bluuebunny/crossref_metadata_2025 huggingface dataset:
Path Optimization of Agricultural Robot Based on Immune Ant Colony: B-Spline Interpolation Algorithm
Authors: Feng Kai , He Xiaoning , Wang Maoli , Chu Xianggang , Wang Dongwei , Yue Dansong
Published: April 2022

This study presents a path planning algorithm based on immune ant colony B spline interpolation to eliminate path duplication and corner inflection points, increase the smoothness of the agricultural robot’s path trajectory, and enhance the path planning performance. Firstly, this study improves the convergence speed of the algorithm by introducing an adaptive factor that obeys the normal distribution to optimize the path heuristic function. Secondly, to strengthen the variety of the target path search, an immune algorithm was utilized to perform a global investigation of the appropriate routes and assign the initial pheromones of the ant colony. Then, the adaptive judgment factor is introduced based on the incentive degree function and the pheromone evaporation coefficient, and the update formula are adjusted to promote the algorithm’s flexibility while suppressing the optimal regional problem. Finally, the pathways are processed using a cubic B spline interpolation method to remove the edges and minimize the peak inflection points. To verify the specific optimization performance of the algorithm in this paper, we have selected the same type of improved algorithm as a comparison and tested it in a raster map model. The simulation results demonstrate that the values of the proposed algorithm paths and the standard deviation of the tracks are smaller than the comparison algorithm, which indicates the higher performance of the algorithm in searching the optimal routes and more flexible and accurate search. Compared with the comparison algorithm, the average time-consuming reduction rate of the algorithm in this paper is between 38% and 45% in different grid maps, and the number of turns is reduced by 33% to 58%. It meets the requirements of the actual work on the robot.
A 3D Printed Ka-Band High-Efficiency Wide-Slit Antenna Array for High-Power Microwave Applications
Authors: Liu Liang , Yang Yu , Li Shifeng , Fang Xianghe , Meng Fanbao , Yu Chuan
Published: April 2022

A Ka-band high-power waveguide wide-slit antenna array with high efficiency and high-power capacity is proposed in this paper. The antenna uses a waveguide vertical transition structure as a part of the feed network, which improves the compactness and power capacity of the antenna. A 2 × 8 antenna array was simulated, fabricated by 3D printed technology, and measured. The measured reflection coefficient is less than −10 dB in the range of 30.4–30.9 GHz, the tested gain at 30.8 GHz is 25.8 dB, and the corresponding aperture efficiency is larger than 80%. This antenna can be applied in high-power millimeter-wave systems.
RAGE Regulating Vascular Remodeling in Diabetes by Regulating Mitochondrial Dynamics with JAK2/STAT3 Pathway
Authors: Sun Shengjia , Chen Qiying , Wu Bangwei , Huang Qingyu , Maimaitijiang Alimujiang
Published: April 2022

In this research, we will explore the role and modulation of mitochondrial dynamics in diabetes vascular remodeling. Only a few cell types express the pattern recognition receptor, also known as the AGE receptor (RAGE). However, it is triggered in almost all of the cells that have been investigated thus far by events that are known to cause inflammation. Here, Type 2 diabetes was studied in both cellular and animal models. Elevated Receptor for advanced glycation end products (RAGE), phosphorylated JAK2 (p-JAK2), phosphorylated STAT3 (p-STAT3), transient receptor potential ion channels (TRPM), and phosphorylated dynamin-related protein 1 (p-DRP1) were observed in the context of diabetes. In addition, we found that inhibition of RAGE was followed by a remarkable decrease in the expression of the above proteins. It has also been demonstrated by western blotting and immunofluorescence results in vivo and in vitro. Suppressing STAT3 and DRP1 phosphorylation produced effects similar to those of RAGE inhibition on the proliferation, cell cycle, migration, invasion, and expression of TRPM in VSMCs and vascular tissues obtained from diabetic animals. These findings indicate that RAGE regulates vascular remodeling via mitochondrial dynamics through modulating the JAK2/STAT3 axis in diabetes. The findings could be crucial in gaining a better understanding of diabetes-related vascular remodeling. It also contributes to a better cytopathological understanding of diabetic vascular disease and provides a theoretical foundation for novel targets that aid in the prevention and treatment of diabetes-related cardiovascular problems.
Comparative Analysis of Proteomic of Curcumin Reversing Multidrug Resistance in HCT-8/VCR Cells
Authors: Li Lei , Yu Libo , Cao Xiansheng , Zhang Chao , Liu Qi , Chen Jun
Published: April 2022

