bluuebunny/crossref_metadata_2025 huggingface dataset:
Teaching Archives Information Management and Quality Diagnosis Method from the Perspective of Collaborative Education Based on Intelligent Computing
Authors: Liu Tieli , Tang Liansheng , Yao Liqiong , Zhu Geng
Published: September 2022

With the growth of better education, improving the quality of better education has become a top priority. In order to facilitate the sustainable and healthy growth of better education and continuously improve the quality of education for faculty and staff, the Ministry of Education has established a regular assessment system for undergraduate-level education. This article analyzes the quality diagnosis of teaching archives management from the perspective of collaborative education by using the bionic method and summarizes the three methods. In the precision rate, the user interface is the highest, followed by accuracy, security requirements, hardware environment, system maintenance, and information flow; in the recall rate, the security requirements are the highest, followed by the hardware environment, accuracy, user interface, information flow, and system maintenance; in F1, the user interface is the highest, followed by hardware environment, information flow, accuracy, security requirements, and system maintenance. In the particle swarm optimization coverage algorithm, the security requirements are obtained, the user interface accuracy is relatively high, and the hardware environment is relatively low; the system maintenance and hardware environment are the most covered, and the security requirements are the least; the number of rejected samples is the largest. It is the hardware environment and system maintenance, and the least is the security requirement; the most correct number of rejected samples is the hardware environment and system maintenance. The scientific analysis of the quality diagnosis of teaching archives management from the view of collaborative education is extremely important for improvement of teaching. The archives management system can be effective in the process of the archives hardware environment, security requirements, system maintenance, user interface, information flow, and accuracy. The efficiency of the manual management process is improved, and the process is simplified.
Improved RRT-Based Moving Path Planning Algorithm for Teaching Reform and Innovation in Western Orchestral Ensemble Classes in Colleges and Universities
Authors: Zhang Wang
Published: September 2022

Western orchestral instruments in colleges and universities, as important instruments, occupy a central place in many bachelor’s degree programs in music education. Music teaching reform and innovation are central to the creative wisdom of music teachers, their musical upbringing, and the lived experience of their students. For teachers, the process of pedagogical reform and innovation is the process by which teachers realize their own pedagogical ideals. Teachers must first be motivated; in the absence of motivation, any advanced teaching methods will become pale in comparison and lose their value and usefulness. Therefore, the construction of teaching activities requires teachers to take responsibility for the education and support of students, to align their teaching goals with the development of each student, to make the students’ feelings closely connected to them, and to make them feel their value in the learning process. Starting from teaching western orchestral instrument ensembles in colleges and universities, this thesis uses the research method of improving RRT’s moving path planning algorithm to explain and analyze some problems that arise in teaching western orchestral instrument ensembles in colleges and universities and puts forward constructive suggestions and recommendations.
Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning
Authors: Sun Zhen’ao , Chen Zhe
Published: September 2022

Aiming at the problems of low prediction accuracy, long time, and poor results in current wind turbine generation power prediction methods, an offshore wind turbine generation power prediction method based on cascaded deep learning is proposed. Using deep belief networks, stacked autoencoding networks, and long short-term memory networks, a cascaded deep learning method is proposed to predict the power generation of offshore wind turbines. Multiple feature extractors are used to extract and fuse high-level features to form a unified feature with richer information to predict the power generation sequence of offshore wind turbines. According to the modeling strategy and port design strategy, using the stacked autoencoding networks as the basic unit, a cascaded deep learning model for generating power prediction of offshore wind turbines is established. Through the selection of input variables, the variables that have a great correlation with wind power are obtained. The layer-by-layer greedy algorithm is used for training from bottom to top, and supervised learning is used to fine-tune the network parameters from top to bottom to realize the generation power prediction of the offshore wind turbine. The experimental results show that the proposed method is effective in predicting the power generation of offshore wind turbines, which can effectively improve the prediction accuracy and shorten the prediction time.
[Retracted] Mechanism of Shishiwei Wendan Decoction in the Prevention and Treatment of Lung Adenocarcinoma Using Network Pharmacology and Molecular Docking
Authors: Li Xiaofan , Sun Qi , Ma Wenli , Ma Xinzhe , Pan Hongyue , Guo Wei
Published: September 2022

