bluuebunny/crossref_metadata_2025 huggingface dataset:
Evolution of Agricultural Innovation Ecosystem in County Areas: A Life-Cycle Perspective of Cases in Hebei Province
Authors: Hou Xiaona , Li Jianmin , Yin Shi , Yuan Haochang
Published: May 2022

The concept of agricultural innovation ecosystem in county areas was defined by reviewing the literature on agricultural innovation systems and innovation ecosystems. Combined with the urgent demand for agricultural innovation-driven development, a multidimensional agricultural innovation ecosystem was established in county areas. From the life-cycle perspective, the evolution law of agricultural innovation ecosystems in county areas was analyzed, and the evaluation system was constructed from four aspects: subjects, network, resources, and the environment. Then, the fuzzy technique for order preference by similarity to an ideal solution approach was used to determine the evolution stage of the system. It was found that the agricultural innovation ecosystem in county areas includes three ecological layers of microsubjects, meso-interfaces, and the macroenvironment. The microsubjects exchange material, energy, and information, with the macroenvironment more effectively depending on the carrier function of meso-interfaces. The system shows periodic laws in the process of spiraling evolution, gestation, growth, maturity, and recession, where different stages of the system exhibit different characteristics. Through an empirical analysis of the apple industry of Neiqiu County, Shunping County, and Yi County in Hebei province, the innovation ecosystems formed around the apple industry in the three counties were judged to be in the maturity stage, growth stage, and growth stage, respectively. The evaluation results are consistent with the actual situation, which effectively verified the scientific nature and effectiveness of the theoretical model and the evolutionary evaluation index system.
RF Fingerprint Extraction Method Based on CEEMDAN and Multidomain Joint Entropy
Authors: Wei JianYu , Yu Lu , Zhu Lei , Zhou XingYu
Published: May 2022

Specific emitter identification (SEI) can distinguish communication radio emitters with the fingerprint features carried by the received signal, and this technology has been widely used in military and civilian fields. However, in the real electromagnetic environment, the number of communication radio emitters is large and the signal-to-noise ratio (SNR) is low, which leads to poor nonlinear fingerprint analysis of SEI in a single domain. Therefore, combining the exploration of multiple domains of electromagnetic spatial information resources, this paper proposed a radio frequency (RF) fingerprint extraction method based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multidomain joint entropy. The proposed method is an attempt and exploration further extraction of nonlinear fingerprint features in multiple domains. Firstly, considering the nonstationarity of the communication signal, this article adopts the CEEMDAN method to decompose the signal to multiple intrinsic mode functions (IMF). Then, the decomposed signal is represented in multiple spaces by a multidimensional phase space reconstruction technique. Nonlinear analysis of the original signal is performed in multiple spaces: multidimensional differential approximate entropy space, singular spectral entropy space, and power spectral entropy space. Finally, the support vector machine (SVM) is adopted in the classification stage. To demonstrate the robustness of the method, the method is verified on the universal software radio peripheral (USRP) dataset and the Northeastern University public dataset. In terms of the identification accuracy, the proposed method performs with 98.5% accuracy on the 5-class USRP dataset. It also performs with 94.7% accuracy on the 16-class public dataset. The experimental results show that the proposed method has a stable identification performance and has a more than 85% recognition rate in the SNR above 5dB.
A Method for Classifying Information in Education Policy Texts Based on an Improved Attention Mechanism Model
Authors: Rao Yutai , Yang Fan
Published: May 2022

Text classification techniques in natural language processing can automatically classify text data in a more efficient way, saving human resources and costs. Therefore, text classification techniques can be applied to the automatic classification of education policy data to quickly locate and accurately find education policy data, thus realising the information management and visual analysis of education policy data. This paper proposes a text classification algorithm based on the attention mechanism of headline and body text according to the characteristics of education policy and introduces this algorithm in detail through a model diagram. The experimental comparison with existing classification algorithms verifies the superiority of the algorithm in the classification method of education policy text classification information.
Design of Digital Platform for College Innovation and Carving Out Cultivation Project Based on Intelligent Analysis
Authors: Cai Qing
Published: May 2022

