bluuebunny/crossref_metadata_2025 huggingface dataset:
Survey of Consensus Algorithms for Proof of Stake in Blockchain
Authors: Ge Lina , Wang Jie , Zhang Guifen
Published: May 2022

As the core of blockchain technology, the consensus algorithm directly affects the security, stability, and decentralisation of the blockchain and numerous other important characteristics. Choosing an appropriate consensus algorithm for different scenarios is currently a challenge in the implementation of blockchain applications. This paper classifies the improvement schemes of proof of stake (PoS) into three categories: PoS-based consensus algorithms, PoS- and PoW-based consensus algorithms, and PoS- and BFT-based consensus algorithms. First, the study introduces the PoS and PoS consensus algorithm variants and then summarises the core ideas, effects, advantages, and disadvantages of these algorithms. Subsequently, the performances of the improved algorithms are compared. Finally, the main improved methods are summarised, and the most common network security attacks are discussed. The study lays a foundation for the main improvement directions of PoS in the future, hoping to provide a reference for researchers to help them select and design consensus algorithms in different application scenarios while also helping the evolution of consensus algorithms and the implementation of blockchain applications.
Breast Self‐Examination Knowledge and its Determinants among Female Students at Addis Ababa University, Ethiopia: An Institution‐Based Cross‐Sectional Study
Authors: Getu Mikiyas Amare , Abebe Mesfin , Tlaye Kenean Getaneh , Goshu Abel Tibebu
Published: May 2022

Introduction. Breast self‐examination is a noninvasive, low‐cost screening method for breast cancer detection. A thorough awareness of breast self‐examination enables the early detection of breast abnormalities and dramatically lowers breast cancer complications and mortality. The purpose of this study was to investigate the level of knowledge about breast self‐examination and its associated factors among female students at Addis Ababa University, Ethiopia. Methods. An institution‐based cross‐sectional study design was employed. The final calculated sample size was 407, and participants were recruited using a proportionate stratified random sampling approach. For data entry and coding, EPI Data 3.1 statistical software was utilized, and for data analysis, SPSS version 18 was employed. The data was described using descriptive analysis. Bivariate and multivariate logistic regression analyses were performed to determine the strength of the association between the predictor and the outcome variables. A 95% confidence interval and a p‐value of less than 0.05 were used to declare statistical significance. Results. The findings of this study revealed that 49.9% of respondents possessed good breast self‐examination knowledge. Previously, urban residents were about two times more likely to have good knowledge of BSE than their rural counterparts (AOR =2.16, 95% CI (1.18–39.91), p =0.011). The odds of having good BSE knowledge were about three times more likely among those who had a good attitude than those who had a poor attitude (AOR =3.17, 95% CI (2.02–4.74), p <0.001). Those who knew someone with a diagnosis of breast cancer were almost three times more likely to have good knowledge than those who did not know (AOR =2.95, 95% CI (1.77‐4.91), p <0.001). Conclusion. According to the findings of this survey, less than half of the students who participated had good knowledge of breast self‐examination. This justifies raising awareness about breast self‐examination among female students.
Mechanical Behavior of Loess Tunnels Caused by Surface Water Joints Infiltration
Authors: Li Qingfu , Liu Ming , Yu Yingqiao
Published: May 2022

Many researchers have conducted a vast amount of research on water sensitivity and joint seepage in loess tunnels. However, studies on the mechanical effects of shallow buried loess tunnels under the influence of joint dominant seepage are still insufficient. In this study, we simulate the seepage range of loess tunnels with joints, and we use this seepage range to numerically simulate the mechanical properties of shallow buried loess tunnels. Studies have shown that the permeability coefficient and the number of joints are the key factors that affect the amount of surface water infiltration. An increase in the permeability coefficient on the order of 5 × 10−3 m/s will cause the surface water to pour into the ground and rapidly form a saturated soak zone on the liner, while an increase in the number of joints will increase the width and formation rate of the saturated zone. The results of the tunnel mechanics simulation show that the surface settlement and the surrounding rock displacement increase with the increase of the wetted area of the vault; the effect of arch footing water immersion on the surface settlement and the surrounding rock displacement is most significant for the same wetted area width. Compared with the three‐stage method, the center cross diaphragm (CRD) method of excavation can better control the surface and the surrounding rock settlement under waterlogged conditions. In particular, the lining settlement on the waterlogged side can be effectively controlled, and the overall settlement of the tunnel is more uniform.
[Retracted] Influence of China’s Carbon Emissions Trading Scheme on Green Innovation of Enterprises
Authors: Qi Yawei , Zha Lei , Peng Wenxinag , Deng Zhikang
Published: May 2022