Objective. This study used network pharmacology and molecular docking technology to elucidate the mechanism of action of Shishiwei Wendan Decoction against lung adenocarcinoma. Methods. By using the world’s largest TCM System Pharmacology Database and Analysis Technology Platform (TCMSP) system to conduct in‐depth mining analysis and data collection of the main active components of the medicinal components in Shishiwei Wendan Decoction and using the human gene card database (GeneCards), Human Mendelian Inheritance Online System (OMIM), and Human Disease‐Related Gene and Mutation Information Database (DisGeNET) to collect the pathogenic targets of lung adenocarcinoma and build a PPI network; for the core drug targets, use GO enrichment analysis and KEGG pathway analysis; use Cytoscape software to build relevant network maps; and use AutoDock to achieve molecular docking. Results. Shishiwei Wendan Decoction screened 144 active ingredients and 384 drug targets; 7680 lung adenocarcinoma disease targets were obtained, including 380 targets for Shishiwei Wendan Decoction in the treatment of lung adenocarcinoma. GO enrichment analysis demonstrated 2,299 downstream genes, and key target genes were closely related to nutrient levels, membrane rafts, and protein serine/threonine kinase activity; KEGG functional enrichment analysis yielded 179 related pathways, including tumor necrosis factor signaling pathway which is related to the target gene. Molecular docking showed that the core active ingredients and key targets could be well combined. Conclusion. Through the network pharmacology analysis and molecular docking experiments of Shishiwei Wendan Decoction against lung adenocarcinoma, it is found that Shishiwei Wendan Decoction has multidimensional effects on the treatment of lung adenocarcinoma, and it is the first Shiwei Wendan Decoction to treat lung adenocarcinoma. Decoction in the treatment of lung adenocarcinoma provides biointellectual support and the oretical support.
On Curvilinear Regression Analysis via Newly Proposed Entropies for Some Benzene Models
Authors: Liu Guangwu , Siddiqui Muhammad Kamran , Manzoor Shazia , Naeem Muhammad , Abalo Douhadji
Published: September 2022

To avoid exorbitant and extensive laboratory experiments, QSPR analysis, based on topological descriptors, is a very constructive statistical approach for analyzing the numerous physical and chemical properties of compounds. Therefore, we presented some new entropy measures which are based on the sum of the neighborhood degree of the vertices. Firstly, we made the partition of the edges of benzene derivatives which are based on the degree sum of neighboring vertices and then computed the neighborhood version of entropies. Secondly, we made use of the software SPSS for developing a correlation between newly introduced entropies and the physicochemical properties of benzene derivatives. Our obtained results demonstrated that the critical temperature , critical pressure , and critical volume can be predicted through fifth geometric arithmetic entropy, second SK entropy, and fifth ND entropy, respectively. Other remaining physical characteristics include Gibb’s energy (qℰ), log(P), molar refractivity (ℳℛ), and Henry’s law (ℋℒ) that can be predicted by using sixth ND entropy.
[Retracted] Meta‐Analysis of Prognostic Correlation of Thrombectomy for Cerebral Infarction Based on Intelligent Medical Treatment
Authors: Han Chungeng , Peng Wenjun , Xie Jiangwen , Xu Jun , Xu Huayan
Published: September 2022

A meta‐analysis is used to investigate the correlation between the status of collateral circulation assessed by head CT angiography (CTA) and the outcome of thrombectomy for cerebral infarction. Meta‐analysis is conducted. The experimental results show that the National Institutes of Health Stroke Scale (NIHSS) score, modified Rankin Scale (mRS) score, incidence of cerebral hemorrhage, and mortality in the group with good collateral circulation are significantly lower than those in the group with bad collateral circulation, and the rate of good prognosis is significantly higher (P < 0.05). The evaluation and treatment of patients with cerebral infarction and their prognosis based on CTA intelligent medical technology are related to collateral circulation, and the effect of effectively reducing the risk of death, cerebral hemorrhage, and neurological function injury and improving the prognosis is more obvious in each collateral circulation.
A Lane-Changing Decision-Making Model of Bus Entering considering Bus Priority Based on GRU Neural Network
Authors: Lv Wanjun , Lv Yongbo , Guo Jianwei , Ma Jihui
Published: September 2022

A mandatory lane change occurs when buses are ready to enter the station, which will easily cause a reduction of urban road capacity and induce traffic congestion. Using deep learning methods to make lane-changing decisions has become one of the research hotspots in the field of public transportation, especially with the development of the Cooperative Vehicle-Infrastructure System. Aiming at the exploration of the bus lane-changing rules and decisions during entering, we built a GRU neural network model considering bus priority by using the first real-world V2X (vehicle to everything) dataset. Firstly, we illustrated the image and point cloud data processing by coordinate transformation. Secondly, the Kalman filtering algorithm was used to evaluate the vehicle state. Combined with the bus priority rules, we propose a flexible right-of-way lane in front of the bus stop. And then, we obtain the feature variables as inputs to the model. The XGBoost algorithm was chosen to train the GRU model. Results show that the model has higher identification accuracy for lane-changing maneuvers by comparison with other models. It plays a very important role in providing a decision basis for more refined bus operation management.
Prognostic Value of Combination of Controlling Nutritional Status and Tumor Marker in Patients with Radical Non-Small-Cell Lung Cancer
Authors: Ma Keru , Wang Hao , Jiang Xiangyu , Fang Chengyuan , Ma Jianqun
Published: September 2022