With the gradual integration of the Internet into people’s daily life and the increasing number of students and organizations joining the Internet wave, more and more people begin to carry out entrepreneurial activities by taking advantage of the rich resources, sharing, and communication characteristics of the Internet. No matter ordinary people or outstanding social elites, they can develop their own innovation and creativity at all levels under the influence and role of Internet informatization. University students’ innovation and carving out are one of the vital methods to alleviate the strain of social employment and industrial transformation and development; however, the lack of entrepreneurial high-quality and bad integration of social sources also has an effect on the success of university students’ innovation and carving out. To resolve the above problems, this article constructs a digital platform for innovation and carving out cultivation undertaking in schools and universities primarily based on smart analysis. Digital-wise evaluation can successfully promote the data administration of the platform, overcome the inherent troubles such as old-fashioned records sources and gradual updating of regular platforms, make full use of the advantages of large data, combine scan and theory, and assemble a remarkable varied ecological environment. Within the functional scope of the platform, provide innovation and carving out information resources for users in need, enhance the clever processing stage of innovation and carving out facts in schools, recognize the adaptive scheduling of innovation and carving out facts, minimize the work depth of innovation and carving out records managers, enhance the deployment effectivity of innovation and carving out data in faculties and universities, and domesticate the innovation and carving out capability of university students in an all-round way. Promote the innovation and improvement of innovation and carving out things to do in universities.
A Lamus-Based Flight Data Sharing Model on Consortium Blockchain
Authors: Li Fengyin , Cui Yang , Huang Baogui , Yu Siqi , Liu Peiyu , Wang Yilei , Li Tao
Published: May 2022

Currently, traditional flight data sharing models cannot resist quantum attacks, which poses the risk of data leakage. The research on the flight data sharing model against quantum attack has become one of the research hotspots. Lattice-based cryptography is recognized as an effective way to resist quantum attacks. A flight data sharing model on consortium blockchain is proposed in this paper to resolve data leakage during data sharing. First, a new lattice-based multisignature scheme (Lamus) is proposed, capable of resisting quantum attacks. We prove the security of the proposed Lamus scheme in the random oracle model. Moreover, a flight data sharing model on consortium blockchain is proposed by applying the proposed Lamus scheme to resist quantum attacks. Security and performance analysis show that the model guarantees antiquantum security, and it achieves good performance in terms of storage efficiency and operating efficiency.
Entropy Minimization on Sutterby Nanofluid past a Stretching Surface with Swimming of Gyrotactic Microorganisms and Nanoparticles
Authors: Ali F. , Loganathan K. , Prabu E. , Eswaramoorthi S. , Faizan M. , Zaib A. , Chaudhary Dinesh Kumar
Published: May 2022

This analysis examines the flow of Sutterby nanofluid having bioconvection due to a stretching sheet and heated convective boundary condition. The heat sink/source was imposed in terms of the energy equation. The entropy minimization is also considered. Through the appropriate transformation of the system of nonlinear PDE’s setting, the existing problem has altered into nonlinear ODEs and then numerically utilized via the bvp4c method. The numerical values of skin friction, Nusselt number, Sherwood number, and motile density profiles are revealed in a tabular form. In comparison, other governing variables on velocity, temperature, concentration, and bioconvection are seen through various plots and discussed. For example, increment in the Deborah number of heat flux depreciated in temperature distribution while opposite observation was carried out for heat source and sink parameter. Moreover, it has also been investigated that the shear-thinning fluid is entirely reverse to that of the shear thickening fluid. Further, the increase in the magnetic number accelerates in the Bejan number and irreversibility ratio. Finally, the comparison has been made with previous literature and found an excellent agreement.
Green Supply Chain Decision-Making considering Retailer’s Fairness Concerns and Government Subsidy Policy
Authors: Shi Wei , Hu Jiawei , Sun Suqin , Meng Qiang , Zhang Xuemei
Published: May 2022

Government’s green subsidy and retailer’s fairness concerns have great implications for enterprise’s operation strategy in the green supply chain (GSC). With the continuous deepening of retailer’s participation in supply chain management, the green services they provided by retailer have become a crucial role in promoting the terminal sales of green products. To further research the government subsidies and retailer’s fairness concerns on the optimal decisions of product pricing, green R&D, and service level, we construct four two-stage GSC models: no subsidy and fairness concerns, subsidizes manufacturer without retailer’s fairness concerns, subsidizes manufacturer with retailer’s fairness concerns, and subsidizes all members with retailer’s fairness concerns. The results show that subsidizes to manufacturers has significantly improved supply chain performance and environmental governance, but it exacerbates the unfair distribution of profits among members, and retailers’ fairness concerns drive them to offer lower green service level. With the green demand of consumers being unable to be fully satisfied, the consumer surplus and effectiveness of government environmental governance decrease accordingly. To eliminate the adverse effect caused by unfair distribution of profits, it is necessary to subsidize retailers so as to share their green service costs and increase their share of profits.
Removal of Safranin-T and Toluidine from Water through Gum Arabic/Acrylamide Hydrogel
Authors: Jabeen Salma , Alam Sultan , Shah Luqman Ali , Zahoor Muhammad , Rahman Najeeb Ur , Khan Farhat Ali , Ullah Riaz , Ali Essam A. , Murthy H. C. Ananda , Sohail Aamir
Published: May 2022

