bluuebunny/crossref_metadata_2025 huggingface dataset:
Cyber Intrusion Detection Based on a Mutative Scale Chaotic Bat Algorithm with Backpropagation Neural Network
Authors: Xu Xuebin , Qin Hu , Zhou Jie
Published: September 2022

In the current complex cyber environment, malicious attacks take various forms and constantly change. To overcome the problems of low detection efficiency and high false alarm rate in traditional intrusion detection techniques, a new method of network intrusion detection combining the variable scale chaos bat (MSCB) algorithm and BP neural network (BPNN) called MSCB-BPNN is proposed in this paper. In the proposed approach, a new MSCB algorithm is proposed to improve the BPNN by optimizing its thresholds and weights so as to prevent it from falling into local optimum. To verify the practical classification performance of the proposed method for intrusion detection, some experiments are carried out. In these experiments, the proposed approach is compared with BPNN, SA-BPNN, and GA-BPNN on the benchmark intrusion detection datasets KDD cup 99 and UNSW-NB15. The experimental results show that the accuracy of the proposed method can reach 99.4% and 99.8% accuracy on the KDD cup 99 training and test sets, respectively, while 89.0% and 93.9% accuracy on the UNSW-NB15 training and test sets, respectively, which are higher than BPNN, SA-BPNN, and GA-BPNN. Furthermore, the precision and recall ratio of MSCB-BPNN are also superior to other approaches. Thus, the proposed model has significant advantages over BPNN, SA-BPNN, and GA-BPNN methods.
Approaches and Methods of Music Education Innovation Based on Digital Image Technology
Authors: Weisheng Luo , Hui Hong
Published: September 2022

Image processing technology can develop appropriate interactions and functions according to different scenarios, enhance the immersion and participation of online teaching, comprehensively improve the quality of teaching and learning, provide technical support for the improvement of online learning, and play an important role in music teaching. School music courses can broaden students' thinking, broaden their horizons, enrich their cultural connotation, and enable them to have a deep cultural heritage. With the deepening of education reform, the demand for talents is no longer single but requires talents to develop in an all-around way to meet the needs of social development. As an art education, music can lead students to touch and understand beauty, which has a positive impact on the cultivation of students’ aesthetic consciousness and the spread of personal thinking. Therefore, it is necessary to have an innovative consciousness of music education, so that students' physical and mental development can be comprehensive. Therefore, this paper starts with finding out some innovative ways and methods of music education.
Superimposed Stress Calculation of Soil Underlying Anchor Beam considering Anisotropy and Strength Nonhomogeneity
Authors: Xu Fei , Wang Zhongshun , Zhou Liming , Zheng Hemin , Liu Bo , Jiang Yuqiu , Wang Daili
Published: September 2022

Prestress anchor beams are an important reinforcement method for underground engineering and rock slopes, and they are crucial for maintaining the stability of civil engineering structures. In this study, the computational formula of superimposed stress of soil underlying an anchor beam was proposed in this paper, based on the assumption that the anchor beam is completely rigid and the prestress of the anchor cable is acting on soil in the form of a strip uniform load. By considering the anisotropy and strength nonhomogeneity of the saturated soft clay, the failure envelopes of the soil and the maximum effective depth underlying the anchor beam were obtained by the D-P yield criterion suitable for soft clay analysis. Furthermore, the experimental comparisons with the previous findings were used to validate the proposed approach. The proposed method was valid to assess the maximum effective depth. Considering the influence of the factors on the failure envelopes of soil, a parametric study was carried out to optimize the design of the prestressed anchor cable. The findings of this study provide a theoretical basis for designing prestressed anchor cables; these can also be used to predict the response of prestressed anchor cables as well as to optimize the design of the cables, such as the anchorage length of cable and grouting radius.
Selecting the Optimal LoA to Prevent the Expansion of COVID‐19 in the Chemical Industry considering Sustainability Factors: A Fuzzy Mathematical Optimization Approach
Authors: Hajghasem Mahtab , Abtahi Amir-Reza , Khalili-Damghani Kaveh , Yousefi-Zenouz Reza
Published: September 2022