Background. Controlling nutritional status (CONUT) and tumor markers are associated with prognosis in patients with non-small-cell lung cancer (NSCLC). This study is aimed at exploring the potential usefulness of T-CONUT, constructed by combining CONUT and tumor markers, for NSCLC patients undergoing radical surgery. Methods. A total of 483 patients with NSCLC underwent radical surgical resection. The receiver characteristic operating curve (ROC) was used to select the tumor marker with the highest predictive performance, and CONUT was combined with this marker to construct the T-CONUT. The Kaplan–Meier method and log-rank test were used to analyze the overall survival (OS), and chi-square analysis was used to analyze the association between T-CONUT and clinicopathological characteristics. The independent risk factors were analyzed by Cox regression. A nomogram was constructed by R studio. Calibration plots, the

c

-index, and decision curves were evaluated for the performance of the nomogram. Results. ROC analysis showed that the predictive performance of CYFRA21–1 was better than that of CEA, NSE, and SCC. CYFRA21–1 was selected for combining with CONUT to construct T-CONUT. Elevated T-CONUT indicates poor prognosis of patients. Histological type, pTNM, and T-CONUT are independent risk factors associated with patient prognosis. The areas under the curve of the nomogram for predicting 3- and 5-year OS were 0.760 and 0.761, respectively. Conclusion. T-CONUT comprising CYFRA21–1 and CONUT can effectively predict the prognosis of NSCLC patients.
Characterization of Metakaolin from Wassa Kaolin and Its Potential as Adsorbent for the Regeneration of Used Engine Lubrication Oil
Authors: Kwakye Ralph , Aniagyei Albert , Vigbedor Bright Yaw , Osei-Owusu Jonathan , Antwi Bonface Yeboah , Amable Anthony Selorm Kwesi , Edusei George , Kwakye-Awuah Bright
Published: September 2022

Metakaolin is an anhydrous clayey material obtained by the calcination of kaolin. This material has been utilized as a valuable substitute or additive for concrete/cement mixtures and many other applications. The current study is on the production and characterization of metakaolin together with its adsorbent properties for the purification and regeneration of spent lubrication oil. Metakaolin was produced from the calcination of kaolin from Wassa in the Western region of Ghana. The metakaolin was characterized by using X-ray fluorescence (XRF), X-ray diffractometry (XRD), scanning electron microscopy (SEM) coupled with energy dispersive X-ray (EDX), and Fourier transform infra-red (FT-IR). The metakaolin was then used as an adsorbent in the regeneration of the engine lubrication oil, mimicking the acid-clay percolation process. The test results show that the efficiency of metakaolin in removing heavy metals from the used oil using an atomic absorption spectrometer (AAS) was 13.25% for Fe, 25.37% for Cr, 94.36% for Zn, and 33.33% for Cu. The viscosity index of the regenerated oil was 118 compared to that of the virgin oil, which was 115. The regenerated oil had 79% saturates, 6% carbon residues, and 18% aromatics compared to the commercial virgin oil, which contained 84% saturates, 5% carbon residue, and 11% aromatics. This indicated that metakaolin is a better adsorbent for regenerating the used lubrication oil.
Study of Intelligent Wireless Network Management in the Context of Artificial Intelligence for the Improvement of Chinese Language Mandarin Test Training Programmes
Authors: Luo Xi , Chen Haiyan
Published: September 2022

One of the most prominent ways of communication between people is through language, which plays a significant role in expressing thoughts. Different ways of expressing a language can be through speech, writing, signing, or gesture. Each country has their own traditional language and they are getting upgraded with technology in a complex environment. The country of China is termed as Chinese and they follow a dialect named Putonghua. Besides this Putonghua, the people follow different dialects, but this Putonghua is considered the official dialect. Also, the Putonghua Proficiency Test is to test the fluency of Chinese-speaking skills. In the traditional system, the test is conducted by the authorities manually. This process will be difficult when multiple people appear for the test, and in some circumstances, complex situations will arise. Hence, technological advancement can be leveraged to simplify the processes. In this research, Chinese language learning and the Putonghua Test were performed with the implementation of the Deep Learning (DL) model. This process involves the design of a DL model for mobile phones and training the model according to the application. Later, the concept is implemented through intelligent wireless network communication for learning and testing of the language. LIDA is implemented in this research work to train the system with DL. The main functionality of LIDA is template matching, which is required for testing the proficiency of the Chinese language by the candidate in PPT. When the new LIDA model is compared to the existing Vector Spaced Model (VSM), it is found that the LIDA achieves 98.67% of the test accuracy.
Numerical Simulation and Active Protection of Lightning Discharge Based on Quantum Heuristic Evolutionary Algorithm
Authors: Wu Mingfa , Qian Yufeng , Yang Chongjing
Published: September 2022

