ethics is explored, and its principles and characteristics are summarized. Then, the technique of convolutional neural network (CNN) image in a deep learning model is analyzed. Finally, deep convolutional neural networks (DCNN) are used to analyze and model the spatial characteristics of contemporary cities based on ecological ethics. According to the investigations, rural residential areas are more consistent with ecological ethics than urban residential areas when compared with the ecological characteristics of farmland and forest, and the highest ecological eigenvalues of the two areas are about 8 and 6. In the analysis of urban space, the maximum value of ecological eigenvalues of an airport is 9, and that of a stadium is 8. However, the scope of their construction that is consistent with ecological ethics is very small. Moreover, the eigenvalues of ecological ethics in the urban business circle of casinos are not only very low (the highest values are about 5 and 3), but also consistent with the construction norms of ecological ethics. The work of urban spatial philosophy is optimized based on the adoption of the DCNN model of deep learning in ecological ethics, which not only provides the reference for future ecological urban planning but also contributes to the common development of urban and ecology.
An EEG-Based Neuromarketing Approach for Analyzing the Preference of an Electric Car
Authors: Raiesdana Somayeh , Mousakhani Morteza
Published: March 2022

This study evaluates consumer preference from the perspective of neuroscience when a choice is made among a number of cars, one of which is an electric car. Consumer neuroscience contributes to a systematic understanding of the underlying information processing and cognitions involved in choosing or preferring a product. This study aims to evaluate whether neural measures, which were implicitly extracted from brain activities, can be reliable or consistent with self-reported measures such as preference or liking. In an EEG-based experiment, the participants viewed images of automobiles and their specifications. Emotional and attentional stimuli and the participants’ responses, in the form of decisions made, were meticulously distinguished and analyzed via signal processing techniques, statistical tests, and brain mapping tools. Long-range temporal correlations (LRTCs) were also calculated to investigate whether the preference of a product could affect the dynamic of neuronal fluctuations. Statistically significant spatiotemporal dynamical differences were then evaluated between those who select an electric car (which seemingly demands specific memory and long-term attention) and participants who choose other cars. The results showed increased PSD and central-parietal and central-frontal coherences at the alpha frequency band for those who selected the electric car. In addition, the findings showed the emergence of LRTCs or the ability of this group to integrate information over extended periods. Furthermore, the result of clustering subjects into two groups, using statistically significant discriminative EEG measures, was associated with the self-report data. The obtained results highlighted the promising role of intrinsically extracted measures on consumers’ buying behavior.
Research on Face Recognition Algorithm Based on Image Processing
Authors: Sun Yan , Ren Zhenyun , Zheng Wenxi
Published: March 2022

While network technology is convenient for our daily life, the problems that are exposed are also endless. The most important thing for everyone is information security. In order to improve the security level of network information and identify and detect faces, the method used in this paper has improved compared with the traditional AdaBoost method and skin color method. AdaBoost detection is performed on the image, which reduces the probability of false detection. The experiment compares the experimental results of the AdaBoost method, the skin color method and the skin color + AdaBoost method. All operations in the KPCA and KFDA algorithms are performed by the inner product kernel function defined in the original space, and no specific non-linear mapping function is involved.The full name of KPCA is kernel principal component analysis. The full name of KFDA is kernel Fisher discriminant analysis. Combining the zero-space method kernel discriminant analysis method improves the ability of discriminant analysis to extract non-linear features. Through the secondary extraction of PCA features, a better recognition result than the PCA method is obtained. This paper also proposes a zero-space based Fisher discriminant analysis method. Experiments show that the zero-space-based method makes full use of the useful discriminant information in the zero space of the intraclass dispersion matrix, which improves the accuracy of face recognition to some extent.If you choose the polynomial kernel function, when d = 0.8, KPCA has a higher recognition ability. When d = 2, the recognition rate of KFDA and zero space-based KFDA is the largest. For polynomial functions, in general, d = 2.
Design of Intelligent Rehabilitation Evaluation Scale for Stroke Patients Based on Genetic Algorithm and Extreme Learning Machine
Authors: Zhi Tongle , Meng Chengjie , Fu Linshan
Published: March 2022

The rehabilitation of stroke patients is a long-term process. To realize the automation and quantification of upper limb rehabilitation assessment of stroke patients, an automatic prediction model of rehabilitation evaluation scale was established by extreme learning machine (ELM) according to Fugl-Meyer motor function assessment (FMA). Four movements in the shoulder and elbow joints of FMA were selected. Two acceleration sensors fixed on the forearm and upper arm of the hemiplegic side were used to collect the motion data of 35 patients. After preprocessing and feature extraction, the feature selection was carried out based on genetic algorithm and ELM, and the single-action model and comprehensive prediction model were established, respectively. The results show that the model can accurately and automatically predict the shoulder and elbow score of FMA, and the root mean square error of prediction is 2.16. This method breaks through the limitations of subjectivity, time-consuming and dependence on rehabilitation doctors in the traditional evaluation. It can be easily used in the assessment of long-term rehabilitation.
Performance Enhancements of Fully Inkjet-Printing Technology for Antenna-in-Package and Substrate Integrated Waveguides
Authors: Chen Yen-Sheng , Huang Sheng-Xue
Published: March 2022

A fully inkjet-printing technology is applied to antenna-in-package (AiP) and substrate integrated waveguide (SIW) to enhance the performance of three components, including via holes, wire bonding, and flexible antenna arrays. First of all, earlier studies utilize shorting pins for the conductive pathway in high-density AiP and SIW, but this requires an additional procedure to plate the conductor. We propose a mechanical approach to form a cylindrical hole, plating the surface with silver nanoparticles and realizing the equivalent circuit model of the shorting pin. The proposed approach does not require high alignment sensitivity or the precise control of laser power level. Second, fully inkjet-printed wire bonding is proposed for the system on the package. The proposed technique not only reduces the discontinuity but also enables a fabrication without additional assembly. Third, the proposed technique is implemented for antenna development, which shows desirable performance with reduced fabrication complexity. The proposed technology is validated by microstrip lines, SIWs, SIW cavity slots, and flexible 4 × 4 patch arrays fabricated on various substrates including RO 4003C, polyimide, and polyethylene naphthalate. For comparison purposes, conventional approaches using printed circuit boards are also implemented and tested. The results indicate the generality and capability of the proposed technique.