To further explore the mechanisms of curcumin reversing multidrug resistance (MDR) in HCT8/VCR cells. Here, we employed comparative analysis of proteomic of essential proteins of human colon carcinoma HCT8/VCR cells with or without treatment of curcumin by separating and quantifying the essential protein posttranslational modification through radical-free two-dimensional polyacrylamide gel electrophoresis with strong reductant. The reverse impact of curcumin on multidrug resistance of HCT8/VCR and HCT8/VCR cells was evaluated using MTT assay. After adding curcumin 25 μM for 72 h, by 2-DE and mass spectrometry, twenty proteins were certified with changed expression levels. Three protein sites were upregulated and seventeen protein sites were downregulated in curcumin-treated HCT-8/VCR. Verification analyses were conducted using RT-PCR and Western blotting for downregulated proteins including GSTP1 and PRDX6. The proteins might have a direct or indirect contact with multidrug resistance. The finding of the research would provide novel sights for systematically comprehending the mechanisms of the reversal impacts of curcumin on MDR in HCT8/VCR cells and contribute to the recognition and application of new markers in clinical practice.
Research on the Spatial Agglomeration Characteristics and Influencing Factors of Express Delivery Station Based on DNN
Authors: Kou Zheng
Published: April 2022

In this paper, the POI data of 736 Cainiao stations in Nanjing is taken as the research sample. With the help of ArcGIS software, the standard deviation ellipse, spatial autocorrelation, average nearest neighbor, cold and hot spot analysis, nuclear density estimation, and other spatial analysis models are used to quantitatively characterize its business mode, spatial distribution characteristics, and equilibrium. Based on DNN, the spatial agglomeration characteristics and distribution directions of the Cainiao station in Nanjing were sorted out, the cold spots and hot spots of the spatial layout were identified, and the spatial differentiation rules and agglomeration patterns were revealed. Finally, the geographically weighted regression analysis model is used to analyze the influencing factors of the spatial agglomeration of the Cainiao station in Nanjing. The research found that Firstly, the proportion of Nanjing Cainiao station operating mode adopting the exclusive mode is 59.1%, the proportion adopting the concurrent operation mode is 33.7%, and the rest adopting the joint operation mode of cooperation with other logistics enterprises. Secondly, Nanjing Cainiao Station gathers in the central city area, forming a “central hot spot.” The urban fringe area does not form a “peripheral cold spot area,” and the whole presents a “1 + 4” five-core agglomeration model across the river. Thirdly, Regional GDP, population density, and the number of convenience stores/supermarkets are the main factors affecting the spatial agglomeration of the Cainiao station in Nanjing.
Multiscale Layout and Planning of Smart Gardens in the Environment of Agricultural Internet of Things
Authors: Geng Xiuting , Chun Ou
Published: April 2022

This paper studies the general architecture and key technologies of the Agricultural Internet of Things and summarizes the latest progress of the Internet of Things system model. On this basis, a general system architecture model suitable for the Agricultural Internet of Things is initially established, and the key technologies of the Agricultural Internet of Things are expounded to provide a reference for the in-depth application and development of the Agricultural Internet of Things. Aiming at the problem that the energy consumption of each node in the network is uneven in the process of monitoring agricultural production environment by wireless sensor network, which leads to the failure of some nodes in the network too fast, an event-driven wireless routing algorithm for agricultural production environment monitoring is proposed. By reducing the number of pieces of control information sent in the cluster, the algorithm selects the next hop forwarding node from candidate nodes according to the forwarding cost function to shorten the data transmission distance, reduce energy consumption, and improve the life cycle of the entire network. The experimental results show that the algorithm in this paper effectively avoids the waste of node energy consumption caused by the active transmission mode and at the same time better balances the overall energy consumption of the network and prolongs the life cycle of the network. The platform satisfies the needs of a local garden bureau to integrate comprehensive landscaping business information and display comprehensive landscaping information in a comprehensive, timely, and dynamic manner. At the same time, it has made useful explorations in the development and integration of landscaping business data interfaces and GIS data interfaces. All system functions of the smart garden information management platform project are reasonably designed and completely correct and basically meet the software function requirements specified in the requirements specification. Some functions were found not realized during the test. After rectification, various systems have been improved. All system interfaces of the smart garden information management platform project have reasonable layout, simple operation, and good ease of use. The smart garden information management platform project meets the business logic requirements and fulfills the requirements of the information construction of the Garden and Greening Administration. All systems of the smart garden information management platform project are simple to install and easy to operate and meet the user’s requirements after installation. The system has a strict permission setting function. People with different permissions can only see the content of the fields that they have permission to access, which effectively ensures the security of the data.
Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology
Authors: Xing Yanhua
Published: April 2022