Automation has attracted interest from the industry sector for its potential to improve energy efficiency, cost efficiency, and environmental performance. By elevating the LoA to the highest degree, associated costs will grow accordingly and its implementation will be far more complicated. This will also result in losing workers and decreasing environmental pollutants. On the other hand, increasing power consumption at high levels of automation leads to the production of greenhouse gases. This paper aims to increase the level of automation (LoA) considering the concept of sustainability. This study presents fuzzy multi‐objective programming to determine the optimal LoA considering sustainability factors to achieve competitive advantages. To solve the model, the Zimmermann max‐min approach was adopted and a cosmetics factory in Iran was chosen to optimize LoA according to this model. The results showed that it is possible to improve the LoA and also consider sustainability factors with the available resources without using the highest LoA. This study can help managers optimize the LoA in their organizations considering the current resources and sustainability issues, and control the company's return on investment and cost of overhead. They can run the model with every definition of LoA proposed till now. This research can benefit the environment and the workers' health in the production line by reducing environmental pollutants and prevent the dismissal of all personnel due to its negative social effects. It also reduces the risk of COVID‐19 by minimizing the number of workers. So far, a mathematical model for selecting optimal LoA in the chemical industry considering sustainability has not been presented.
Damage Detection Test of GFRP Composite Civil Materials Based on Piezoelectric Ceramic Sensors
Authors: She Yanhua , Cai Gaojie
Published: September 2022

The piezoelectric ceramic sensor was fixed on Glass Fiber Reinforced Plastic (GFRP) composite civil materials, using the external paste method for damage detection test. The effect of the depth and number of cracks on the surface of the GFRP specimen on the signal received by the piezoelectric ceramic sensor was studied. The time-domain signal graph and energy graph based on wavelet packet were drawn, combining the active induction method and the energy method based on wavelet packet. It is found that the greater the damage degree of GFRP specimens, the smaller the voltage value of the signal, the smaller the energy of the signal, and the greater the damage index based on wavelet packet. The results showed that the active induction method can be used to collect the data of GFRP specimens by piezoelectric ceramic sensor. The collected data are processed by using the damage index principle based on wavelet packet. The maximum voltage value of the specimen, the energy of wavelet packet, and the damage index based on wavelet packet can all accurately judge the damage change of GFRP specimens. The active induction method based on piezoelectric ceramic sensor can detect the damage change of GFRP specimens in real time. It provides an effective method for damage analysis of GFRP composite civil materials.
A Critical Review of Croton as a Multipurpose Nonedible Tree Plant for Biodiesel Production towards Feedstock Diversification for Sustainable Energy
Authors: Takase Mohammed
Published: September 2022

Oil demand has risen steadily due to the growing industrialization and modernization of the world. In addition to rising costs, the supply of fossil fuels is also declining. These and many other concerns couple with food shortages have drawn attention of scientists to a substitute fuel that is generated from feedstocks that can be renewed. Biodiesel as an alternative fuel with a lot of expectations is produced using edible grown conventional vegetable oils such as sunflower, rapeseed, palm, and soybean. The production of biodiesel from edible oils has, meanwhile, worsened the existing competitiveness of oil used for food and fuel. Emphasis on using nonedible feedstock is currently guided by research to discover more potential nonedible feedstock such as croton. Differences between perceptions and facts about these nonedible oils necessitate efforts to diversify feedstocks into sources that can warrant the production of energy without impacting on the security of food. Croton is a multipurpose evergreen plant that is nonedible and is commonly present and cultivated under environmental and socio-economic conditions, which are complex in nature. This plant, referred to as a golden tree, has various uses including fuels, medicinal, ornamentals, dyes, feed, enriching of soil, and afforestation. This research was therefore carried out to investigate the multipurpose use croton. Among the highlighted areas include croton (feedstock) used for biodiesel, the necessity for croton seed oil and its value chain, the process for the modifying croton oil to biodiesel, factors that influence the production of biodiesel, the application of croton biodiesel in engines for efficiency and emission characteristics, and prospects for croton biodiesel.
Visualization of the Digital Enterprise Performance Management System Integrating the Simulated Annealing Algorithm
Authors: Zhang Bin
Published: September 2022

Enterprises conduct performance management through performance management systems, which can greatly improve work efficiency and quality. The win-win of human resources and time resources has been realized, making the assessment fair and objective. At present, the application of performance management to digitalization is becoming more and more intense. This paper proposes to use the optimization of the simulated annealing algorithm to help the research of the enterprise performance management system. It is helpful to analyze and solve some negative factors brought by the current traditional enterprise performance management system, so that the employees of the enterprise have higher satisfaction in the performance, and the enterprise can have more far-reaching development goals. In the subsequent survey of employee satisfaction with the enterprise performance management system, the results show that after the implementation of performance management reform, the recognition of performance management by enterprise managers and employees has increased significantly. Among the returned questionnaires, 3360 people expressed great satisfaction, and the proportion increased from 32.69% to 63.62%, almost doubling. A total of 4259 people expressed great satisfaction and satisfaction, the proportion increased from 70.08% to 87.35%. A total of 1021 people expressed dissatisfaction and very dissatisfaction, the proportion dropped from 29.92% to 12.65%. Therefore, this survey result shows that the visual analysis of the digital enterprise performance management system and its application in the operation of the enterprise is very important.
[Retracted] Study on the Effects of Different Doses of Dahuang Zhechong Pills on the Ubiquitin Proteasome Pathway/Nuclear Factor‐κB in Rats with Atherosclerosis and Its Mechanism
Authors: Chen Peng , Cui Xuan , Chen Xi , Chen Zhengyu , Zhang Xia , Zhang Chengrong , Zhang Jinyu
Published: September 2022