Thunder is a discharge phenomenon that often occurs in nature. Due to its physical influences such as strong current, high temperature, strong shock waves, and strong electromagnetic radiation, it has a huge destructive effect instantly, which may bring serious threats to people’s lives and property safety. This study aimed to study the lightning discharge numerical simulation and active protection based on the quantum heuristic evolutionary algorithm and proposed to apply the lightning discharge numerical simulation to the prevention of lightning disasters. This article gives a detailed description of the quantum algorithm, the generation, and harm of lightning discharge. The genetic algorithm is used to optimize the lightning data simulation algorithm, and the optimization process is introduced in detail. In addition, this article conducts related experiments on lightning discharge numerical simulation and active protection. The experimental results show that targeted active protection and effective numerical simulation are important measures to prevent lightning disasters. Active lightning protection measures can reduce lightning by 30%. Losses are caused by disasters.
The Impact of 3S2E Nursing Management on the Psychological Status of Respiratory Function and Quality of Life of Patients with Severe Pneumonia in the ICU
Authors: Xu Yaoyao , Li Xia , Yang Qin , Ma Xiuxian
Published: September 2022

Objective. This study examines the effects of the 3S2E nursing management mode on patients with severe pneumonia in the intensive care unit’s respiratory function, psychological status, and quality of life (ICU). Methods. According to a random number table, 82 ICU patients with severe pneumonia who were admitted between March 2021 and March 2022 were enrolled and assigned to the control and observation class (n = 41, respectively) in a 1 : 1 ratio. The observation class added 3S2E manner in addition to ordinary breastfeeding, whereas the control class received treatment in the usual nursing mode. The two groups’ preintervention and postintervention times for mechanical ventilation, white blood cell count (WBC) recovery, duration of hospital stay, problems, respiratory function, psychological state, and living quality were compared. Results. Fever time abatement, mechanical ventilation time, WBC recovery time, and length of hospital stay in the observation category were found to be shorter in comparison with the control class (

P
<
0.05

). In contrast to the other group, the observation group had fewer issues (

P
<
0.05

). Both teams’ oxygenation indices and SaO2 were higher after the intervention (

P
<
0.05

), with the observation team’s index being higher than the control group’s index. The total SAS and SDS scores of the two groups were less in the postintervention period than in the preintervention period, with the observational class having lower postintervention SAS and SDS ratings than the comparison group (

P
<
0.05

). The postintervention ratings in the observation class were higher than those in the control, and the World Health Organization Quality of Life (WHOQOL) scale scores in the 2 categories were greater after the intervention than they were before (

P
<
0.05

). Conclusion. 3S2E nursing management model improves respiratory function, alleviates negative emotions, and improves living quality in ICU patients with severe pneumonia.
Soil Pollution Detection and Waste Classification Management of Environmental Protection Enterprises Based on 5G Internet of Things
Authors: Yu Minglong , Liu Yi , Song Jaehoon
Published: September 2022

Soil is the foundation of human existence, the foundation of development, and an important part of the ecological environment. The current situation of soil pollution in China is severe, and the environmental hazards caused by soil pollution are endless. At present, soil testing mainly studies the basic soil environment through electronic technology and monitoring technology and cannot carry out in-depth research. In soil testing, due to some geological problems, it is impossible to accurately detect in soil testing. It has become a consensus to strengthen soil environmental protection and implement special laws on soil pollution prevention and control. In the absence of legislation, strengthening research on the legal system for soil pollution prevention and control is in line with the basic laws of legislation. This article uses 5G Internet of Things soil pollution detection; from the strategic height of soil ecological safety, combined with the current national conditions in China, it is necessary to strengthen the timely detection of the status of soil environmental quality and strengthen the evaluation of soil environmental quality. In addition, this article applies 5G Internet of Things to the waste sorting management of environmental protection companies. In recent years, with the advancement of urbanization, residents’ living standards and consumption levels have continued to improve. This has also led to a sharp increase in the amount of domestic waste. The disposal of a large amount of domestic waste has caused a huge burden on the ecological environment and has also caused some environmental problems. Under the background that the party and the state attach great importance to the environmental protection awareness of the whole people, local governments have also actively supported and promoted urban waste management and prevention work in order to achieve the purpose of “harmless, reduced, and resource-based” waste classification. In this article, environmental protection companies’ use of IoT 5G to classify garbage can provide impetus for the future development of garbage classification. The 5G Internet of Things provides new ideas for enterprise waste classification management and soil pollution detection.
Investigation and Process Parameter Optimization on Wire Electric Discharge Machining of Aluminium 6082 Alloy
Authors: Murali G. , Murugan M. , Arunkumar K. , Elumalai P. V. , Mohanraj D. , Prabhakar S.
Published: September 2022