China’s economic growth has entered “new normal,” and the task of reducing carbon emissions has become more onerous. Hence, this study aimed to explore whether China’s carbon emissions trading pilot policy stimulated corporate green innovation capabilities. The data pertained to the green patent data of the listed companies in Shanghai and Shenzhen stock exchanges during 2008–2018. Using a difference‐in‐difference‐in‐differences (DDD) method, the study took advantage of the variations across regions, across enterprises, and across years and obtained several novel findings. First, the pilot carbon emissions trading policy significantly stimulated the green innovation capabilities of emission control companies in the pilot areas compared with enterprises in nonpilot areas and the nonemission control list. Second, the effect of the policy on the improvement in corporate green innovation capabilities might be driven by the improvement in corporate input factor allocation efficiency and the additional benefits that could be obtained from the carbon trading market. Third, the positive effect of the policy on the green innovation capabilities of state‐owned enterprises was more significant. Therefore, the establishment and promotion of a unified national carbon emissions trading market and supporting mechanisms should be accelerated to achieve the balance of stable economic growth and carbon emission task.
Research on the Innovation of Music Teaching in Universities Based on Artificial Intelligence Technology
Authors: Shi Junyan , Ning Qinliang
Published: May 2022

At present, music education in colleges is in a period of rapid development in China. At the same time, music education in universities is facing innovation and reform of teaching modes. How to improve music education in colleges and universities has become an important issue for music teachers in colleges. Music teaching and management activities for students in general universities can enrich students’ talents and expand their knowledge. It can also help them develop a positive emotional psychology and develop positive and healthy character characteristics, both of which are vital for college students’ healthy development. Innovation and modification in music teaching and management activities for students is the only way to increase the quality and effectiveness of music teaching for nonarts students and to promote the overall quality of students. Based on this, this paper proposes an innovative research method of college music teaching based on artificial intelligence technology. The method introduces a fuzzy evaluation algorithm to establish a two-level teaching evaluation index system and calculates the weights of each index based on fuzzy mathematical theory. In data processing, the SVM algorithm in the field of data mining is used to classify all collected teaching evaluation data in advance through supervised learning, which significantly improves the efficiency of data processing. The experimental results show that the model in this paper can well assess the quality of music teaching in colleges and universities and play a role in promoting the progress of music teaching in colleges and universities.
Grey Multiattribute Emergency Decision-Making Method for Public Health Emergencies Based on Cumulative Prospect Theory
Authors: Wang Hanyi
Published: May 2022

In view of the different risk preferences of decision-makers and the information gap in the timing of the subjects under evaluation, a multiattribute dynamic decision-making model that considers decision risk in addition to the priority of time is proposed. First, to account for the uncertainty of the evaluation information, the interval grey number method was applied to perform numerical abstraction and key data feature extraction on the scene of public health emergencies and to obtain the information after attributes were unified. Second, acknowledging the bounded rational behavior of actual decision-makers, the decision-makers’ risk preference was introduced into emergency decision-making. In combination with the time degree, the multiattribute unified decision matrix was transformed into an expected dynamic cumulative prospect decision matrix. Furthermore, based on cumulative prospect theory (CPT), the prospect value function and the weight function are constructed; a new optimization model is proposed to obtain the attribute weight. And according to the comprehensive prospect value of each alternative, the priority of the alternatives was determined. Finally, with the COVID-19 epidemic serving as an example, the results confirm that the emergency decision-making method proposed in this paper is feasible and effective.
Health Assessment of Foundation Pit Based on the Fuzzy Analytical Hierarchy Process
Authors: Sun Jinbo , Sun Ke , Gong Liang , Gu Sheng , Hao Hao
Published: May 2022

Many monitoring indexes affect the health condition of foundation pits to different extents. How to use a massive on‐site monitoring dataset to quantitatively assess the health of foundation pits is a problem that deserves due consideration. This paper proposes a foundation pit health assessment model based on the fuzzy analytical hierarchy process (AHP) method. First, factors affecting the health of foundation pits are classified by the AHP, a hierarchical factor system for foundation pit health assessment is established, and the index scale method is used to assign weights to assessment factors at different hierarchical levels. Combined with the fuzzy mathematical method, the membership functions of four health degree levels (A to D) are constructed, and the determination range of each health degree level is given to realize the quantitative calculation of the health condition from the bottom‐level assessment factor to the overall foundation pit. Considering that each assessment factor involves many monitoring points and different monitoring data, a comprehensive assessment operator is also constructed to highlight the most adverse impact. Finally, the proposed model is used to perform a health assessment of an actual foundation pit project, and the variation in the foundation pit health during the entire monitoring period is obtained. The health grade of the foundation pit is determined to be B, which is a basically healthy condition consistent with the on‐site inspection results.
Post-Ortho-K Corneal Epithelium Changes in Myopic Eyes
Authors: Qu Dongyi , Zhou Yuehua
Published: May 2022