In order to investigate the effects of different doses of Dahuang Zhechong pills on the ubiquitin proteasome pathway/nuclear factor‐κB (UPP‐NF‐κB) in rats with atherosclerosis (AS), 58‐week‐old male Wistar rats were selected and randomly divided into the normal group, model group, control group, low‐dose group, and high‐dose group. The model group and the drug group are given intraperitoneal injections of vitamins, and the model group and the drug group are given a high‐fat diet. Rats in the low‐dose group and high‐dose group are given low‐dose and high‐dose Dahuang Zhechong pill lavage solution, respectively. Besides, the control group is given simvastatin solution by gavage, and intervention is performed once a day for 12 weeks. Ubiquitin (Ub) protein expression, ubiquitin activase (UBE1), nuclear factor‐κB, nuclear inhibitory factor‐κB (IκB) gene expression, total cholesterol (TC), triglyceride (TG), low‐density lipoprotein cholesterol (LDL‐C), and serum tumor necrosis factor‐α (TNF‐α) are compared. The experimental result shows that Dahuang Zhechong pills can reduce inflammation and prevent and treat AS by blocking the activation of the UPP/NF‐κB signaling pathway and can be used as a proteasome inhibitor in the clinical treatment of AS.
Sentiment Classification of Educational Tourism Reviews Based on Parallel CNN and LSTM with Attention Mechanism
Authors: Wang Ying , Chu Chengxi , Lan Tian
Published: September 2022

With the rapid development of the Internet and tourism, the Internet has been widely used in the tourism industry. Tourism enterprises and tourists use the Internet to publish and obtain travel-related information. Educational tourism is a new type of tourism activity. As a combination of “tourism + education,” it has gradually attracted the attention of tourists. With its convenience, fast speed, and low barrier, tourism text data provide great convenience for tourists’ sentiment calculation and have become one of the main sources of big data for tourism. However, the reviews of educational tourism have a lot of redundant information and complex sentence patterns, leading to a relatively low classification accuracy of the existing sentiment analysis algorithms. In order to effectively obtain the implicit semantic information of short text reviews for sentiment orientation recognition, a sentiment classification model for educational tourism online reviews based on parallel CNN and LSTM with multichannel attention mechanism is proposed. Firstly, Word2Vec technique is used, and based on noise word filtering, the feature words of educational tourism reviews are extracted to preprocess the input data set. Then, parallel CNN and LSTM are used to extract text local information and contextual features, and a multichannel attention mechanism is used to extract the attention values from the LSTM output. Finally, the output information of the multichannel attention mechanism is fused to effectively extract text features and focus on important words. The experimental results show that compared with other advanced methods, the proposed algorithm achieves improvements in terms of precision, recall, and F1 value and improves the AUC performance. It will help the educational tourism bases to carry out targeted development and construction in response to tourists feedback, enhance the sense of gain and happiness of tourists in educational tourism activities, improve tourism experience, and promote the rapid development of high-quality educational tourism.
Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model
Authors: Zhang Hanjie , Qian Shuqu
Published: September 2022

Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are increasing. In this paper, based on the study of wireless sensor networks and biological neural network models, the environmental factors that mainly affect the growth of domestic rabbits are analyzed as an example, and the technology is used to design and implement an environmental monitoring system for modern farms. The design of the system is divided into three main parts: hardware design of each node, software design, and upper computer monitoring software design. The hardware part of the system uses coordinator nodes, router nodes, sensor nodes, and control nodes to form a wireless sensor network in the farm, carries out the hardware circuit design of each node, and based on the protocol stack, designs the software program of each node to realize the collection, transmission, and regulation of environmental information in the farm. In the upper computer part, the design and development of the upper computer monitoring software interface are used to complete the real-time display of environmental data, historical query, database storage, and curve drawing, and to design a remote client data query system based on the architecture to realize the query of environmental data of the farm by remote users and to carry out monitoring fault intelligent identification alarm. At the same time, the paper investigates the optimal deployment of wireless sensor network nodes and searches for the optimal location of sensor nodes through an improved biological neural network algorithm to maximize the network coverage and reduce the coverage of blind areas, and conducts simulation experiments with the coverage rate of a rabbit farm as the optimization target.
Research on Intelligent Scoring and Style of Calligraphy Post Based on Machine Vision
Authors: Zhigao Zhou , Jie Zhou , Zhuo Li
Published: September 2022