Wire electrical discharge machining (WEDM) is an unconventional machining process that is being extensively used in the aerospace,medical devices, die, tooling, and automotive industries for machining high-hardness materials with conductivity. In the present work, WEDM of aluminium 6082 alloys was carried out since it influences diversified applications in manufacturing industries. The WEDM process includes an extensive number of variables that influence its execution. However, based on the literature survey, three process parameters such as pulse-on time (PTON), pulse-off time (PTOFF), and wire feed (WF) were taken into consideration. The factorial design was used for the selection of parameter levels and arrived at the 27 trails for the machining. The output responses of the WEDM, namely, surface roughness (SR), kerf width (KF), and metal removal rate (MRR) were measured, and its parameter optimization was also carried out to minimize the significant effect on productivity and the quality of components. The measured output response was compared with the predicted response surface methodology (RSM) results; it was found that the SR and KF values decreased with the increase of PTON. The MRR was increased with the increase of PTON.
Application and Analysis of RGB-D Salient Object Detection in Photographic Camera Vision Processing
Authors: Fu Qiang
Published: September 2022

To identify the most visually salient regions in a set of paired RGB and depth maps, in this paper, we propose a multimodal feature fusion supervised RGB-D image saliency detection network, which learns RGB and depth data by two independent streams separately, uses a dual-stream side-supervision module to obtain saliency maps based on RGB and depth features for each layer of the network separately, and then uses a multimodal feature fusion module to fuse the latter 3 layers of RGB and depth high-dimensional information to generate high-level significant prediction results. Experiments on three publicly available datasets show that the proposed network outperforms the current mainstream RGB-D saliency detection models with strong robustness due to the use of a dual-stream side-surveillance module and a multimodal feature fusion module. We use the proposed RGB-D SOD model for background defocusing in realistic scenes and achieve excellent visual results.
Research on the Performance of the Trend Following Trading Strategy in the Chinese Commodity Market
Authors: Xie Anran , Huang Yichong , Bian Yian , Zhao Shucen , Lin Jiaxin
Published: September 2022

Trend following strategy is a popular strategy that investors often use in trading around the world. Stocks are bought during an upswing and sold during a decline, the two main phases of the trend-following trading technique. This research evaluates the performance of the trend-following strategy in the Chinese commodity market by systematically employing quantitative methods to trade and get back test results for performance evaluation. The main trading indicator for this research is DMAC (Dual Moving Average Crossover) with a trend indicator called ADX for adjustment. As a kind of technical analysis, so-called “Dual Moving Average Crossovers” are often cited as providing reliable signs for discerning future stock price movements. By employing these indicators and systematically backtesting on 21 commodity futures for ten years, the research discovers that DMAC does not perform well (negative annualized return and sharpe ratio) from 2011 to 2021. By refining the strategy which is to replace DMAC with MACD (Moving Average Convergence/Divergence) and abandon ADX, the backtest result performs much better. The refinement suggests that the utilization of different trading signals/indicators will lead to a different performance of the trend following strategy in the Chinese commodity market. The research concludes that the trend following strategy is worthy of exploring in the Chinese commodity market in terms of using different trading indicators.
Cultivation Method Analysis for Teachers’ Teaching Ability Driven by Artificial Intelligence Technology
Authors: Chen Yanfang , Xu Shasha
Published: September 2022

Strengthening construction for teaching staff is an eternal theme of development and construction for colleges, and it is also the focus of personnel management in colleges. China is swiftly transitioning from the industrial age to the era of intelligence as a result of the rapid growth of information technology and artificial intelligence. Colleges and universities have reached a new stage in their evolution, one marked by intelligent use of technology, as represented by the fourth generation of information technology: cloud computing, big data, and artificial intelligence. Higher standards for college professors’ teaching abilities have been imposed by this new policy. It is therefore beneficial to evaluate teachers’ teaching abilities from an artificial intelligence perspective to improve the overall quality of college education. First, this work researches and improves the teaching ability training strategies of college teachers driven by artificial intelligence from different levels. Second, this work proposes a neural network (IPSO-BP) for evaluating the teaching ability of college teachers via artificial intelligence technology. Aiming at the issues in BP network, this work constructs IPSO by improving the weight decay strategy and learning factor of PSO algorithm. Then, it uses IPSO to optimize the BP to construct IPSO-BP. Third, the results of the experiments in this work suggest that the strategy proposed here is both feasible and preferable.
Tracking Control Method for Double Compound-Combination Synchronization of Fractional Chaotic Systems and Its Application in Secure Communication
Authors: Al Themairi A. , Abed-Elhameed Tarek M. , Farghaly Ahmed A.
Published: September 2022