Hydrogels as “smart sorbents” for wastewater treatment have attracted much attention due to their facile fabrication, rapid regeneration, environment friendly nature, and strong interaction with pollutants. In this study, gum arabic/acrylamide (GA/AM) hydrogel was developed via the free radical polymerization method by employing acrylamide (AM) (monomer), gum arabic (GA) (grafting backbone), N,N-methylenebisacrylamide (MBA) (chemical crosslinker), and tetramethylethylenediamine (TEMED) (accelerator). The fabricated adsorbent was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and surface area analyzer. The adsorption properties of the subject hydrogel were explored against cationic safranin and toluidine dyes in aqueous media. The point of zero charge (PZC) for the GA/AM sorbent was found to be [Formula: see text] whereas maximum sorption occurred at pH 11. Different kinetic and isotherm models were applied to evaluate the adsorption mechanism and estimate values of different adsorption parameters. The adsorption isotherm and kinetics were better explained by the Langmuir isotherm and pseudo-second-order kinetic model whereas the adsorption thermodynamics depicted the endothermic, spontaneous, and favorable nature of the process. The adsorbent was regenerated with acetone and reused for the selected dyes for many cycles. After the 5th cycle, the hydrogel retained safranin/toluidine removal [Formula: see text] which pointed toward the reusability of the prepared adsorbent for cycles without appreciable reduction in its adsorption capacity. Hence, the GA/AM sorbent can be applied as an alternative of activated carbon to treat dye-contaminated waters.
Smart Memory Storage Solution and Elderly Oriented Smart Equipment Design under Deep Learning
Authors: Gong Chen , Liu Runze , Zhou Nan , Luo Jidong , Kumar Jain Deepak
Published: May 2022

This study explores the memory characteristics of elderly individuals to design effective smart devices based on smart memory storage solutions under deep learning to improve the learning efficiency of elderly individuals. The different memory formation stages in the existing human brain are analysed. A smart memory storage solution based on memory-enhanced embedded learning is constructed based on meta-learning under deep learning, which reduces the cost of learning new tasks to the greatest extent. Finally, the performance of the proposed solution is verified using different datasets. The results reveal that the solution based on deep learning has obvious effects on different datasets, with an average accuracy rate of 99.7%. By synthesizing a large number of target sample features, this solution can lower the learning difficulty and improve the learning effect. The proposed elderly oriented smart device effectively reduces the shortcomings in the current market and lowers the learning difficulty, which provides an important reference for further enriching devices in the ageing market.
Considering the Characteristics of Traffic Risk Factors and the Method of Establishing a Flexible Traffic System
Authors: Chen Lingling , Zhang Yuanyuan
Published: May 2022

With the acceleration of urbanization and the development of the automobile industry, the contradiction between the traffic capacity of existing urban roads and the growing traffic demand has become increasingly acute. Traffic congestion is becoming increasingly prominent. The purpose of this article is to consider the characteristics of traffic risk factors and to study the method of establishing a flexible traffic system. It can relieve traffic congestion and provide a smooth and orderly traffic environment using intelligent transportation systems to control and direct traffic flow. Based on the large urban road network, this research uses the theory of coordinated control and learning mechanism, fuzzy control, dynamic reprogramming, and other theories to study phase sequence, balance peripheral load, and overall traffic flow. It also uses on-board sensors to optimize the collection and processing of network information, decompose traffic guidance work, select the optimal route, and autonomously guide the intelligent transportation system. Under the flexible demand scheme, the average load of the trunk road is reduced by a larger degree, which is 4% lower than that of the fixed demand scheme. At the same time, the average load of the branch has increased more, which is 4% higher than that of the fixed demand scheme. It can be seen that under the elastic demand scheme, the distribution of traffic flow in the road network is more balanced and the optimization effect of relieving traffic pressure on trunk roads and improving the utilization rate of branch roads is more significant.
Research on the Optimization of Basketball Teaching Content of Physical Education Major in Artificial Intelligence Colleges and Universities
Authors: Qiao Rongtong , Ren Jie , Ji Yuguang
Published: May 2022

The article uses the literature material law, expert interview method, mathematical statistics, the author of Suzhou University, Chengdu research, education of Guangdong Institute of Education, Guangdong teachers college education schools such as the current sports education professional study, and the similarities and differences in teaching outline of education of Guangdong province basketball specialty to conduct a comprehensive study and analysis and comparison on the current situation of education of Guangdong teachers college basketball in the system. This paper expounds the Guangdong education normal study and the Guangdong education status of school physical education professional basketball specialty courses, for Guangdong teachers college education reform of physical education professional basketball specializing in research, and the Guangdong education for theory reference.
Big Data Energy Scheduling Game Management Algorithm Based on Dual Carbon Goals
Authors: Zhang Qian , Wu Minghui
Published: May 2022