Machine vision is a noncontact measurement method. In some jobs that are not suitable for artificial work environment or artificial vision, machine vision is usually used to replace artificial vision to meet the traditional requirements. Therefore, this paper quotes machine vision into the intelligent scoring and style research of calligraphy post. Firstly, it briefly introduces the related concepts and steps of calligraphy post and briefly explains the style of calligraphy post. Then for the third part of the machine vision localization research algorithm, the correlation function algorithm was proposed in the research field to explain and analyze. Finally, by comparing other research methods of machine vision, it shows that machine vision is more conducive to the study of calligraphy style intelligent scoring, through the experimental study of people on the calligraphy style and calligraphy style of people on the intelligent score. At the end, it is proposed that machine vision can greatly promote the study of intelligent scoring and style of calligraphy and also reflect the accuracy and usability of intelligent scoring for calligraphy style. On-contact test of machine vision refers to not touching the tested object, so as to obtain the test result. It is a three-dimensional testing technology. Among them, the test system of machine vision is relatively simple. It is easy to move and easy to collect data, and the cost of this noncontact test is low. It has more accurate testing technology. A plurality of test quantities can be simultaneously tested by image laser detection of the test quantity without touching the object.
Percutaneous Endoscopic Lumbar Discectomy for the Treatment of Recurrent Lumbar Disc Herniation: A Meta‐analysis
Authors: Zhao Ke , Li Lin-Da , Li Tong-Tong , Xiong Yong
Published: September 2022

Objective. To evaluate the incidence and safety of clinical complications associated with percutaneous endoscopic lumbar discectomy (PELD) for the treatment of recurrent lumbar disc herniation (RLDH) by meta‐analysis. Methods. PubMed, Embase, The Cochrane Library, and Web of Science electronic databases were searched for clinical studies on complications related to the treatment of RLDH with PELD. The search time extended from the databases’ inception until May 2021. RevMan5.4 software was used for meta‐analysis after two researchers independently scanned the literature, gathered data, and assessed the bias risk of the included studies. Results. A total of 8 clinical studies, including 1 randomized controlled trial and 7 cohort studies including 906 individuals, were included. According to the results of the meta‐analysis, the overall complications (OR = 0.18, 95% CI: 0.04‐0.83, p = 0.03) and dural tear rates (OR = 0.11, 95% CI: 0.01‐0.92, p = 0.04) of PELD were lower than those of traditional fenestration nucleus pulposus removal. Moreover, the PELD group had a greater recurrence rate compared to the MIS‐TLIF group (OR = 19.71, 95% CI: 3.68‐105.62, p = 0.0005), and the difference was statistically significant. However, compared with MED and MIS‐TLIF, there were no significant differences in the incidence of overall complications, dural tear, nerve root injury, and incomplete nucleus pulposus removal (P > 0.05). Conclusion. PELD is an effective and safe method for the treatment of recurrent lumbar disc herniation, with a lower incidence of complications and higher safety profile than traditional fenestration nucleus pulposus removal.
Validation of the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group Neurotoxicity Questionnaire for the Latin American Population
Authors: Ribeiro Ivana Leao , Lorca Luz Alejandra , Cuevas-Cid Rodrigo , Dixit Snehil , Yáñez-Benavides Nicolás , Ortega-Gonzalez Francisco
Published: September 2022

Background. Chemotherapy-induced peripheral neuropathy is a common adverse effect of chemotherapeutic treatment and is associated with decreased quality of life. The aim of this study was to evaluate the validity and reliability of the neurotoxicity subscale of the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity (FACT/GOG-Ntx) for the Chilean population. Methods. A cross-sectional study in which 101 participants with haematologic, colorectal, breast, gastric, gynaecological, and other types of cancer completed the FACT/GOG-Ntx. Content validity (

n
=
14

health professionals evaluated the subscale in four categories: test-retest reliability (

n
=
20

patients), dimensionality, internal consistency, and concurrent validity and discriminant validity. In all analyses,

p
<
0.05

was considered significant. Results. There was an agreement among the evaluators for all categories of the subscale (Kendall’s coefficient,

W
=
0.4

,

p
<
0.01

) and moderate to high intrarater reliability (

intraclass
 
correlation
 
coefficient
=
0.7
–
0.9

). Of the 11 original items that make up the subscale, none was eliminated. The factor analysis generated four factors that represented 72.2% of the total variance. Cronbach’s