The tourism teaching system provides all kinds of teaching resources for students and shares good teachers, which greatly improves the quality of teaching and learning, and it enables students to teach and learn randomly in the system. The mode of educational information dissemination has also changed, resulting in great changes in educational models, ideas, and methods. The tourism teaching system overcomes the shortcomings of traditional education. In the 21st century, the means of education and teaching will be ITS-based, and technology will be used to optimize teaching. With the rapid development of network technology, it has been widely used in the field of education, and the way of educational information dissemination has also changed, which has led to great changes in educational models, concepts, and methods. Therefore, the combination of artificial intelligence technology and tourism teaching system is the focus of current educational research. In this paper, the tourism teaching system is designed based on artificial intelligence technology. Combined with the application status of the tourism teaching system in education, a personalized cognitive student model is designed by combining artificial intelligence technology with student behavior analysis, and an intelligent and personalized educational environment is developed. Design and implement a simple web-based tourism teaching system, use a personalized cognitive student model to model students, and use an expert question bank and self-built question bank to test students’ learning to provide a basis for students’ next learning.
Research on Dynamic Task Allocation Algorithm to Improve User Participation in the Witkey Mode
Authors: Wan Yujie , Fu Minglan , Chen Lvqiang , Chen Debao , Li Jiekun , Zhou Wei , Liu Mengxue
Published: April 2022

The task allocation process in the Witkey mode is dynamic and open, in which Witkey is a rational person. Due to Witkey's individual rationality, in order to make the result of task allocation stable, task allocation must reach the Nash equilibrium. However, the Nash equilibrium point does not necessarily have the highest total system revenue. In order to make the task allocation result stable and have a high total system revenue, this paper proposes an incentive measure based on integral ranking to improve user participation. When Witkey adopts the best response strategy to select task, the order of participating in the selection will affect the individual income of Witkey to a certain extent. The higher the order, the greater the probability of obtaining higher system income. Based on this idea, a dynamic task allocation algorithm with complex tasks in the Witkey mode is proposed by combining incentive measures with best response strategy and reasonable benefit allocation strategy. The finally simulation results verified the effectiveness of the proposed algorithm, and the impact of incentive measures on the total revenue of the system was also examined.
Reform of PBL Teaching Mode of Microcomputer System and Embedded Application Course Group
Authors: Ding Peifen , Zhao Baoqi
Published: April 2022

In recent years, the problem-based learning (PBL) model has become a hot spot in the education field. Its unique “learner”-centered and “problem-based” characteristics have been widely used in teaching. Make a useful attempt to comply with the development of the times and the requirements of education. This article puts forward the research on the reform of PBL teaching mode based on the microcomputer system and embedded application course group, using literature data method, fuzzy comprehensive analysis method, questionnaire survey method and other methods to explore. This paper designs the experiment of PBL teaching mode reform based on the microcomputer system and embedded application course group, analyzes the domestic research status of PBL teaching mode, and analyzes and summarizes the teaching effect of PBL. The average score of the experimental class with PBL teaching was 74.76 points, and the passing rate reached 95.13%; while the average score of the control group was 70.21 points, and the passing rate was 92.30%. The PBL teaching model of the microcomputer system and embedded application course group conforms to the general direction of my country's education development and reform. It can cultivate students’ innovative consciousness and problem-solving ability. It has a certain reference value for improving the quality of course teaching and also provides teachers with a Kind of new teaching ideas and approaches.
Construction of Higher Education Management Data Analysis Model Based on Association Rules
Authors: Zhao Fanfei , Zhang Guanqun , Wang Zhaoli , Hao Xiangyu
Published: April 2022

This paper adopts the analytical method of intelligent programming to conduct in-depth research and analysis on university education management and designs a corresponding university education management model to be applied in actual teaching. Based on the basic theories and methods of big data distributed data mining and machine learning, automated machine learning, and big data programming computational methods, this paper combines the importance and technical challenges of the algorithms themselves and the background of the practical application needs of the industry and firstly selects a series of data mining and machine learning algorithms that are commonly used based on high complexity, outstanding computational efficiency problems, and difficult to design distributed algorithms. The research on efficient large-scale distributed parallelized data mining and machine learning methods and algorithms is carried out. As an innovative teaching model, the experiential teaching model focuses on the cultivation of individual students’ independent learning ability and subjective initiative, which not only can effectively activate the classroom atmosphere and improve the teaching effect but also meet the requirements of classroom teaching for the development of the times. The empirical evidence of the experiential teaching model proposed in this paper is carried out, and the implementation process of the experiential teaching model of high school physics is further improved. In terms of the overall implementation effect, the experiential teaching model proposed in the paper can deepen students’ understanding of concepts and physical laws, thus enabling them to solve physics problems better, and has a positive and active effect on enhancing students’ learning interests and attitudes. In the case of system security, the average comprehensive index of each unit is 61.11, 22 are higher than the average index, accounting for 61.11%, 14 are lower than the average index, accounting for 38.89%, and 23 management departments’ management systems passed the safety monitoring, accounting for 63.89%. At the same time, the proposed model also provides educational practitioners with a more reliable framework for instructional design, which has realistic reference value and significance.
“Pseudomonas fluorescens” as an Antagonist to Control Okra Root Rotting Fungi Disease in Plants
Authors: Sharma Harsha , Haq Mohd Anul , Koshariya Ashok Kumar , Kumar Anil , Rout Sandeep , Kaliyaperumal Karthikeyan
Published: April 2022