“Carbon peak and neutrality” are an important strategic decision to promote the transformation of China’s energy economy and build a community with a shared future for mankind. China is a big energy consumer, and the whole society is facing huge challenges under the goal of carbon peak and neutrality. The realization of the goal of carbon peak and neutrality requires the guidance of correct theoretical methods and scientific deployment. This paper mainly studies the energy consumption scheduling, demand response management, and energy trading problems of microgrid. In view of the shortcomings of the existing energy optimization scheduling methods in the microgrid, a variety of energy resources such as electric energy, natural gas, heat energy, and cold energy are considered into the microgrid model. Based on the noncooperative game and Stackelberg game, a new energy optimal dispatch model is constructed with a variety of game methods such as two-layer game. Maximize the personal benefits of the microgrid while meeting the reliable operation of the system and the electricity demand of users. The three-stage noncooperative game problem is solved based on the reverse bootstrap method, and the closed expression of the optimal strategy in each stage is obtained. The power generation forecasting technology based on big data is studied, and a power forecasting method is proposed, which can effectively guide the energy consumption of the microgrid. The simulation results show the effectiveness of the proposed renewable energy management model based on big data, which verifies that the accurate wind power prediction results are conducive to better theoretical analysis of energy management.
Optimization Algorithm for Multipath Transmission of Distance Education Resources Using Reinforcement Learning
Authors: Yajun Wang , Liang Zhao
Published: May 2022

Distance education resources are the essential components of modern distance learning systems, and the development of high-quality resources is a potent assurance of modern distance education quality. The standard transmission efficiency assessment approach ignores the optimal data transmission stop rule and is unable to calculate the ideal stop time, resulting in excessive data transmission energy consumption and an evaluation result that does not match the real value. As a result, this study provides a reinforcement learning-based optimization technique for multipath transmission of distance education materials, because to calculate the optimal transmission rate thresholds in different detection time periods and realize the optimization of resource data transmission efficiency. According to the simulation result, the suggested efficiency optimization approach uses less energy on average and transmits data at a faster pace, improving resource data transmission efficiency. The result also shows that the proposed method performs well in terms of packet loss as compared to other existing methods.
Antioxidant Effects of Curcumin Gel in Experimental Induced Diabetes and Periodontitis in Rats
Authors: Mohammad Chenar Anwar , Ali Khadeeja Mohammed , Sha Aram Mohammed , Gul Sarhang Sarwat
Published: May 2022

This study aimed to evaluate the effect of curcumin gel on antioxidant marker level in experimental induced diabetes and periodontitis (EDP) in rats. Adult Wistar rats were randomized into five groups (20 each): (1) EDP treated with scaling and root planing (SRP) + curcumin gel (CU), (2) EDP treated with CU, (3) EDP treated with SRP, (4) EDP without treatment, and (5) systemically healthy and without ligature (control). Each group was subdivided equally into 4 subgroups of 5 rats. Diabetes was induced by intraperitoneal injection of streptozotocin (STZ), and periodontitis was induced by a ligature. Blood samples were collected by cardiac puncture at 0, 7, 14, and 21 days to assess oxidative stress of malondialdehyde (MDA) and antioxidant enzymes of glutathione peroxidase (GPx), catalase (CAT), and suproxidase dismutase (SOD) levels. The results showed a significant increase in serum MDA and antioxidant enzyme levels in the untreated EDP group compared to the control group (p < 0.05). The adjuvant use of CU to SRP resulted in a significant reduction of MDA and CAT levels as compared to the SRP group (p < 0.05); however, significant reduction of GPX and SOD levels can be found only at day 7. It can be concluded that the decreased level of antioxidant enzymes can be construed as a result of decreased oxidative stress by curcumin therapy.
TRIP13 Induces Nedaplatin Resistance in Esophageal Squamous Cell Carcinoma by Enhancing Repair of DNA Damage and Inhibiting Apoptosis
Authors: Zhang Lin-Ting , Ke Li-Xin , Wu Xin-Yi , Tian Hui-Ting , Deng Hua-Zhen , Xu Li-Yan , Li En-Min , Long Lin
Published: May 2022

Thyroid hormone receptor interactor 13 (TRIP13) plays a crucial role in poor prognosis and chemotherapy resistance of cancer patients. This present study is aimed at investigating the role of high expression of TRIP13 inducing nedaplatin (NDP) resistance in esophageal squamous cell carcinoma (ESCC) cells. High expression of TRIP13 promoted the proliferation and migration of ESCC cells performed by MTS assay, colony formation assay, wound healing assay, and transwell assay. High TRIP13 expression induced NDP resistance to ESCC based on the cell proliferation promoting/inhibition rate and cell migration promoting/inhibition rate analysis, flow cytometry assay of apoptotic subpopulations with a combination of Annexin V‐FITC and propidium iodide, and Western blot analysis downregulating cleaved PARP, γH2A.X, cleaved caspase‐3, and Bax and upregulating Bcl‐2 expression. This study indicated that high expression of TRIP13 promoted proliferation and migration of ESCC cells and induced NDP resistance via enhancing repair of DNA damage and inhibiting apoptosis. This will provide a preliminary reference for the clinical use of NDP in ESCC treatment.
Research on the Application of a Universal Design Model for Urban Healthy Living Based on Artificial Intelligence Computing
Authors: Zhang Wei , Li Jingliang , Wang Xiaoxiao
Published: May 2022