The study is aimed at evaluating corneal epithelial thickness changes associated with overnight orthokeratology (ortho-K). In this retrospective study, epithelial thickness was measured using optical coherence tomography (OCT) before and after 1 day, 1 week, 1 month, and 3 months ortho-K nightly lens wear. Compared with pre-orthokeratology measurements, central (2 mm) corneal epithelium thickness was significantly reduced at 1 day, 1 week, 1 month, and 3 months with ortho-K (

P
<
0.05

). Paracentral (2 mm~5 mm annular ring) epithelial thickness was also significantly reduced at superior temporal, inferior temporal, temporal, and inferior locations after ortho-K (

P
<
0.05

), while midperipheral (5 mm~6 mm annular ring) epithelial thickness was greater post- than pre-ortho-K at superior, superior temporal, inferior temporal, inferior, and inferior nasal locations (

P
<
0.05

). In other zones, superior, superior nasal, nasal, and inferior nasal in paracentral annular ring and temporal and superior nasal in midperipheral ring, epithelial thickness underwent no significant change. Ortho-K lens wear caused the central corneal epitheliums to thin. The temporal half zones become thinner in paracentral zones and thicker in midperipheral zones.
Proposing Lane and Obstacle Detection Algorithm Using YOLO to Control Self-Driving Cars on Advanced Networks
Authors: Huu Phat Nguyen , Pham Thi Quyen , Tong Thi Quynh Phuong
Published: May 2022

Developing self-driving cars is an important foundation for the development of intelligent transportation systems with advanced telecommunications network infrastructure such as 6G networks. The paper mentions two main problems, namely, lane detection and obstacle detection (road signs, traffic lights, vehicles ahead, etc.) through image processing algorithms. To solve problems such as low detection accuracy of traditional image processing methods and poor real-time performance of methods based on deep learning methods, lane and object detection algorithm barriers for smart traffic are proposed. We first convert the distorting image caused by the camera and use a threshold algorithm for the lane detection algorithm. The image with a top-down view is then determined through the extraction of a region of interest and inverse perspective transform. Finally, we implement the sliding window method to determine pixels belonging to each lane and adapt it to a quadratic equation. YOLO algorithm is suitable for identifying many types of obstacles for identification problems. Finally, we use real-time videos and the TuSimple dataset to perform simulations for the proposed algorithm. The simulation results show that the accuracy of the proposal for detecting lanes is 97.91% and the processing time is 0.0021 seconds. The accuracy of the proposal for detecting obstacles is 81.90%, and the processing time is 0.022 seconds. Compared with the traditional image processing method, the average accuracy and execution time of the proposed method are 89.90% and 0.024 seconds, which is a strong antinoise ability. The results prove that the proposed algorithm can be deployed for self-driving car systems with a high processing speed of the advanced network.
Herbal Medicines for the Treatment of Chronic Obstructive Airway Diseases (Asthma or Chronic Obstructive Pulmonary Disease): A Prospective Observational Study
Authors: Lyu Yee Ran , Lee Su-Won , Kim Si-Yeon , Han Hye-Bin , Yang Won-Kyung , Kim Seung-Hyung , Jung In Chul , Kwon O-jin , Kim Ae-Ran , Kim Jinhee , Lee Mi Young , Park Yang-Chun
Published: May 2022

Background. Obstructive airway disease is a major health problem and has a great impact on global socioeconomic burden. Despite therapeutic advances in recent decades, there is still a need for effective and safe therapeutic agents for patients with asthma or chronic obstructive pulmonary disease (COPD). Methods. This prospective observational study explored the effects of herbal medicines in patients with asthma and COPD. All participants visited the hospital at least every 4 weeks for 12 weeks to receive their herbal medicines based on their pattern identification and to evaluate safety and efficacy endpoints. We followed the diagnostic criteria used by Korean medicine doctors to prescribe herbal medicines, explored variations in prescribed herbal medicines, and explored a number of clinical features in patients with asthma or COPD. Results. A total of 24 patients were enrolled: 14 were diagnosed with asthma and 10 with COPD and 19 completed the study. After 12 weeks of herbal medicine treatment, herbal medicines significantly improved the modified Clinical Asthma Measurement Scale in Oriental Medicine-V in asthma patients and the modified Medical Research Council Dyspnoea Scale and St. George’s Respiratory Questionnaire in COPD patients. For all patients, modified Medical Research Council Dyspnoea Scale score and interleukin-13 were found to be significantly different after treatment. Additionally, the majority of patients were satisfied with our herbal medicine treatments, and no severe adverse events were reported during the study. Conclusions. Our study provides preliminary clinical data on the safety and efficacy of herbal medicines in patients with asthma and COPD.
Physical Education Teaching Based on Human Model Design and Computer Simulation of Human Two-Dimensional Moving Image
Authors: Dai Jianghong
Published: May 2022