The common bacteria found in fruit and vegetables are Pseudomonas fluorescens which is Germ-negative and is rod-shaped. Pseudomonas fluorescens has been originated from the rhizosphere of Roorkee-grown okra. The presented work involves recognizing and controlling the isolates of Pseudomonas fluorescens. The scope of the proposed work is that the technique used here is a unique strategy to plant protection and control of rotting fungus diseases based on the recognition and management of Pseudomonas fluorescens isolates. Antagonist effect occurs commonly in vegetable and fruit plants. The main goal of this study is to isolate, identify, and evaluate the development of these bacteria which effects on plant growth. In this research work, five isolates have been chosen for further research based on their morphological, biochemical, and physiological characteristics. All five isolates have been identified as Pseudomonas fluorescens from Bergey’s Manual for the determination of bacteriology. Catalase, urease, amylase, and citrate utilization test were all positive in all of the isolates. PFTT4 was identified to be a likely strain for all plant growth promoting exercises such as age of IAA, HCN, ammonia, and phosphate solubilization subsequent to being assessed for their plant development advancing properties. Further, in vitro exploring uncovered that PFTT4 diminished the development of phytopathogens such as Fusarium solani and extraordinarily further developed seed germination just as all development boundaries like shoot and root length. Furthermore, Pseudomonas sp. PFTT4’s plant growth promoting and antifungal activities put forward to it could be there used because of bioinoculant agents for Abelmoschus esculentus.
Cocaine and COVID-19 in ST-Elevation Myocardial Infarction
Authors: Appenzeller Florian , Gawaz Meinrad , Müller Iris
Published: April 2022

Both COVID-19 disease and cocaine consumption have prothrombotic and hypercoagulable effects and are associated with increased risk of cardiovascular events. We report the case of a patient with acute myocardial infarction in the setting of active COVID-19 disease and recent cocaine consumption. We hypothesize that COVID-19 and cocaine synergistically provoke cardiovascular events. Identifying COVID-19 disease and/or cocaine abuse as potential triggers of acute myocardial infarction can be crucial due to distinctive therapeutic consequences.
Iterative Least Square Optimization for the Weights of NURBS Curve
Authors: Wang Tuo-Ran , Liu Ning , Yuan Lei , Wang Ke-Xin , Sheng Xian-Jun
Published: April 2022

NURBS curves have been widely applied in the field of data points approximation, and their fitting accuracy can be improved by adjusting the values of their weights. When applying the NURBS curve, it is difficult to obtain the optimal weights values due to the nonlinearity of the curve fitting problem with NURBS. In this paper, a weights iterative optimization method for NURBS curve fitting is proposed, where the geometric property of weight has been adopted to iteratively obtain the adjusting values of the weights with the least square method. The effectiveness and convergence of the proposed method are demonstrated by numerical experiments. The results show that the proposed method can obtain higher fitting accuracy than other iterative optimization methods. Meanwhile, it has the merits of data noise robustness, high accuracy with small-scale knots, and flexibility. Hence, the proposed method is suitable for applications including noisy data approximation and skinned surface generation.
Optimization of Ideological and Political Education Management Strategies under k-Means Algorithm in Big Data Environment
Authors: Wang Hongyan
Published: April 2022

The development of information technology has promoted the reform of ideological and political education in colleges and universities. A large amount of data has been accumulated in the education management database, and the information implied by these data can provide scientific guidance for the optimization of educational management strategies in colleges and universities. The relationship and characteristics of big data in ideological and political education in colleges and universities were expounded, and the feasibility of applying big data technology to ideological and political education was analyzed. The k-means algorithm was selected for cluster analysis, and its process and principles were expounded. As the traditional k-means clustering algorithm has low data processing efficiency and large deviation of the results, the algorithm was optimized by controlling the iterative method of the algorithm. Besides, the ideological and political education management under the optimized k-means algorithm was established. The work assessment quantitative scale in the management of ideological and political education was adopted as the data source, and the optimized k-means algorithm was used to carry out cluster analysis. The results show that management attitude was scored as 0.634, the management ability was 0.6092, the management effect was 0.6082, and the management method was 0.5792. It was indicated that all the scores were above the middle for greater than 0.5, suggesting that the overall management level was above the middle, which was relatively good. The optimized k-means-based ideological and political education management strategy model can analyze the current educational management status of colleges and universities more accurately. It can also provide scientific guidance for colleges and universities to conduct teaching management reasonably and scientifically according to the data analysis results. The optimized k-means algorithm was compared with the traditional algorithm, from which the optimized algorithm was obviously better than the traditional algorithm in terms of clustering effect and operation stability.
Virtual Digital Communication Feature Fusion Based on Virtual Augmented Reality
Authors: Zhang Xi
Published: April 2022