α

was 0.8 for the 11 items. Women showed greater compromise in emotional well-being and neurotoxicity symptoms compared with men, and age was directly correlated with the questions ‘I have difficulty hearing’ (

r
=
0.2

,

p
=
0.019

) and ‘I feel a noise or buzzing in my ears’ (

r
=
0.2

,

p
=
0.03

). Conclusion. The Chilean version of the FACT/GOG-Ntx neurotoxicity subscale is a valid and reliable scale for evaluating neurotoxicity symptoms in adult cancer survivors in Latin America. The scales also adequately distinguish between sex-based well-being among the afflicted population.
An Earthquake Emergency Web Data Cleaning and Classification Method Based on Word Frequency and Position Weighting
Authors: Liu Shuai , Huang Meng , Li Chenxi , Lv Wenchao , Wang Zhonghao
Published: September 2022

The speed of earthquake emergency web document data cleaning is one of the key factors affecting emergency rescue decision-making. Data classification is the core process of data cleaning, and the efficiency of data classification determines the speed of data cleaning. This article is based on earthquake emergency Web document data and HTML structural features, combined with TF-IDF Algorithm and information calculation model, improves the word frequency factor and location factor parameters, and proposes the weighted frequency algorithm P-TF-IDF for earthquake emergency Web documents. To filter out less frequent words and optimize the FastText model, N-gram Feature word vectors effectively improve the efficiency of Web document data classification; for text classification data, use missing data recognition rules, data classification rules, and data repair rules to design an artificial intelligence-based earthquake emergency network information data cleaning framework to detect invalid data sets value, complete data comparison and redundancy judgment, clean up data conflicts and data errors, and generate a complete data set without duplication. The data cleaning framework not only completes the fusion of earthquake emergency network information but also provides a data foundation for the visualization of earthquake emergency data.
Integration and Sharing Mechanism of Vocational Education Resources from the Perspective of Industry-Education Integration
Authors: Zhang Ying , Zhu Yongxiang
Published: September 2022

Teaching resource platform construction is a systematic project that needs to be constantly enriched and improved in use. The improvement process should always adhere to the principles of school-enterprise cooperation and joint construction and sharing. To solve the problems of weak service content and imperfect sharing mechanism during the construction of the current vocational education teaching resource library, a general framework for the construction of a shared teaching resource platform is designed. To address the problems of low platform resources differentiation and few sharing paths, an intelligent recommendation method based on coupled collaborative filtering and portrait analysis technology is proposed. By introducing attention mechanism and portrait analysis technology into the coupled collaborative filtering model, the platform users and resources are accurately matched to achieve intelligent sharing of vocational education resources.
Design and Realization of Computer Network Virtual Experiment Economic Teaching Platform Based on Mathematical Image and Signal Processing
Authors: Li Xiao
Published: September 2022

The emergence of computer network virtual experiment teaching provides the possibility to solve the limitations of hardware equipment and time for cultivating computer talents in colleges and universities. Although the network virtual experiment teaching platform can provide learners with an autonomous learning environment, the design of these modules still has problems such as weak integration with the teaching management system, immature functions, and high cost. In order to make the computer network virtual experiment economic teaching platform better integrate with the teaching management system, reduce the cost of investment and provide a better system environment for the development of functions; this research will design the virtual teaching platform through the method of mathematical image and signal processing. By analyzing the functional requirements of the users of the teaching platform, the overall design of the system is determined, and the designed system is specifically implemented and designed for performance testing and functional testing. The functional test results are combined with expectations. The average response time of the system in the performance test results is 0.30 s, which is in line with the expected results. The experiments show that the system designed and developed in this study works well.
Analysis of the Preventive Action of Rivaroxaban against Lower Extremity Deep Venous Thrombosis in Patients after Laparoscopic Radical Gastrectomy
Authors: Dong Qinhui , Zhu Xiayin , Gao Yafen , Wang Zhengrong , Zheng Dexing , Zhu Jian
Published: September 2022

Background. Gastric carcinoma (GC) is a common lethal cancer in the world. Patients are prone to develop lower extremity deep venous thrombosis (LEDVT) after laparoscopic radical gastrectomy (LRG), which threatens their life and health. Purpose. This research is to clarify the preventive action of rivaroxaban (Riv) against LEDVT in patients undergoing LRG. Methods. A retrospective study was conducted on 70 patients with GC admitted for LRG between January 2019 and January 2022, including 40 patients (observation group) receiving Riv treatment and 30 patients (conventional group) treated with air wave pressure therapy apparatus. Quality of life, coagulation function, LEDVT formation, and complications were compared between groups. Results. The observation group had better recovery of life quality than the control group, along with more effective inhibition of coagulation disorders, less DVT formation, and fewer complications. Conclusions. Compared with air wave pressure therapy apparatus, Riv has better preventive action against LEDVT in GC patients after LRG.
Impacts of Cooperative Adaptive Cruise Control Links on Driving Comfort under Vehicle-to-Vehicle Communication
Authors: Li Yongchun , Shan Chuanping
Published: September 2022