This paper introduces the chaotic fractional-order Sprott Q system and its dynamics. The double compound combination of eight fractional-order chaotic synchronizations was investigated. This kind of synchronization is considered a generalization of many types of synchronization given in the literature. The analytical formula of the control functions is developed and proven using the tracking control method. As we know the fractional derivatives of two multiple functions are very difficult to calculate, and the tracking control method is more suitable for this kind of synchronization. This technique’s communication is more secure and reliable because there are more drive and response systems. To achieve the proposed synchronization, four driving and four response identical chaotic systems are used as an example. This scheme may be used in many applications such as secure communications and safe information. Using the proposed double compound-combination synchronization is an example given to encrypt a message. The analytical results were confirmed by numerical simulation, and we found good agreement.
[Retracted] Research on Microseismic Source Location Method Based on Waveform Characteristics Monitored by Nanomaterial Sensor under the Background of Metal Oxide Polluted Environment
Authors: Wang Shuai , Hu Zhaoxin , Lu Shasha
Published: September 2022

In recent years, the monitoring range of source location technology has developed from being one‐dimensional and two‐dimensional to being three‐dimensional. However, due to the complexity and nonuniformity of the seismic wave propagation medium and the uncertainty of the propagation law, there will be large errors in the source location results. Therefore, the analysis of vibration signal has become the key problem of current research. This paper designs a microseismic monitoring system based on Internet of Things sensors, which can monitor the vibration wave characteristics of vibration signals. In order to test the positioning accuracy of the system, this paper introduces three positioning methods: target positioning method based on time difference, time delay estimation method based on EMD, and source target positioning method based on the characteristic frequency of vibration signal. The purpose of this paper is to find the most accurate method from the three source location methods. Through these three methods, the vibration source generated by a single person walking in situ can be located in the vibration positioning experiment of human walking. The error between the actual position and the measurement source position is compared. The results show that the time delay estimation method based on empirical mode decomposition has the highest positioning accuracy. In addition, in the microseismic experiment, it is proved that the positioning accuracy of EMD using L1 norm statistical criterion is higher than that using L2 norm statistical criterion.
An Improved Phase Space Reconstruction Method‐Based Hybrid Model for Chaotic Traffic Flow Prediction
Authors: Hou Yue , Li Da , Zhang Di , Deng Zhiyuan
Published: September 2022

Traffic flow is chaotic due to nonstationary realistic factors, and revealing the internal nonlinear dynamics of chaotic data and making high‐accuracy predictions is the key to traffic control and inducement. Given that high‐quality phase space reconstruction is the foundation of predictive modeling. Firstly, an improved C‐C method based on the fused norm search domain is proposed to address the issue that the C‐C method in the phase space reconstruction algorithm does not meet the Euclidean metric accuracy and reduces the reconstruction quality when the infinite norm metric is used. Secondly, to address the problem of insufficient learning ability of traditional convolutional combinatorial modeling for complex phase space laws of chaotic traffic flow, the high‐dimensional phase space features are extracted using the layer‐by‐layer pretraining mechanism of convolutional deep belief networks (CDBNs), and the temporal features are extracted by combining with long short‐term memory (LSTM). Finally, an improved probabilistic dynamic reproduction‐based genetic algorithm (PDRGA) is proposed to address the problem of the hybrid model falling into a local optimum when learning the phase space law. Experiments are conducted in three aspects: phase space reconstruction quality analysis, comparison of optimization algorithm convergence, and prediction model performance comparison. The experimentation with two data sets demonstrates that the improved C‐C method combines the advantages of the high accuracy metric of the L2 norm with the low operational complexity of the infinite norm, achieving a balance between reconstruction quality and algorithm efficiency. The proposed PDRGA optimization algorithm is a lightweight improvement of the traditional genetic algorithm (GA) and solves the problem that the model tends to fall into a local optimum by optimizing the initial weights of CDBN. Meanwhile, the five error evaluation indexes of the proposed PDRGA‐CDBN‐LSTM hybrid model are lower than those of the baseline model, providing a new modeling idea for chaotic traffic flow prediction.
Empirical Analysis on Unexpected Information Effect of Monetary Policy and Stock Price Fluctuation: Taking Military and Defense Enterprises as an Example
Authors: Lyu Yifei , Zeng Hao , Wang Jie , Zhang Yuhua
Published: September 2022