The traditional gait analysis is mainly used in the research of joint dynamics and electromyography, which is helpful to develop a medical feedback system. The system judges the leg injury by analyzing the patient’s gait, so as to provide effective treatment. In order to study how computer technology can be more appropriately introduced into physical education, this paper puts forward a new design method of manikin. The application of some key implementation technologies of computer simulation of two-dimensional motion images of human body is conducive to the scientization of physical education teaching and training. The experiment analyzes the daily movement of human body. MEMS technology is a technology to obtain human inertial data by simulating human motion with one or more computers. By integrating the collected data, different characteristics can be obtained. Then, classify these feature processing algorithms, and finally, identify the actions performed by the human body. Finally, the advantages of this recognition method in recognition accuracy are verified.
SmHeSol (IoT-BC): Smart Healthcare Solution for Future Development Using Speech Feature Extraction Integration Approach with IoT and Blockchain
Authors: Upadhyay Shrikant , Kumar Mohit , Kumar Ashwani , Ghafoor Kayhan Zrar , Manoharan S.
Published: May 2022

Voice of any human plays an important role in communication and sharing information among each other. Through voice, internal behavior can be identified as to whether the person is happy or angry which is reflected. A person’s behavior is not exactly reflected by their face; variation in his/her voice reflects somehow their behavior as there will be variation in voice and variation in frequency and pitch. Feelings and natural behaviors are important features, and there are many biological aspects through which they can be identified. Therefore, in this paper, we consider a Hindi speech specimen of different groups to identify the person’s behavior and natural feelings under different acoustic conditions. Many research papers show emotion recognition based on neural networks with different models using speech signals to identify the present status of any patient, and it helps to build a way for a smart healthcare system. Enabling service in terms of Blockchain means the sufferer does not require communicating with complex and failed tasks for collecting information from various sources to send to their expert. Blockchain provides experts access to systems and enables entry to the dataset section. Patients have total control over their data, and they no longer require monitoring to keep their data managed and up to date. Also, manually coordinating with data is required for multiple visitors, which can be a very tedious one. In this paper, we focused on feature removal of speech using different extraction approaches which were used to know the quality or state of voice specimens and also understand which feature extraction plays a vital role in gaining a close state of speech. Internet of Things-based learning platforms are used to gather the voice sample, and also, a deep gaining method was followed to reach and achieve the best accuracy and identify the error rate which will help to gather close behavior and state of mind. Finally, a proposed model based on the Gaussian mixture model as a classifier was used for its spotting and attestation.
Design and Implementation of Multiple Music System Based on Internet of Things
Authors: Xu Mengchen
Published: May 2022

With the rapid development of social economy and Internet of Things, the society has entered the era of networking, digitalization, and intelligence, bringing great convenience to people's life; Internet of Things music system also has begun to get people's extensive attention. Due to the influence of such factors as strong music professionalism, complex music theory knowledge, and diverse changes, it is difficult to identify music features. In order to strengthen the user’s personal experience of the music system, the multimusic systems are interconnected through information technology to realize the connection between objects and people. The system uses an embedded processor to realize the central control module and then according to network standard the sensor network is built, through radio frequency identification (RFID) technology for light, sound, infrared sensor, temperature, and other sensors for information reading. Music selection logic is designed based on the theory of music psychology and user behavior log, so as to select the best music for users to improve their mood and improve their life quality and work and study efficiency. At the same time, the system uses voice recognition technology to enhance user interaction, through the system, to provide the website to share their own music data and comments on songs and view song information, and the system runs stably and can collect high quality music signals and correctly identify the characteristics of music form and emotional characteristics.
Effects of Organizational Culture on Employer Attractiveness of Hotel Firms: Topic Modeling Approach
Authors: Lee Kyoung-Joo , Choi Sun-Yong
Published: May 2022

As the acquisition and retention of motivated and skilled employees are key to the high performance of hotel firms, employer attractiveness of hotel firms is a critical factor in achieving competitive advantage. Focusing on organizational culture, this study analyzes how different cultural attributes affect hotel firms’ attractiveness as employers. For the empirical analysis, this study collected 54,040 reviews of 157 large hotel chains and firms from Glassdoor in the United States. This study combines an unsupervised machine learning tool for topic modeling (latent Dirichlet allocation) with the coding process of researchers to measure the different cultural attributes of hotel firms. The research results show the positive and significant impact of four cultural attributes—collaborative, employee development, fair compensation, and customer focus—on employer attractiveness measured by both users’ employer satisfaction and recommendations to friends. In contrast, an innovation culture has no significant effect on attractiveness.
A Design Optimization Method with Sparse Scattered Data and Evolutionary Computation
Authors: Liu Yuxiang , He Shipei , Liu Wei , Chen Xihong
Published: May 2022