In order to have a deeper understanding of the integration of virtual digital communication characteristics, it is studied and combined with virtual augmented reality technology. The progress of science and technology has promoted the development and progress of virtual reality technology. In recent years, virtual reality technology has been more and more widely used in digital media art creation and plays an important role in improving the quality of digital media art. This study introduces digital media art and virtual reality technology and analyzes the specific measures and practical significance of the application of virtual reality technology in digital media art creation. As an emerging technology, virtual reality technology has certain advantages. Its application in digital media art creation can effectively make up for the shortcomings of traditional creation and realize the innovation and reform of art creation.
Using Identity-Based Cryptography as a Foundation for an Effective and Secure Cloud Model for E-Health
Authors: Mittal Shikha , Bansal Ankit , Gupta Deepali , Juneja Sapna , Turabieh Hamza , Elarabawy Mahmoud M , Sharma Ashish , Bitsue Zelalem Kiros
Published: April 2022

Nowadays, one of the most popular applications is cloud computing for storing data and information through World Wide Web. Since cloud computing has become available, users are rapidly increasing. Cloud computing enables users to obtain a better and more effective application at a lower cost in a more satisfactory way. Health services data must therefore be kept as safe and secure as possible because the release of this data could have serious consequences for patients. A framework for security and privacy must be employed to store and manage extremely sensitive data. Patients’ confidential health records have been encrypted and saved in the cloud using cypher text so far. To ensure privacy and security in a cloud computing environment is a big issue. The medical system has been designed as a standard, access of records, and effective use by medical practitioners as required. In this paper, we propose a novel algorithm along with implementation details as an effective and secure E-health cloud model using identity-based cryptography. The comparison of the proposed and existing techniques has been carried out in terms of time taken for encryption and decryption, energy, and power. Decryption time has been decreased up to 50% with the proposed method of cryptography. As it will take less time for decryption, less power is consumed for doing the cryptography operations.
Reliable Laboratory Tests: A Prerequisite for the Design of High-Quality Slurry Surfacing Mixtures
Authors: Destrée Alexandra , Vansteenkiste Stefan , Tanghe Tine , De Visscher Joëlle
Published: April 2022

Surface treatment with slurry surfacing is a quick and cost-effective technique to restore the condition and performance of a road surface, compared with the replacement of the wearing course. It is therefore not surprising that cities and municipalities, faced with increasingly tight budgets, choose slurry surfacing to maintain their roads. A slurry surfacing with good performance and a long service life requires, above all, a well formulated mixture and a good control of the execution quality. Slurry surfacing could then even be considered for ultrathin wearing courses on new pavements. From 2016 to 2020, the Belgian Road Research Centre (BRRC) was engaged in a 4-year research project called BeP2S (Better Performing Slurry Surfacing) financially supported by the Belgian Bureau for Standardization (NBN). The project focussed on laboratory testing, gathering data and field experience, assessing the link between laboratory and field performance, and the mix design process. This paper is dedicated to the European test methods of the EN 12274 series (“Slurry surfacing–Test methods”), as well as some alternative test methods, which were evaluated (complexity, relevance for the mix design process, …) and improved in terms of test conditions, precision, and discriminating power. Five performance-related tests proved to have sufficient ability to discriminate between mixtures with variations in mix design. Recommendations were made for the ranges within which the laboratory test results should fall in order for the slurry surfacing to perform well.
Dogs: A Man’s Best Friend or a Deadly Beast—A Discussion on Capnocytophaga canimorsus
Authors: Muttana Swathi , Singh Christopher Solowiej , Dhami Sabina , Ntelis Spyridon , Dhami Dana , Michael Miriam
Published: April 2022

Capnocytophaga canimorsus is a catalase-positive and oxidase-positive gram-negative bacillus commonly found in dog saliva that is a rare cause of infection in immunocompromised individuals. We report the case of a 70-year-old woman with Waldenström macroglobulinemia treated with ibrutinib and a history of bilateral shoulder arthroplasty and bilateral knee arthroplasty who reported a 1-year history of multi-joint pain and swelling. The patient resides with two pet dogs that often scratch and bite, penetrating the skin, and on culture was found to have Capnocytophaga canimorsus.
Interaction of Transformer Oil Parameters on Each Other and on Transformer Health Index Using Curve Estimation Regression Method
Authors: Saeid Morteza , Zeinoddini-Meymand Hamed , Kamel Salah , Khan Baseem
Published: April 2022