The existing studies have shown that the unexpected information effect of monetary policy (IEOMP) would affect the fluctuation of stock prices. This paper tests the effect of unexpected IEOMP on stock prices in China. Taking Chinese listed military and defense enterprises, for example, the author further explored how the influence of unexpected IEOMP over stock price varies between industries. The empirical analysis reveals that, despite the industrial heterogeneity of the said influence in China, the stock price of Chinese listed military and defense enterprises is still affected by unexpected IEOMP. Our analysis enables Chinese military and defense enterprises to build a more robust market mechanism for the military and defense market, open more effective channels for the industrial chain, capital chain, and private capital, and drive the national economy with the sci‐tech results of defense. In addition, policymakers are recommended to keep an eye on the price fluctuation in the stock market and important economic information from stock price fluctuation in order to effectively identify the potential risks in the future economy and to improve the predictability of the feedback mechanism of monetary policy.
Curriculum Setting Method for Special Education Specialty Integrating Projective Geometry
Authors: Sun Lishuang , Zhao Xin , Ju Yuanyuan
Published: September 2022

In recent years, education has been paid more and more attention by the society, and the concept of “education without discrimination” has gradually taken root in the hearts of the people. Special education is a form of education for special groups, which embody the fairness of education. Different from the conventional education model, special education often pays more attention to the physical and mental development of special populations, so the curriculum setting method of special education major is also different from the general method. Under this circumstance, how to carry out reasonable curriculum setting has become the core problem that needs to be solved in the reform of special education curriculum. Projective geometry is one of the methods of studying graph transformation, and its core is the principle of projective transformation invariance. Under the guidance of this theory, curriculum reform also presents many invariable characteristics. Based on this, this paper proposed a special education professional curriculum setting method integrating projective geometry, aiming to reform the special education professional curriculum setting strategy by using the invariant theory. In the evaluation of curriculum setting, the article analyzed the effect of new curriculum setting methods on special groups from different dimensions, and preliminarily formulated the initial special education curriculum. It can be concluded from the article in the evaluation grades that with the blessing of the new curriculum setting method, the student's health evaluation reached 2.7, a year-on-year increase of 42.1%. This fully shows that in the course of special education curriculum setting, projective geometry can provide novel ideas and directions for new curriculum setting methods.
TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network
Authors: Alam Md. Shahinur , Kwon Ki-Chul , Md Imtiaz Shariar , Hossain Md Biddut , Kang Bong-Gyun , Kim Nam
Published: September 2022

Air-writing is a growing research topic in the field of gesture-based writing systems. This research proposes a unified, lightweight, and general-purpose deep learning algorithm for a trajectory-based air-writing recognition network (TARNet). We combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network. The architecture and applications of CNN and LSTM networks differ. LSTM is good for time series prediction yet time-consuming; on the other hand, CNN is superior in feature generation but comparatively faster. In this network, the CNN and LSTM serve as a feature generator and a recognizer, optimizing the time and accuracy, respectively. The TARNet utilizes 1-dimensional separable convolution in the first part to obtain local contextual features from low-level data (trajectories). The second part employs the recurrent algorithm to acquire the dependency of high-level output. Four publicly available air-writing digit (RealSense trajectory digit), character (RealSense trajectory character), smart-band, and Abas datasets were employed to verify the accuracy. Both the normalized and nonnormalized conditions were considered. The use of normalized data required longer training times but provided better accuracy. The test time was the same as those for nonnormalized data. The accuracy for RTD, RTC, smart-band, and Abas datasets were 99.63%, 98.74%, 95.62%, and 99.92%, respectively.
Identification of the Hub Genes and Potential Regulation Network in Chronic Hepatitis B via Bioinformatics Analysis
Authors: Fan Jingjing , Chen Yong , Zhang Wei , Zhou Xiaoying , Bai Xue , Chang Caifang , Han Yongping , Liu Jinlu
Published: September 2022

Background. Chronic hepatitis B (CHB) is a serious infectious disease which is induced by hepatitis B virus (HBV) infection. This project was conducted to reveal the potential mechanism in CHB development via analyzing the public clinical data. Methods. GSE33857 and GSE110217, obtained from the GEO database, were used for bioinformatics excavation. Briefly, the raw data of GSE33857 and GSE110217 were analyzed with the GEO2R, and then the expressed matrix files were generated. The matrix files was visualized as heat map with

R

software. The targets of the miRNAs were analyzed with the miRDIP database. The functional annotation and pathway enrichment were performed using “clusterProfiler” package in