Engineering design can be regarded as an iterative optimization process. This process is difficult because of two main problems: the first is that computer‐aided engineering (CAE) is time‐consuming in terms of evaluating design solutions, while the second is the high dimensionality of design solutions. In the research community, a surrogate model is proposed to deal with the first problem while an evolutionary algorithm is adopted for the second. In this work, we develop a new method with only sparse scattered data, which is very common in many practical scenarios. The surrogate model can also assign a penalty factor for the predicted value, and this penalty factor can be used as one of the targets of the evolutionary algorithm to balance global exploration and local exploit. We also adopt a new evolutionary strategy, which can search high‐dimensional space. Three groups of experiments are conducted to validate the proposed methods. The experimental results show that the surrogate model can predict performance and the corresponding penalty factor, the evolutionary strategy is better in terms of searching high‐dimensional space compared with other evolutionary strategies, and the whole method can generate new design solutions that are near to the known design solutions. The experimental results show that this method can be used in practical scenarios, especially where they only have sparse scattered data.
IoT Smart Agriculture and Agricultural Product Income Insurance Participant Behavior Based on Fuzzy Neural Network
Authors: Tian Jiajia , Li Dan , Jia Xiaochen
Published: May 2022

With the continuous development of modern science, more and more attention is paid to the application of science and technology for agricultural production. First of all, this article analyzes the development trend of modern agriculture, and designs the system through an overall plan of four parts: automatic acquisition, terminal control, network transmission, and cloud platform and terminal application. Taking automatic irrigation system and temperature automatic control system as examples, design an automatic control algorithm based on fuzzy neural network, and obtain the best control strategy through fuzzy inference and neural network training. Then, this paper studies the reliability of “price setting” in agricultural income insurance. This article takes the main crops of a certain province as an example to verify and analyze the risks of farmers’ income levels from the viewpoint of corn yield risks and price risks, and advocate the necessity and feasibility of agricultural income insurance. Finally, from the perspective of different participants, this article analyzes the impact of insurance premium subsidies on the enthusiasm of agricultural producers to participate in insurance, the impact of insurance companies on the improvement of insurance operation efficiency in the planting industry, and the stability of the government’s promotion of agricultural production. It also influences to design a set of IoT automatic control system for intelligent agriculture. The system realizes the acquisition and remote control of agricultural data by establishing an Internet of Things cloud platform, and uses automatic control algorithms based on fuzzy neural networks to realize automatic water-saving irrigation, automatic temperature control, and other functions. Based on this research, the main actions of joining agricultural income insurance and the participation model based on the perspective of tripartite participants.
Preparation and Optical Properties of Compound Nanopowder Art Ceramics
Authors: Li Yulan
Published: May 2022

The fluorescent glass of white LEDs has high physical and chemical stabilities, good heat dissipation performance, and can maintain an excellent performance of fluorescent powder itself. In order to realize luminescent materials such as white LED lighting, laser lighting, and long-term lighting, this study proposes the preparation of a compound nanopowder art ceramic and its optical properties. In order to play the role of nanoparticles in optoelectronic or photonic devices, it is necessary to explore the preparation process and performance research of dielectric ceramics. This study uses high-purity aluminate (MgAl2O4) powder for transparent ceramics, doped with yellow nanophosphor as raw materials, through a sintering process in oxygen atmosphere, sintering combined with heat treatment, and isostatic pressing to prepare transparent ceramics. The ceramic sample is placed in a high-temperature and high-pressure environment for heat and other static pressure, and fluorescent samples are obtained. The results show that under 350 mA driving current, high-power white LEDs in the fluorescent ceramic package have almost no attenuation after 700 h, and the average attenuation of the LED package of the phosphor package is about 10%. The use of fluorescent ceramics can be packaged not only to improve the LED device’s light efficiency but also to increase the life of the white LED device.
Binocular Images Dense Matching considering Image Adaptive Color Weights and Feature Points
Authors: Xu Zhenghui , Wang Jingxue
Published: May 2022

When the matching cost function in Semiglobal Matching is unstable, the inaccurate matching cost values will be propagated in the cost aggregation process. It will lead to a serious mismatching phenomenon. To address the problem, a binocular images dense matching method considering image adaptive color weights and feature points was proposed. Firstly, The Color Birchfield Tomasi (CBT) matching cost calculation method was proposed to obtain a stable initial cost volume, which combined image adaptive color weights and gradient information. Secondly, the Scale-invariant Feature Transform matching algorithm was used to extract the a priori feature points from binocular images. Then, the feature points were filtrated. The cost volume was optimized by using their coordinate information and disparity information. Finally, an aggregation path segmentation rectification method was adopted to optimize the SGM aggregation paths and reduce the propagation of incorrect paths. Experimental results demonstrate that the proposed method can effectively improve the stability and accuracy of dense matching, reduce the mismatching phenomenon, and finally produce high-quality disparity maps.
The Recognition Method of Athlete Exercise Intensity Based on ECG and PCG
Authors: Wang Baiyang , Zhu Haiyan
Published: May 2022