Power transformers are one of the most significant and expensive equipment in power systems that are exposed to electrical, thermal, and chemical tensions. The transformer health index is a measure that uses test data and field inspections to assess the condition and determine the remaining life of the transformer. The purpose of this article as a new idea is to determine the relationships between electrical, physical, and chemical parameters of transformer oil, dissolved gases, and the transformer health index. One of the advantages of using the regression method in analyzing transformer data compared to the other methods to evaluate the transformer health index is determining the influence of the parameters that have the most impact on each other. Some achievements of this article are as follows: (1) introducing moisture content as the parameter that plays an effective role in reducing dielectric oil breakdown voltage and improving the transformer health index; (2) determining the inverse relationship between acidity and furfural components; (3) determining furfural as a parameter with the greatest role in reducing the Interfacial tension (IFT) of oil (molecular interconnection); (4) determining CO gas as the parameter with the most role in the production of furfural component; (5) determining C2H2 gas as the parameter with the most role in producing the acid component. For example, with a 1 ppm increase in the moisture component, the oil breakdown voltage decreases by 0.583 kV in the compound, growth, exponential, and logistic regressions, or with a 1 ppm increase in the furfural component, the oil interfacial tension decreases by 0.644 mN/m in power regression. In this article, the curve estimation regression method is used and the results are plotted by SPSS statistical software to analyze the interaction between different transformer parameters. To perform the simulations, test data related to 120 transformers have been considered.
Application of Internet of Things Artificial Intelligence and Knowledge Innovation System in Table Tennis Teaching and Training
Authors: Cui Yingmin , Zhou Changlei
Published: April 2022

The Internet of Things is an important link in the future development of the Internet and will have a significant impact on development over the next five years or so. The purpose of this article is to learn how to use the Internet of Things to learn and practice table tennis and find new ways to integrate table tennis and the internet. This article proposes to build a detection system for IoT sensors and collect information about the ball’s movement state and the athlete’s movement state during table tennis. Through data analysis, we better guide athletes to develop table tennis training. At the same time, it also uses knowledge innovation to integrate the Internet of Things with the reality of life and better derive the development of the Internet of Things artificial intelligence. Based on this, this paper designs the construction of IoT sensors to collect the sports status of table tennis enthusiasts and verify the sports experience of table tennis enthusiasts by comparing factors such as physical training, basic skills and mobile training, interest, and confidence. The experimental results of this article show that the Internet of Things artificial intelligence has great potential, and it can innovatively promote industrial change. It better realizes the teaching and training of table tennis players and improves the teaching and training ability of table tennis by 20%.
Notes on Upper and Lower Truncation of Picture Fuzzy Graphs
Authors: Shoaib Muhammad , Mahmood Waqas , Al-Kenani Ahmad N. , Islam Sahidul
Published: April 2022

Due to the absence of a neutral function, there are drawbacks to the existing definition of an intuitionistic fuzzy graph (Int‐FG). In that definition of Int‐FG, membership function and nonmembership function are involved. In this study, lower truncation (Low‐T) and upper truncation (Up‐T) are applied to picture fuzzy graphs (Pic‐FGs). Pic‐FG has an additional neutral membership function. Furthermore, Low‐T and Up‐T of subdivision of the picture fuzzy line graph (Pic‐FLnG) are also discussed. The degree of an edge in both Up‐T and Low‐T of Pic‐FG is discussed. Some related theorems related to the degree of Up‐T and Low‐T of Pic‐FG are discussed.
Accountable Monero System with Privacy Protection
Authors: Zhang Yifan , Xu Haixia
Published: April 2022

Monero is one of the prominent cryptocurrencies bringing robust privacy safeguard levels. However, for Monero lacking accountability, it is easy to use anonymity and privacy for committing the crime without the leakage of identity. And some exchanges are not very receptive to Monero. The purpose of this study is to balance privacy and accountability in Monero. Specifically, we studied the way to provide accountability while keeping privacy. We develop an accountable Monero model. Our model isolates three kinds of roles, users, the trusted registration authority, and a trusted regulator. Accountability enables the trusted regulator to reveal the signer’s identity. Only the trusted regulator can trace users’ public keys as needed. We give a construction for the accountable Monero system by combining CryptoNote protocol with ElGamal encryption. Our instantiation is with a marginal influence on efficiency. The security of our scheme is based on the discrete logarithm and decisional Diffie–Hellman assumption.
Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues
Authors: Alanya-Beltran Joel , Raut Roshani , Patil Sonali , Christobel Y. Angeline , Vats Prashant , Nagaprasad S. , Kumar Yekula Prasanna
Published: April 2022

The intelligent wireless system focuses on integrating with the advanced technologies like machine learning and related approaches in order to enhance the performance, productivity, and output. The implementation of machine learning approaches is mainly applied in order to enhance the efficient communication system, enable creation of variable node locations, support collection of data and information, analyze the pattern, and forecast so as to provide better services to the end users. The efficiency of using these technologies tend to lower the cost and support in deploying the resources effectively. The wireless network system tends to enhance the bandwidth, and the application of novel machine learning approaches supports detection of unrelated data and information and enables analysis of latency at each part of the communication channel. The study involves critically analyzing the key determinants of machine learning approaches in supporting enhanced intelligent network communication in the industries. The researchers are aimed at gathering both primary data and secondary data for the study. The respondents are chosen in the industry so that they can provide better inputs and insights related to the area of research. The key determinants considered for the study are machine learning-influenced management of hotspots, identification of critical congestion points, spectrum availability, and management. The analysis is made using SPSS data analysis package based on which it is noted that all the factors make major influences towards the intelligent communication, and hence machine learning supports critically in enhancing the user experience effectively.
Tracing Information for Agricultural Product and Identifying Key Regulatory Decisions towards Eco-Economics Sustainability
Authors: Ma Xinyu , Zhang Qing
Published: April 2022