Along with the continuous improvement of living standard, people gradually improve the design quality of urban living space and require the living space to meet the standard of health design concept. This paper mainly focuses on the health concept of urban living space design, briefly elaborates on the health design concept of living space, and designs a soft furnishing layout model based on DBN according to the advantages of deep confidence network compared with BP neural network. The effect of the two models in the practical application of soft furnishing layout is explored, which is very important for the practical application.
Multiobjectives for Optimal Geographic Routing in IoT Health Care System
Authors: Aravind K. , Maddikunta Praveen Kumar Reddy
Published: May 2022

In numerous internet of things (IoT) appliances, messages might require to be distributed to certain specified nodes or objects with the multicast transmission. “The multicast routing protocol can be divided into nongeographic based and geographic based.” As locations of device are roughly extracted by GPS devices, geographic‐oriented multicast routing schemes were chosen, because it induces lesser overheads. Nevertheless, the extant geographic‐oriented routing models are found to have particular disadvantages. After the advent of the IoT systems for remote healthcare, medical services can be rapidly provided to patients in rural areas. The IoT network encapsulates flexible sensors in the environment to collect environmental information. This gathered sensor information is sent to the nursing stations for timely medical assistance. The IoT network is wireless, which leads to security breaches. Therefore, there is a necessity to have a secured data transmission in the context of healthcare. Hence, this study intends to propose a novel optimal route selection model in IoT healthcare by deploying optimized ANFIS. Here, the optimal routes for medical data are selected using a new self‐adaptive jellyfish search optimizer (SA‐JSO) that is the enhanced edition of the extant JSO model. Accordingly, the optimal route selection for medical data is performed under the consideration of “energy, distance, delay, overhead, trust, quality of service (QoS), and security (high risk, low risk, and medium risk).” In the end, the performances of adopted work are compared and proved over other extant schemes.
[Retracted] Athletes’ Ankle Injury Features and Rehabilitation Methods Based on Internet Big Data
Authors: Li Xinke , Kang Meisheng
Published: May 2022

With the development of science and technology and the improvement of living standards, people are paying more and more attention to sports and health. However, the foot joints are very important joints in the human body and are one of the most vulnerable parts in sports. Foot joint sprain is a common clinical disease in orthopedics, accounting for the first place of joint ligament sprain. If the treatment is not timely or appropriate, it often leaves pain and joint instability, followed by osteoarthritis. The rate of repeated injuries after the first injury is very high. Due to the rapid development of visual sensing technology, computer technology, and image processing technology, Internet big data has been widely used in food, medical treatment, construction, chemical industry, electronics, packaging, automobile, and other fields. This article mainly studies the characteristics and rehabilitation methods of athletes’ ankle injuries based on Internet big data. Based on Internet big data, this paper studies the function and structural stability of the foot joints and analyzes the causes of gymnast foot joint injuries. On this basis, this paper puts forward the treatment method of ankle joint injury. In this paper, 20 male competitive bodybuilders with ankle joint injury on one side were randomly divided into experimental group and control group. The experimental group received proprioception recovery training, and the control group received normal functional recovery training. Experimental results show that the proprioception training of foot joint can improve the dynamic balance ability of gymnasts and effectively prevent the occurrence of foot joint injury. In this paper, the stability of the anterior and posterior foot joints was measured by comparing the control group with the experimental group. After 6 weeks of proprioception training, there was no significant difference between the static balance ability of one foot and the healthy part of the injured side foot joints (P > 0.05).
Exploration of Stock Portfolio Investment Construction Using Deep Learning Neural Network
Authors: Xie Zizheng , Wang Yi
Published: May 2022

To study the intelligent and efficient stock portfolio in China’s financial market, based on the relevant theories such as deep learning (DL) neural network (NN) and stock portfolio, this study selects 111 stable stocks from the constituent stocks of the China Security Index (CSI) 300 from January 1, 2018, to December 31, 2021, as the research samples. Then, it analyzes these research samples and imports the relevant data of 111 stocks into the DL NN model. The corresponding prediction results of stock prices are obtained. Finally, the stock portfolio model based on DL NN is compared with the data results of the Shanghai Stock Exchange (SSE) 50 Index and CSI 500 Index. The results show that the closing prices of the selected 111 stocks are relatively stable and fluctuate up and down around the horizontal axis, and the positive and negative returns are relatively balanced, roughly between −5% and 5%. There is a phenomenon of fluctuation aggregation to a certain extent. Comparing the prediction results of different models reveals that the prediction results of model c are closest to the actual stock price trend. Comparing the relevant returns of the proposed stock portfolio with other stocks uncovers that the annualized return of the stock portfolio based on the DL NN model is 47.44%. The sharp ratio is 1.52, the maximum pullback is 18.15%, the monthly excess return is 3.11%, and the information ratio is 0.82. Compared with other indexes, the proposed stock portfolio shows the best results. Therefore, the proposal of the stock portfolio based on DL NN provides a theoretical basis for the development of the financial field in the future.
A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data
Authors: Yem Souhe Felix Ghislain , Boum Alexandre Teplaira , Ele Pierre , Mbey Camille Franklin , Foba Kakeu Vinny Junior
Published: May 2022

Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables considered in this article are the voltage module at the measurement nodes. The results obtained show that the proposed hybrid algorithm has better performance compared to single state estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the weighted least squares (WLS) method.
Risk Adapting Stock Trading System Associated Technical with Macroeconomic Indicators
Authors: Wang Qinan
Published: May 2022

Machine learning has emerged as a powerful tool in studying the behavior of stock movement. However, it has yet to be highly accurate due to market randomness. This article aims to improve stock movement classification accuracy by addressing macroeconomic factors, which have been neglected in previous machine learning stock prediction studies. Hence, we propose a Risk Adapting Stock Trading System (RAST) using both technical and macroeconomic indicators. The simulated trading result of the system presented here proves that a combination of these two types of indicators is more effective than only using technical indicators when associated with machine learning.
An Inclusive Entrepreneurial Path Model Based on Rural Digital Entrepreneurship Data in Zhejiang Province Using Few-Shot Learning
Authors: Meng Xiangmin , Zhang Jie , Sun Min
Published: May 2022

The main strategic direction of promoting rural revitalization is to achieve high-quality rural development, maintain the dominant position of farmers, and promote common prosperity of farmers. In recent years, the sustainable development of economy has raised farmers’ awareness of starting their own businesses, and their entrepreneurial enthusiasm has been heating up. Based on the empirical survey data of typical rural areas in the Zhejiang Province and structural equation model analysis technology, this paper makes an empirical analysis of the relationship among network embeddedness, entrepreneurial resources, and entrepreneurial ability. The results show that knowledge resources have a significant negative impact on farmers’ inclusive entrepreneurial behavior. The dual embedment of social network and industrial network has a significant positive impact on migrant workers’ access to knowledge resources and operational resources needed for entrepreneurship, and the entrepreneurial ability of migrant workers mainly depends on operational resources. Under the background of common prosperity, we should continue to implement the policy of supporting farmers’ inclusive entrepreneurship and encouraging industrial and commercial capital to enter rural areas, and at the same time, provide rural areas with agricultural knowledge, agricultural skills, and market guidance to promote employment.
Online English Teaching Quality Assessment Based on K-Means and Improved SSD Algorithm
Authors: Dai Yuhua
Published: May 2022

Classroom teaching quality is a key content to measure the teaching level, and the teaching effect can be intuitively reflected from the students’ listening state. In order to improve the teaching quality, this paper proposes an online English teaching quality evaluation model based on K-means and an improved SSD algorithm. In the SSD algorithm, the backbone network is replaced by DenseNet with a dense connection to improve detection accuracy. The network structure of quadratic regression is designed to solve the problem of unbalance between positive and negative samples in the default box of the candidate region. A feature graph scaling method is used to fuse feature graphs without introducing additional parameters. The number of default boxes and the optimal aspect ratio were obtained by k-means clustering analysis. Finally, the state of students in the teaching process is predicted through the dual-mode recognition model of facial expression and posture, and the state of students in class is judged. Experimental comparison and analysis were conducted on the public data set and a self-built classroom teaching video data set. Experiments show that compared with other comparison algorithms, the algorithm presented in this paper performs better in the index of detection accuracy.
[Retracted] Growth Response in Oryctolagus cuniculus to Selenium Toxicity Exposure Ameliorated with Vitamin E
Authors: Rehman Rukhshanda , Sial Nuzhat , Ismail Amina , Hussain Shabir , Abid Sobia , Javed Maryium , Nadeem Khansa , Ayoub Muhammad
Published: May 2022