Athletes usually arrange their training plans and determine their training intensity according to the coach’s experience and simple physical indicators such as heart rate during exercise. However, the accuracy of this method is poor, and the training plan and exercise intensity arranged according to this method can easily cause physical damage, or the training cannot meet the actual needs. Therefore, in order to realize the reasonable arrangement and monitoring of athletes’ training, a method of human exercise intensity recognition based on ECG (electrocardiogram) and PCG (Phonocardiogram) is proposed. First, the ECG and PCG signals are fused into a two-dimensional image, and the dataset is marked and divided according to the different motion intensities. Then, the training set is trained with a CNN (convolutional neural network) to obtain the prediction model of the neural network. Finally, the neural network model is used to identify the ECG and PCG signals to judge the exercise intensity of the athlete, so as to adjust the training plan according to the exercise intensity. The recognition accuracy of the model on the dataset can reach 95.68%. Compared with the use of heart rate to detect the physical state during exercise, ECG records the total potential changes in the process of depolarization and repolarization of the heart, and PCG records the waveform of the beating sound of the heart, which contains richer feature information. Combined with the CNN method, the athlete’s exercise intensity prediction model constructed by extracting the features of the athlete’s ECG and PCG signals realizes the real-time monitoring of the athlete’s exercise intensity and has high accuracy and generalization ability.
Theoretical and Simulation Studies of Transverse Beam Size Effects on Optical Transition Radiation in Prewave Zone
Authors: Liu Haofeng , Fang Fang
Published: May 2022

Optical transition radiation (OTR) has been studied and applied on the beam diagnostics for decades. The potential implication of OTR also includes THz radiation sources. Therefore, the theoretical analysis and simulation tools have become indispensable for the OTR study. Moreover, the OTR theory, for the wave zone (far-field approximation), has been widely used in the literature. However, these theories for the prewave zone have been proposed on the basis of single electron approximation. In this study, we developed a theory with consideration of the electron beam structure based on Kirchhoff’s method for studying the effect of beam transverse size on the angular distribution of the OTR in prewave zone. The proposed formalism involves complicated convolution integral of functions and the dimensions of integrand are not low. To perform such integral, we developed a Fortran program for quasi-monte Carlo method, which is robust and suitable for high dimensional integration. The disadvantage of this method is that a large amount of samplings may need to be employed to achieve good convergence. Therefore, in order to get the radiation angular profile, we need to perform such integration for different observation angles that might be computationally intensive. Therefore, we parallelize the program with the Message Passing Interface (MPI) programming concepts. To verify the theoretical calculations, few two-dimensional FDTD simulations were carried out that show the validity of the proposed model. The proposed theory and numerical tool would be used to predict radiation properties of the NSRRC THz coherent transition radiation (CTR) in the prewave zone.
Classification of Muscle Invasive Bladder Cancer to Predict Prognosis of Patients Treated with Immunotherapy
Authors: Wang Zhifeng , Li Xiqing , Wang Xiaoqing , Liu Jie , Wang Lingdian , Wei Wei , Duan Xiaoyu , Ding Degang
Published: May 2022

Background. Recently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates. Methods. The mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters. Results. Two TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71. Conclusion. The present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.
An Overview of Systematic Reviews on the Effectiveness of Wenxin Keli in the Treatment of Atrial Fibrillation
Authors: Wang Yuanping , Liu Yuntao , Yan Xia , Wang Dawei
Published: May 2022

Background. Atrial fibrillation is one of the most common cardiac arrhythmias. Wenxin Keli (WXKL) is a Chinese herbal extract widely used in China to treat patients with atrial fibrillation. This study aimed to outline and summarize the current evidence of systematic reviews (SRs)/meta-analyses (MAs) investigating the clinical efficacy of WXKL in atrial fibrillation. Methods. From inception to December 2021, 6 electronic databases in English and Chinese were searched for potential SRs/MAs. The GRADE (Grading of Recommendations Assessment, Development, and Evaluation), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) checklist, and AMSTAR-2 (Assessing the Methodological Quality of Systematic Reviews-2) were used to evaluate the quality of the evidence, reporting, and methodology of SRs/MAs regarding WXKL for the treatment of atrial fibrillation. Results. A total of 8 SRs/MAs were included in the present study. The results of AMSTAR-2 and PRISMA were unsatisfactory for the main insufficiency founded in registration and protocol, search strategy, excluded study statement, evidence certainty assessment, and funding and conflict of interest information. All the included SRs/MAs were assessed as very low in methodological quality. Moreover, 23 outcomes were evaluated by GRADE for the certainty of evidence, and 2 outcomes were assessed as moderate, while 15 were low and 6 were very low. Risk of bias and publication bias contributed to the downgrading. Conclusion. WXKL may be clinically efficacious and safe for the treatment of atrial fibrillation. This finding, however, should be regarded with caution because of the low level of evidence and methodological qualities of the involved SRs/MAs. More standardized, rigorous, and comprehensive SRs/MAs and randomized control trials are needed to provide strong evidence to reach more convincing conclusions.
Effects of Herbal Therapy on Intestinal Microbiota and Serum Metabolomics in Different Rat Models of Mongolian Medicine
Authors: Jiu Guniang , Dao Riao , Wu Dongxing , Hung Wang , Jin Haburi , Li Li , Fu Xiquan , Sa Chula , Eerdunchaolu
Published: May 2022