Information traceability of agricultural products is of vital importance in the supply chain in the ongoing wave of big data relying on a group of advanced technologies, such as blockchain. This paper investigates how to effectively supervise and manage the agricultural supply chain with regard to information traceability. Starting from establishing a system framework for tracing and querying information by applying BigchainDB, this paper combines Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) to identify and assess the key regulatory decisions for the government for both economic and ecological purposes. The final results obtained by DEMATEL and ANP are consistent. The vital role of the planting link in eco-economics sustainability is affirmed. Through analyzing the demands of supply chain practitioners, as well as both economic and ecologic views of key points, the qualification information is attached significance. And the preservation and storage processes also deserve attention.
Contrast‐Enhanced Ultrasonography for Differential Diagnosis of Benign and Malignant Thyroid Lesions: Single‐Institutional Prospective Study of Qualitative and Quantitative CEUS Characteristics
Authors: Petrasova Hana , Slaisova Radka , Rohan Tomas , Stary Karel , Kyclova Jitka , Pavlik Tomas , Kovalcikova Petra , Kazda Tomas , Valek Vlastimil
Published: April 2022

Objectives. To extend and revise the diagnostic value of contrast‐enhanced ultrasonography (CEUS) for differentiation between malignant and benign thyroid nodules. Methods. This single‐institution prospective study aims to compare CEUS qualitative and objective quantitative parameters in benign and malignant thyroid nodules. Consecutive cohort of 100 patients was examined by CEUS, 68 out of them were further analysed in detail. All included patients underwent cytological and/or histopathological verification of the diagnosis. Results. Fifty‐five (81%) thyroid nodules were benign, and 13 (19%) were malignant. Ring enhancement pattern was strongly associated with a benign aetiology (positive predictive value 100%) and heterogeneous enhancement pattern with malignant aetiology (positive predictive value 72.7%). The shape of the TIC (time‐intensity curve) was more often identical in the benign lesion (98.2%) than in malignant lesions (69.2%), p = 0.004. Conclusions. This study indicates that CEUS enhancement patterns were significantly different in benign and malignant lesions. Ring enhancement was a very strong indicator of benign lesions, whereas heterogeneous enhancement was valuable to detect malignant lesions.
Study on the Mechanism of Bu-Shen-He-Mai Granules in Improving Renal Damage of Ageing Spontaneously Hypertensive Rats by Regulating Th17 Cell/Tregs Balance
Authors: Zhang Peng , Song Xu-Yu , Li Wen , Wei Jian-Liang , Cui Yan-Jun , Qi Ying-Zi , Chen Xiu-Bao , Jiang Yue-Hua , Yang Chuan-Hua
Published: April 2022

Aim of the Study. The present study was designed to interpret the immunoregulatory effect of Bushen Hemai (BSHM) granules to improve renal damage in ageing spontaneously hypertensive rats (SHRs). We focused on regulation of the Th17 cell/Treg balance and explored the targets of BSHM granules. Methods. Blood pressure and urine biochemical indices were recorded. Renal blood flow was evaluated by renal ultrasonography. Transmission electron microscopy (TEM) and HE staining were used to assess kidney and spleen morphology. Renal fibrosis was assessed using Masson staining. Serum levels of IL-6, IL-10, and IL-17A were measured using ELISAs. The density of RORγ and Foxp3 in the spleen was observed by immunofluorescence staining. The levels of Th17 cells and Tregs in blood were detected via flow cytometry. Transcriptome sequencing was performed to screen the targets of BSHM granules in hypertensive kidneys. Results. BSHM granules decreased SBP by 21.2 mm·Hg and DBP by 8.8 mm·Hg in ageing SHRs (P<0.05), decreased the levels of urine mALB, β2-Mg, and NAG (P<0.01), and improved renal blood flow and arteriosclerosis. BSHM granules increased IL-10 expression (P<0.05) while decreasing IL-6 (P<0.01) and IL-17A (P<0.05) levels. BSHM granules improved Foxp3 density and the number of Tregs (P<0.01) and reduced RORγt density and the number of Th17 cells (P<0.01). Transcriptome sequencing identified 747 differentially expressed (DE) mRNAs in kidneys after BSHM treatment. GO analysis suggested that BSHM granules act through immunoregulation. Conclusions. BSHM granules attenuated hypertensive renal damage in ageing SHRs, by significantly increasing Tregs and decreasing Th17 cells.
Research on Neural Network Machine Translation Model Based on Entity Tagging Improvement
Authors: Xu Xijun
Published: April 2022