Although automated vehicles could release drivers from the driving task, there are still passengers sitting in the vehicle. It is required that the driving comfort of passengers should be guaranteed. Cooperative adaptive cruise control (CACC) vehicle is of the one important type of automated vehicles using vehicle-to-vehicle (V2V) communications with various communication links. Different V2V communication links might have different driving comfort. Then, this paper focuses on exploring which link type for CACC vehicles is better from the perspective of improving driving comfort. To deal with this, car-following models of manual-driven vehicles (MDV) and CACC vehicles were first described. Then, simulations were performed using these car-following models, in which various CACC feedback link types, CACC penetration rates, and flow scenarios were taken into consideration. Simulations outputted microcosmic trajectory data of vehicles, based on which the driving comfort was evaluated using the comfort index described by the International Organization for Standardization (ISO) 2631-1. From the driving comfort perspective, simulation results suggest that CACC should monitor the immediately preceding vehicle and the third vehicle ahead when CACC penetration rates are less than approximately 50%. Additionally, if CACC penetration rates exceed 50%, the better choice is that CACC receives feedback links from two immediately successive vehicles ahead.
Sustainable Agricultural Management of Land Using Technology for Soil and Water Conservation within the Central Rift Valley, Central Ethiopia
Authors: Birhan Tesfaye , Tekalign Wondimagegnehu
Published: September 2022

Ethiopia has used several techniques for conserving water and soil. However, different sociocultural and technical problems have been affecting their implementation. The study is aimed at assessing sustainable agricultural management of land implementations through traditional and modern conservation of soil and water technologies in the central Rift Valley, Ethiopia. The research approach used was a descriptive survey using a cross-sectional research design. Household heads were chosen at random from the three kebeles, while representatives were chosen using a proportionate sample technique. Furthermore, kebeles and key informants were selected by using a purposive sampling technique. Data were collected through questionnaires, key informant interviews, and field observation. The results show that both traditional and contemporary methods for conserving soil and water have advantages and disadvantages of their own, and some of them have been combined. A number of factors, including age, family size, education, topography, distance from the homestead, income, and the availability of training, have a big impact on whether or not soil and water conservation methods are adopted. The majority of farmers use mixed farming followed by crop production to meet their livelihood needs. The most widely implemented physical measure was terracing, followed by stone bunds. The farmers practiced traditional waterways, furrows, check dams, terracing, and stone bunds as traditional conservation practices. Agroforestry, followed by grass strip and area closure, was the most commonly implemented vegetative measure. Besides, animal manure, followed by animal parking, was the most implemented agronomic measure. The concerned stakeholders need to pay more attention to community mobilization for the conservation, upkeep, and development of traditional and modern soil and water conservation structures. In order to employ traditional and contemporary soil and water conservation measures for sustainable agricultural land management practices, experts need to instruct the local farmers.
GDP Economic Forecasting Model Based on Improved RBF Neural Network
Authors: Yu Ying
Published: September 2022

Among the existing GDP forecasting methods, time series forecasting and regression model forecasting are the two most commonly used forecasting methods. However, traditional macroeconomic forecasting models are unable to accurately achieve optimal forecasts of highly complex nonlinear dynamic macroeconomic systems due to the influence of multiple confounding factors. In order to solve the above problems, a GDP economic forecasting model based on an improved RBF neural network is proposed. First, the main traditional GDP forecasting methods are analyzed. Then, RBF neural networks are used to solve the problem that traditional forecasting technology methods cannot handle multi-factor complex nonlinearities well. Second, to further improve the convergence speed and accuracy of the RBF neural network learning algorithm, the Shuffled Frog Leaping Algorithm with global search capability and high practicality is fused into the RBF network training. Finally, the improved RBF neural network is used to build a GDP economic forecasting model. The performance of the Shuffled Frog Leaping Algorithm and the improved RBF neural network was tested using the approximation of Hermit polynomials and the Iris classification problem as simulation examples. The experimental results show that the improved RBF neural network-based GDP economic forecasting model achieves more accurate forecasting accuracy than other forecasting methods.
An Integer Linear Programming Model for Partially Ordered Sets
Authors: Badr Elsayed , Selim I.M. , Mostafa Hoda , Attiya Hala
Published: September 2022