Objective. Heyi disease, Xila disease, and Badagan disease are three common diseases in Mongolian medicine. The changes in intestinal microbiota may be associated with the occurrence, development, and treatment of these diseases. This study aimed to investigate the effects of herbal treatment on intestinal microbiota and serum metabolites in rats with these three diseases. Methods. Firstly, Heyi, Xila, and Badagan disease model rats were established by environmental, diet, and drug intervention. Then, 16S rRNA gene sequencing and metabolomics analysis were used to analyze the changes in intestinal microbiota and serum metabolites after treatment. PICRUSt analysis was applied to predict the potential functions of intestinal microbiota, and OPLS-DA multivariate model was applied to screen differential serum metabolites. Results. 16S rRNA gene sequencing showed that herbal treatment significantly increased the species diversity and changed the composition of intestinal microbiota in Heyi disease and Xila disease rats. After treatment, there were 10, 9, and 3 bacterial biomarkers that were increased in Heyi, Xila, and Badagan disease rats, respectively. In the Heyi disease model, treatment resulted in 45 differential serum metabolites, involving 4 pathways. In the Badagan disease model, treatment resulted in 62 differential serum metabolites, involving 4 pathways. However, there was no significant difference in serum metabolites between TreatB and ConB in the Xila disease model. Conclusions. Herbal treatment significantly changed the intestinal microbiota and serum metabolites of rats with three Mongolian medicine diseases.
Expression of TRP Channels in Colonic Mucosa of IBS-D Patients and Its Correlation with the Severity of the Disease
Authors: Cheng Li , Luo Qing-Qing , Chen Sheng-Liang
Published: May 2022

Aim. Lots of researches have endeavored to elucidate the pathogenetic mechanism of visceral hypersensitivity in order to guide the therapy of diarrhea predominant-irritable bowel syndrome (IBS-D). Transient receptor potential (TRP) channels and their role in visceral nociception have been vastly investigated. We investigated the expression of TRP channels in IBS-D colonic biopsies and its correlation with the severity of the disease. Methods. Sigmoid biopsies were obtained from 34 IBS-D patients and 28 healthy controls (HCs). IBS-D was diagnosed according to Rome IV criteria. Their clinical parameters were assessed through questionnaires. Expression of TRPV1, TRPV4, TRPA1, TRPM2, and TRPM8 was evaluated with immunohistology staining. Results. Expression levels of TRPV1, TRPV4, and TRPA1 in the colonic mucosa of IBS-D patients were significantly higher than those in HCs (

p
<
0.05

), while there was no obvious difference of TRPM2 and TRPM8 expression between IBS-D patients and HCs. In addition, the expression levels of TRPV1 and TRPA1, but TRPV4, in the colonic mucosa correlated positively with the severity of diseases (

r
=
0.6303

and 0.4506, respectively,

p
<
0.05

). Conclusions. Expression of TRPV1, TRPA1, and TRPV4 in the colonic mucosa was enhanced in IBS-D patients compared with HCs with the former two correlated with the severity of the disease. TRP channels might be promising biomarkers in the diagnosis and estimate of the severity in IBS-D.
Study on Effect of Consumer Information in Personal Credit Risk Evaluation
Authors: Wen Hongmei , Sui Xin , Lu Shaopeng
Published: May 2022