The adverse impacts of high temperature during the summer season on the rabbit industry have gained increased global attention. In this study, the comparative effects of biological (BIO) and chemical (CH) nanoselenium (nano‐Se) combined with vitamin E on the growth and immune performances of rabbits were observed. A total of 200 white male rabbits of similar age (90 days) were divided into five treatment groups (T0, T1, T2, T3, and T4), 40 animals in each treatment. The rabbits in the first treatment group (T0) was fed basal diet; (T1) basal diet supplemented with 35 mg biological synthesized nanoselenium/kg diet; (T2) basal diet with 35 mg biological nanoselenium/kg diet+150 mg Vit. E/kg; (T3) basal diet+35 m chemically synthesized nanoselenium/kg diet; and (T4) basal diet+35 mg of chemical nanoselenium/kg diet+150 mg Vit. E/kg. The duration of this experiment was 63 days. The body weight of each rabbit was recorded weekly. Results revealed a significant (P < 0.05) increase in live body weight (LBW), total body gain (TBG), and feed conversion ratio (FCR) of rabbits treated with BIO‐Se+Vit. E (T2) compared to the other groups. Selenium concentrations in the kidneys and liver were significantly higher (P < 0.05) in animals fed with BIO‐Se+Vit. E (T2). The concentrations of serum urea, glutamyl transferase (GGT), and triglycerides (TG) were lower in untreated (T0) and treated groups (T1, T2, T3, and T4). From the results of this study, it can be concluded that biological nano‐Se gave maximum improvement for the parameters under study compared to the chemically synthesized nanoselenium by playing a role in alleviating heat stress, increasing the growth performance, and enhancing the immunity of growing white male rabbits. Further addition of Vit. E is an alternative method to maximize productivity with no adverse effects during the fattening period of growing white male rabbits.
A Treatment to Cure Diabetes Using Plant-Based Drug Discovery
Authors: Mahankali Sumalatha , Kalava Jagadish , Garapati Yugandhar , Domathoti Bullarao , Maddumala Venkata rao , Sundramurty Venkatesa Prabhu
Published: May 2022

The field of peptides and proteins has opened up new doors for plant-based medication development because of analytical breakthroughs. Enzymatic breakdown of plant-specific proteins yields bioactive peptides. These plant-based proteins and peptides, in addition to their in vitro and vivo outcomes for diabetes treatment, are discussed in this study. The secondary metabolites of vegetation can interfere with the extraction, separation, characterization, and commercialization of plant proteins through the pharmaceutical industry. Glucose-lowering diabetic peptides are a hot commodity. For a wide range of illnesses, bioactive peptides from flora can offer up new avenues for the development of cost-effective therapy options.
Using a Machine Learning Approach to Predict the Thailand Underground Train’s Passenger
Authors: Kusonkhum Wuttipong , Srinavin Korb , Leungbootnak Narong , Chaitongrat Tanayut
Published: May 2022

In today’s world, data has become an asset for businesses. Many sectors use data technology to advance their businesses. Building management is one of the processes on which numerous studies have been conducted to assist building users. Thailand has progressed in terms of transportation infrastructure and public transportation. The Metropolitan Rapid Transit (MRT) system has more than one hundred million users per year. However, crowding is a concern in the present since crowding creates a problem and reduces customer pleasure. The goal of this research is to create a machine learning model for forecasting passenger demand over time. In addition, standard data collecting equipment was used to collect data from the Metropolitan Rapid Transit (MRT) Purple Line. This line has a total of 16 stations. Station name, date, day, month, period, number of passengers, holidays, weekends, and weather are among the nine factors. Analysis approaches included the analysis phase, classification, and regression algorithm. However, the regression algorithm’s accuracy is poor and therefore cannot be used. Before using machine learning classification methods, the K-means was used to cluster the types of passengers. In addition, for this investigation, three classification methods were used: artificial neural network, random forest, and decision tree. Furthermore, the findings revealed that the artificial neural network has a high predicting accuracy. The accuracy value stated is more than 0.85 for demand over time.
Development of a Bile Acid-Related Gene Signature for Predicting Survival in Patients with Hepatocellular Carcinoma
Authors: Wang Gang , Guan Jun , Yang Qin , Wu Fengtian , Shao Junwei , Zhou Qihui , Guo Zixuan , Ren Yanli , Zhu Haihong , Chen Zhi
Published: May 2022

Background. Hepatocellular carcinoma (HCC) is one of the most common diseases that threaten millions of lives annually. Evidence supports that bile acid (BA) affects HCC through inflammation, DNA damage, or other mechanisms. Methods. A total of 127 BA-associated genes were analyzed in HCC tumor and nontumor samples using The Cancer Genome Atlas data. Genes correlated to the prognosis of patients with HCC were identified using univariate and multivariate Cox regression analyses. Furthermore, a prediction model with identified genes was constructed to evaluate the risk of patients with HCC for prognosis. Results. Out of 26 genes with differential expressions between the HCC and nontumor samples, 19 and 7 genes showed upregulated and downregulated expressions, respectively. Three genes, NPC1, ABCC1, and SLC51B, were extrapolated to construct a prediction model for the prognosis of patients with HCC. Conclusion. The three-gene prediction model was more reliable than the pathological staging characters of the tumor for the prognosis and survival of patients with HCC. In addition, the upregulated genes facilitating the transport of BAs are associated with poor prognosis of patients with HCC, and genes of de novo synthesis of BAs benefit patients with HCC.
Innovative Application of Big Data Combined with Machine Learning in Education and Training Product Marketing
Authors: Huang Dongmei
Published: May 2022