Linear programming is an important approach that is used to represent a large class of combinatorial optimization problems. The simplex algorithm is one of the algorithms for solving linear programming problems with exponential time complexity. Fortunately, this algorithm solves real‐world problems with polynomial time complexity. In particular, a new Integer Linear Programming model (ILPM) is proposed for partially ordered sets. Robert Dilworth’s Decomposition theorem is formulated by ILPM and proves its correctness using the paradigm of strong duality in linear programming. Finally, ILPM is run on fifteen benchmark partially ordered sets for finding their width. The computational experiments show the validity of the proposed model.
Digital Development for Music Appreciation of Information Resources Using Big Data Environment
Authors: Li Yi
Published: September 2022

With the continuous development of information technology and the arrival of the era of big data, music appreciation has also entered the digital development. Big data essence is highlighted by comparison with traditional data management and processing technologies. Under different requirements, the required time processing range is different. Music appreciation is an essential and important part of music lessons, which can enrich people’s emotional experience, improve aesthetic ability, and cultivate noble sentiments. Data processing of music information resources will greatly facilitate the management, dissemination, and big data analysis and processing of music resources and improve the ability of music lovers to appreciate music. This paper aims to study the digital development of music in the environment of big data, making music appreciation more convenient and intelligent. This paper proposes an intelligent music recognition and appreciation model based on deep neural network (DNN) model. The use of DNN allows this study to have significant improvement over the traditional algorithm. This paper proposes an intelligent music recognition and appreciation model based on the DNN model and improves the traditional algorithm. The improved method in this paper refers to the Dropout method on the traditional DNN model. The DNN is trained on the database and tested on the data. The results show that, in the same database, the traditional DNN model is 114 and the RNN model is 120. The PPL of the improved DNN model in this paper is 98, i.e., the lowest value. The convergence speed is faster, which indicates that the model has stronger music recognition ability and it is more conducive to the digital development of music appreciation.
Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm
Authors: Tian Lei
Published: September 2022

With the rapid economic development, the financial industry has quietly become the leader of industries, the core and lifeblood of promoting economic development. At the same time, various financial services and management platforms emerge one after another. However, the emergence of financial services and management platforms cannot effectively alleviate the current financial crisis. In the face of increasingly complex financial risks, traditional financial service and management platforms cannot achieve effective information sharing, which leads to continued low service and management efficiency and frequent financial risk problems. Support vector machine is a data classification algorithm based on supervision, which can realize data sharing and improve the efficiency of data processing. The article firstly readjusted the underlying architecture of the financial service and management platform to break through the barriers of data interaction. Then on this basis, the article further combines the support vector machine algorithm and extends it from binary data classification to multivariate classification. Finally, the paper redesigns the financial service and management platform considering support vector machines. After a series of experiments, it can be found that the financial service and management platform based on the support vector machine algorithm can reduce the financial risk by 17.2%, improve the financial service level by 30.2%, and improve the financial comprehensive service level by 45.2%. At the same time, thanks to information sharing and interaction, the financial service and management platform can effectively predict financial risks, and the accuracy of the prediction basically reaches 78.9%. This shows that a financial service and management platform that takes into account the support vector machine algorithm can effectively prevent financial risks and improve the efficiency of financial services and management.
Modeling and Simulation of Theoretical Digging Force of an Excavator Based on Arbitrary Posture
Authors: Qin Zuhe
Published: September 2022

Theoretical digging force is an important performance parameter of the hydraulic excavator. Due to the multi-linkage mechanism, the digging posture of an excavator is ever-changing; thus, it is not easy to calculate the theoretical digging force. Traditionally, the theoretical digging force is calculated by selecting a limited number of postures of the excavator based on experience, which cannot reflect the force condition of each posture in the excavation process. In order to calculate the theoretical digging force of any posture in the excavation process, this paper uses the principle of the analytical method to establish a theoretical excavation force mathematical model of bucket excavation and uses Matlab software to solve the theoretical excavation force mathematical models. In order to verify the model’s correctness, the experimental method is used to determine the actual maximum excavation force of the excavator. The test results showed that there was little difference between the actual maximum digging force and the theoretical maximum digging force, which verified the correctness of the theoretical digging force mathematical models and provided a theoretical digging force calculation method and reference for the structural design and optimization of the excavator.
Healthcare Professionals’ Experiences with Rehabilitation Practices for Patients with Cognitive Impairment after Stroke in North Norway: A Qualitative Study
Authors: Bogstrand Anniken , Gramstad Astrid , Anke Audny , Stabel Henriette Holm , Arntzen Cathrine
Published: September 2022