This study selects Chinese borrowers’ information from a platform that has both online shopping and consumer loan service as sample, studies the effect of consumer information in personal credit risk evaluation, and uses the lLogistic regression model, light gradient boosting machine (LightGBM) algorithm, and Shapley Additive Explanation (SHAP). The results show that the information of all consumer loan groups cannot be covered by traditional credit information. Consumer information can help predict the behavior of borrower’s repayment and provide support for personal credit risk evaluation effective. Adding consumption information to the personal credit risk evaluation model can improve the accuracy of the model effectively. The model variables are ranked by feature importance, and there are 5 consumption indicators in the first 5 indicators of feature importance, which further verifies the value and effect of consumption information in personal credit risk evaluation. This study not only reveals the effect and value of consumer information in personal credit risk evaluation effectively, but also provides new ideas for the development of consumer financial market.
Research on Nutrition, Dental Caries Status Using Novel Methods, and Related Factors to Preschool Children in Rural Areas of Vietnam
Authors: Cuong Do Hong , Tam Vu Van , Tinh Hoang Quy , Do Le Thanh , Nghia Nguyen Trong , Anh Hoang Cong
Published: May 2022

The study aims to examine correlations between nutrition status with different factors and dental caries of preschool children in rural areas of Vietnam. A big data based on a total of 690 children (356 boys and 334 girls), aged 2–5 years, living in Van Xuan commune, were thoroughly analyzed. Oral examinations were performed by dentists with the assistance of nursery teachers and the research team. Caries was diagnosed using criteria established by the International Caries Detection and Assessment System (ICDAS). The examined children and their parents responded to questions pertaining to dental hygiene practices. The nutrition status of preschool children was determined by the World Health Organization (WHO) standards in 2006. There are factors which have effects on the malnutrition status of children in the research. The prevalence of dental caries also contributed importantly to assess children’s development. In this study, the stunting groups have a higher ratio of caries compared to the others. Children’s morphology and nutritional status are associated with dental caries among the preschool children in Van Xuan commune, Vinh Tuong district, Vinh Phuc province.
Watery Rose Apple: A Comprehensive Review of Its Traditional Uses, Nutritional Value, Phytochemistry, and Therapeutic Merits against Inflammation-Related Disorders
Authors: Yassir Mouna , Bakrim Widad Ben , Mahmoud Mona F. , Drissi BadrEddine , Kouisni Lamfeddal , Sobeh Mansour
Published: May 2022

The myrtle family, Myrtaceae, constitutes over 5500 species, and Syzygium is considered the largest genus of the flowering plants within the family. The watery rose apple, Syzygium aqueum, is a traditional medicinal plant with various bioactive compounds distributed in all plant parts. These include phenolic compounds, flavonoids, tannins, terpenoids, and essential oils. S. aqueum extracts and their isolated compounds showed multiple beneficial biological effects such as antibacterial, antifungal, antidiabetic, analgesic, antimalarial, antioxidant, anti-inflammatory, and anticancer activities. This review is aimed at discussing all the available information about the nutritional value, traditional uses, and therapeutic properties of the leaves, fruit, and stem bark of the plant, in addition to the distribution of phytoconstituents in its different parts as well as recommend future research directions on this species to promote its clinical uses.
Kernel L‐Ideals and L‐Congruence on a Subclass of Ockham Algebras
Authors: Alemayehu Teferi Getachew , Engidaw Derso Abeje , Addis Gezahagne Mulat
Published: May 2022

In this paper, we study L‐congruences and their kernel in a subclass of the variety of Ockham algebras A. We prove that the class of kernel L‐ideals of an Ockham algebra forms a complete Heyting algebra. Moreover, for a given kernel L‐ideal ξ on A, we obtain the least and the largest L‐congruences on A having ξ as its kernel.
Methane Flux Effect on Hydrate Formation and Its Acoustic Responses in Natural Sands
Authors: Bu Qingtao , Xing Tongju , Hu Gaowei , Liu Changling , Li Chengfeng , Zhao Jinhuan , Wang Zihao , Zhao Wengao , Kang Jiale
Published: May 2022

The acoustic properties of hydrate deposits are important parameters for hydrate geophysical exploration, and the gas leakage model plays a very important role in hydrate accumulation systems. In order to reflect the gas supply environment during hydrate formation, a high-pressure device with a simulated leakage system was designed to achieve different methane flux supplies. The effects of different methane fluxes on the hydrate formation rate and the maximum hydrate saturation were obtained. The results in this study indicate that similar hydrate formation rates occur in systems with different methane fluxes. However, when the methane flux is large, it takes longer to reach the maximum hydrate saturation, and the larger the methane flux, the larger the hydrate saturation formed. In each methane flux system, the elastic velocity increased slowly with increasing hydrate saturation at the beginning of hydrate formation, but velocity increased quickly when the hydrate saturation reached 50–60%. In order to take into account the effect of the gas, the calculated values of the elastic velocity model were compared with the experimental data, which indicated that the BGTL theory and the EMT model are more adaptable and can be used to deduce hydrate morphology. In the large methane flux system, the hydrate mainly forms at grain contacts when the hydrate saturation is 10–60%. As the hydrate saturation reaches 60–70%, hydrate forms first in the pore fluid, and then the hydrates contact sediment particles.