Education and training (EAT) enterprise C is taken as an example to enable EAT enterprises to choose appropriate marketing strategies according to their own characteristics to improve their competitiveness. The marketing of its EAT products is taken as the research object to analyze its marketing philosophy and marketing behavior. Initially, the marketing environment of EAT products is analyzed from macro aspects such as political environment, economic environment, and internal environment of enterprises and micro aspects such as peer competition, alternative competition, and bargaining power of buyers. Then, the main problems existing in the marketing of EAT products of enterprise C are analyzed through a questionnaire survey. Afterward, the model analyzes the effectiveness of marketing revenue and output of enterprise C. The result shows that 48% of the teachers are dissatisfied with the EAT products of enterprise C, 59% of the students dislike the training courses of enterprise C, and only 11% of the students express their willingness to continue studying the training courses of enterprise C. To solve the above problems, enterprise C can increase investment in EAT products or develop new EAT products to optimize the product marketing strategy. Additionally, it can develop agent channels rationally and adopt multichannel integration to improve the efficiency of channel strategy. Meanwhile, the new media platform can broaden marketing channels to push targeted content to users according to big data analysis results, thereby improving user experience. The marketing model provided here has referencing value as well as important theoretical and practical significance for the development of EAT enterprises.
A Biomechanical Study on Failed Snatch Based on the Human and Bar Combination Barycenter
Authors: Liu Gongju , Zhu Houwei , Ma Jing , Pan Huiju , Pan Xu , Zhang Yingyue , Hu Ting , Fekete Gusztáv , Guo Haiying , Liang Minjun
Published: May 2022

Objective. The purpose of this research was to use a new method the human and bar combination barycenter to exposit the differences between successful and failed characteristics of snatch attempts in competition. Try to establish an effective biomechanical method that can uncover the main factors for the failed snatch. The obtained results will provide valuable information for weightlifters to improve the success rate in snatch by altering their technical issues accordingly. Methods. A 3-D video analysis method was used to compare the characteristics of the heaviest successful and failed attempts of ten elite weightlifters in the men’s 73 kg category. The video was captured under competitive conditions at the 2019 World Weightlifting Championships, the 2019 Asian Weightlifting Championships, and the 2020 China Olympic Trial. The video data were digitized using the SIMI°Motion7.50 3-D system (Germany). Results. Significant difference (

P
>
0.05

) was not found between the successful and failed attempts in the parameters, such as the maximal vertical rising velocity, the maximal vertical height, and the vertical displacement of the barbell. The maximal descending acceleration of the human body, the time duration, the angles of the hip, and knee joints were no significant difference. However, significant differences were found in the variation of the human and bar combination barycenter on the

X

-axis in the inertial ascent stage and the squat support stage (

t
=
2.862
,
P
<
0.05
;
t
=
3.376
,
P
<
0.05

). Conclusions. A probable cause of the failed snatch is that the displacement of human and bar combination barycenter on the

X

-axis is not enough to reach the position for supporting barbell during the inertial ascent stage and the squat support stage. The reason is that the strength of reclining of torso at the end of the force phase is insufficient. Insufficient knee flexion in the knee flexion phase (

M
2

), which leads to a lower maximum vertical velocity of barbell, may be an indirect factor leading to the failed snatch. The cumulative variation of human and bar combination barycenter on the

X

-axis can effectively determine the technical characteristics between the success and failure in snatch.
Integrated Analysis of Multiple Microarray Studies to Identify Core Gene‐Expression Signatures Involved in Tubulointerstitial Injury in Diabetic Nephropathy
Authors: Zhou Huandi , Yang Zhifen , Mu Lin , Shi Yonghong
Published: May 2022

Diabetic nephropathy is a leading cause of end‐stage renal disease in both developed and developing countries. It is lack of specific diagnosis, and the pathogenesis remains unclarified in diabetic nephropathy, following the unsatisfactory effects of existing treatments. Therefore, it is very meaningful to find biomarkers with high specificity and potential targets. Two datasets, GSE30529 and GSE47184 from GEO based on diabetic nephropathy tubular samples, were downloaded and merged after batch effect removal. A total of 545 different expression genes screened with log2FC > 0.5 were weighted gene coexpression correlation network analysis, and green module and blue module were identified. The results of KEGG analyses both in green module and GSEA analysis showed the same two enriched pathway, focal adhesion and viral myocarditis. Based on the intersection among WGCNA focal adhesion/Viral myocarditis, GSEA focal adhesion/viral myocarditis, and PPI network, 17 core genes, ACTN1, CAV1, PRKCB, PDGFRA, COL1A2, COL6A3, RHOA, VWF, FN1, HLA‐F, HLA‐DPB1, ITGB2, HLA‐DRA, HLA‐DMA, HLA‐DPA1, HLA‐B, and HLA‐DMB, were identified as potential biomarkers in diabetic tubulointerstitial injury and were further validated externally for expression at GSE99325 and GSE104954 and clinical feature at nephroseq V5 online platform. CMap analysis suggested that two compounds, LY‐294002 and bufexamac, may be new insights for therapeutics of diabetic tubulointerstitial injury. Conclusively, it was raised that a series of core genes may be as potential biomarkers for diagnosis and two prospective compounds.