Machine translation, as an efficient tool, can achieve equivalent conversion between different languages while preserving the original semantics. At present, machine translation models based on deep neural networks have become a hot research topic in the fields of natural language processing and image processing. However, the randomness of neural networks leads to the existing neural network machine translation models unable to effectively reflect the linguistic dependencies and having unsatisfactory results when dealing with long sentence sequences. To solve these two problems, a new neural network machine translation model with entity tagging improvement is proposed. First, for the low-frequency word translation problem, UNK entity tags replacement is used to compensate for the weakness of the randomness of neural networks and the encoding/decoding strategy of entity tagging is improved. Then, on the basis of the LSTM translation model, an attention mechanism is introduced to dynamically adjust the degree of influence of the context at the source language end on the target language sequence to improve the feature learning ability of the translation model in processing long sentences. The analysis of the experimental results shows that the translation evaluation index BLEU of the proposed translation model is significantly improved compared with various translation models, which verifies its effectiveness.
Longitudinal Changes of Serum Creatine Kinase and Acute Kidney Injury among Patients with Severe COVID-19
Authors: Soto-Fajardo Juan M. , Castillo-Avalos Valeria J. , Hernandez-Paredes Elisa Naomi , Santillán-Cerón Airy , Gaytan-Arocha Jorge E. , Vega-Vega Olynka , Uribe Norma , Correa-Rotter Ricardo , Ramirez-Sandoval Juan C.
Published: April 2022

Background. Acute kidney injury (AKI) is a common complication of COVID-19. Several etiologies have been identified, including pigment deposition likely associated with myopathic damage. Nevertheless, the relationship between longitudinal creatine-kinase trends and renal outcomes is uncertain. Aim. To correlate longitudinal changes in serum creatine-kinase levels with hospital-acquired AKI (beyond 48 h of hospital admission) in severe COVID-19 patients. Methods. This is a retrospective cohort study, and creatine-kinase levels were assessed over time in 1551 hospitalized patients with normal renal function at the time of hospital admission. Results. In subjects who developed hospital-acquired AKI (n = 126, 8.1%), the serum creatine-kinase concentration before AKI onset was not different when compared to patients without AKI (slope of log creatine-kinase/day = −0.09 [95% CI −0.17 to +0.19] vs. +0.03 [95% CI −0.1 to +0.1]). After AKI diagnosis, serum creatine-kinase levels showed a significantly ascendent slope (slope of log creatine-kinase/day after AKI diagnosis = +0.14; 95% CI + 0.05 to +0.3). The AKI evolution was the main factor associated with the creatine-kinase trend. Subjects with persistent AKI (n = 40, 32%) had rising creatine-kinase levels during hospitalization (slope of log creatine-kinase/day = +0.30 95% CI + 0.19 to +0.51). A rising creatine-kinase trend (n = 114, 8%) was associated with a 1.89-fold higher risk of in-hospital death (95% CI 1.14 to 3.16). Nevertheless, this association disappeared after adjusting AKI evolution and LDH baseline levels. Conclusion. In severe COVID-19 patients, a slight increase in creatine-kinase levels was observed after AKI occurrence but not before. Our results show that, at least for the appearance of hospital-acquired AKI, the CK rise does not meet the temporality criterion of causality regarding the occurrence of AKI. Rising creatine-kinase trends were associated with a higher risk of mortality, but this association was modified by AKI evolution and inflammation. There is a limited efficiency for AKI prognosis in the serial follow-up of CK levels in severe COVID-19 patients with normal renal function.
Cross-Regional Transaction Path Configuration of Renewable Energy Resources by Graph Theory-Based Transmission Cost Allocation Method
Authors: Yao Yin , Lu Dahuan , Gao Wenzhong , Zhou Bo , Li Dongdong
Published: April 2022

For the transaction path configuration of renewable energy cross-regional consumption, there are several critical problems such as the key node identification, the maximum delivery quota, and the transmission cost allocation (TCA). To solve these problems, firstly, the simplified graph model of the ultra-high-voltage (UHV) network is constructed, and the network connectivity and the vulnerability of the key nodes are analyzed from the perspective of the system topology. Secondly, the source and sink nodes are set corresponding to the electricity seller and buyer in the power market, and the Edmonds–Karp algorithm is utilized to search for the augmenting path. Also, the maximum transmission quota of the transaction path is achieved effectively and rapidly. Finally, the social welfare is set as the optimization objective, and the optimal allocation of multiple power flows in multiple feasible transaction paths is carried out. The case study was conducted based on the 17 cross-regional transactions in China including the typical Gansu-Shanghai renewable energy consumption case. Compared to the existing TCA method, the simulation result shows that the proposed method can effectively utilize the transmission potential, decrease the overall transmission cost, and provide proper economic signals.