Background and Aim. Cognitive impairment is a common consequence following stroke. Previous research shows differences in rehabilitation services supporting physical, cognitive, and psychosocial needs. The aim of the current study was to explore health professionals’ experiences with service provision for patients with mild and moderate cognitive impairment after stroke in a North Norwegian context. Methods. A focus group interview with clinicians, coordinators, and leaders involved in stroke survivors’ rehabilitation trajectories was conducted. The group consisted of a strategic selection of participants with diverse professional backgrounds from specialist and primary healthcare services. The transcribed material was analyzed thematically using systematic text condensation based on an inductive, interpretive approach. Results. We found that patients with mild and moderate cognitive impairment after stroke were characterized as a neglected group in rehabilitation services and that neglect was related to both structural and professional issues. First, neglect seemed partly related to the availability of existing healthcare services, which mainly followed up on physical challenges after stroke. Second, cognitive rehabilitation seemed to be less prioritized than other health services, and the established interdisciplinary municipality teams did not seem prepared to follow-up on cognitive issues. Finally, at a professional level, the study reveals the need for building competence in cognitive rehabilitation and having services available in the long run. Conclusion. The study demonstrates the need to increase knowledge concerning cognitive rehabilitation and how rehabilitation trajectories and services should be organized to fulfil stroke survivors’ and carers’ long-term needs.
Morroniside Protects Human Granulosa Cells against H2O2-Induced Oxidative Damage by Regulating the Nrf2 and MAPK Signaling Pathways
Authors: Ma Yucong , Hao Guimin , Lin Xiaohua , Zhao Zhiming , Yang Aimin , Cao Yucong , Zhang Shuancheng , Fan Lijie , Geng Jingran , Zhang Yu , Chen Jingwei , Song Cuimiao , He Ming , Du Huilan
Published: September 2022

Morroniside is the main ingredient of Cornus officinalis and has a variety of biological activities including antioxidative effects. Ovarian granulosa cells (GCs) are responsible for regulating the development and atresia of follicles, which are susceptible to oxidative stress. In this study, we determined whether morroniside can inhibit the oxidative stress of GCs induced by hydrogen peroxide (H2O2), leading to improved oocyte quality. The oxidative damage and apoptosis of ovarian GCs cultured in vitro were induced by the addition of H2O2. After pretreatment with morroniside, the levels of ROS, MDA, and 8-OHdG in ovarian GCs were significantly decreased. Morroniside significantly upregulated p-Nrf2 and promoted the nuclear translocation of Nrf2, which transcriptionally activated antioxidant SOD and NQO1. In addition, morroniside significantly regulated the levels of apoptosis-related proteins Bax, Bcl-2, cleaved caspase-9, and cleaved caspase-3 via the p38 and JNK pathways. These results suggest that morroniside can reduce the oxidative damage and apoptosis of ovarian GCs induced by H2O2.
Temporal and Spatial Distribution of Food Security Production and Total Water Resources in Western Jilin: Based on Center of the Gravity Model
Authors: Wang Haokun , Gao Biao
Published: September 2022

In recent years, the main grain-producing areas in the west of Jilin have gradually concentrated in the perennial irrigation areas and supplementary irrigation areas in the north, and the stress degree of grain irrigation water resources is gradually increasing. Therefore, this paper selects the arid and semiarid area in the west of Jilin Province as the research area and compares and analyzes the modeling process and simulation accuracy of the temporal and spatial distribution of food security production and total water resources. Based on the center of gravity model, this paper studies the temporal and spatial distribution of food security production and total water resources in Western Jilin. It can be seen that after 2018, rice and wheat show a fluctuating upward trend, and their values rise from 0.612 to 0.786 and 0.356 to 0.612, respectively; the rise of corn is small, showing an inverted “U” trend, which first increases from 0.693 in 2018 to 0.701 in 2019 and then decreases to 0.671 in 2021; By comparing the precipitation statistics in different years, the inverted “U” trend of corn irrigation water efficiency may be related to the sudden increase of precipitation. The center of gravity model is used to analyze the evolution trend of the center of gravity of grain production and water resources in Western Jilin from the national and regional levels, and the spatiotemporal coupling degree of the center of gravity of grain production and water resources is explored through the two-factor center of gravity coupling situation model.
Region Convolutional Neural Network for Brain Tumor Segmentation
Authors: Pitchai R. , Praveena K. , Murugeswari P. , Kumar Ashok , Mariam Bee M. K. , Alyami Nouf M. , Sundaram R. S. , Srinivas B. , Vadda Lavanya , Prince T.
Published: September 2022

Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now. In this article, a segmentation model is designed using RCNN that enables automatic prognosis on brain tumors using MRI. The study adopts a U-Net encoder for capturing the features during the training of the model. The feature extraction extracts geometric features for the estimation of tumor size. It is seen that the shape, location, and size of a tumor are significant factors in the estimation of prognosis. The experimental methods are conducted to test the efficacy of the model, and the results of the simulation show that the proposed method achieves a reduced error rate with increased accuracy than other methods.