R

software. The STRING database was utilized to analyze the interaction of the DEGs, and the PPI and miRNA-mRNA network were established according to the related results. Results. 93 downregulated genes and 17 upregulated genes in GES33857, and 111 downregulated and 40 upregulated genes in GSE110217 were identified as the hub nodes. The targets of the DEGs in the datasets were enriched in PI3K/AKT and MAPK pathways and associated with transcriptional regulation. Moreover, PPI and miRNA-mRNA networks were also established with the DEGs and related targets in the datasets. miR-122-5p, miR-125b-5p, miR-136-5p, miR-194-5p, miR-139-5p, miR-140-5p, miR-181a-5p, and miR-29b-3p were identified as the potential biomarkers in CHB. Conclusion. Eight miRNAs, including miR-122-5p, miR-125b-5p, miR-136-5p, miR-194-5p, miR-139-5p, miR-140-5p, miR-181a-5p, and miR-29b-3p, were identified as the potential biomarkers in CHB, and the PPI and miRNA-mRNA networks were also established.
[Retracted] Judicial Application of Punitive Damages for Ecological Environment Infringement in Civil Public Interest Litigation
Authors: Chen Yuanqing
Published: September 2022

This study aimed to explore the judicial application of punitive damages for ecological environment infringement in civil public interest litigation.Methods. Combined with a case, the judicial application of punitive damages for ecological environment infringement in civil public interest litigation was analyzed.Results. After thinking and judgment, the total cost of repairing the enterprise plot in the case was 2168000 yuan, the cost of environmental functional loss was 57135.45 yuan, the cost of taking relevant emergency measures such as new drinking water and laundry Wharf Engineering was 532860.11 yuan, and the cost of testing and identification was 95670 yuan.Conclusion. The judicial application should be fully considered in the process of civil public interest litigation to ensure that the punitive compensation for ecological environment infringement is more reasonable.
Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode
Authors: Tang Qi , Wu Baotong
Published: September 2022

With the dynamic sharing of global resources shifted from the independent production of a single enterprise to and the development of collaborative manufacturing, today’s manufacturing industry has collaborative production among multiple enterprises. This paper extends the optimization method of the multienterprise dynamic equipment collaborative scheduling system with the dynamic manufacturing network theory approach, combining the neural network system and model predictive control theory to continuously and dynamically diagnose the changes of demand and capacity in the actual production process and design the corresponding multilayer game collaborative optimization robust model according to the uncertainty relationship. The study considers the production characteristics and management needs of multiple enterprises within the manufacturing industry, as well as the optimization of the dynamic equipment collaborative scheduling system, uncertain demand, dynamic equipment matching effectiveness, and equipment failure under the shared manufacturing model, deduces the market capacity demand for manufacturing enterprises by inverse prediction of dynamic market demand through the simple recurrent neural network (Elman network), and then maximizes the idle equipment utilization and load capacity under the shared manufacturing model in model rolling optimization. This paper gives model solutions based on big data analysis and combines improved population intelligence algorithms to determine the dynamic matching of manufacturing resources, product supply and demand paths, and transportation flows. The simulation results show that the application of model predictive control theory to the model of Elman neural network system diagnosis can effectively reduce the adverse effects of the two-way uncertainty environment on the profitability of enterprises. Meanwhile, considering the maximum performance matching of dynamic equipment scheduling and combining with robust optimization methods will reduce the cost loss of manufacturing enterprises in the worst case, and the cost loss reduction can reach 24.73%. The analysis results verify that the method is of great practical significance in the direction of intelligent production collaboration and industrial digital transformation.
Analysis of Urban Rail Express and Local Lines Parking Scheme Based on Elastic Demand
Authors: Li Ningchuan
Published: September 2022

With the rapid economic development and the continuous expansion of the city scale, the construction of urban rail transit has also been greatly developed. The parking scheme in urban rail transit, as one of its core components, has also received more and more extensive attention. Therefore, in order to effectively solve the parking problem of express trains and local trains in urban rail transit, this paper constructs a two-level parking planning model for express trains and local trains in urban rail transit based on the demand elasticity of passenger flow. The model constructed by the upper level stops mainly aims at maximizing the operational benefit, with the lower limit of the train stops as the constraint; the routes of the lower level stops are designed to minimize the total travel time and limit the continuity and feasibility of the passenger path. Finally, the method proposed in this paper is verified by an example, which shows the usability and effectiveness of the model and proves that the high-speed local mobile parking scheme is more suitable for the passenger flow with special spatial distribution. It provides a certain reference value for the optimization of urban rail transit express and slow line parking scheme.
Construction of Integrated Platform for Eco-Sustainable Tourism and Marketing in Southeast Asia Based on Machine Learning
Authors: Chen Yi-Tui , Li Yi-Min , Tseng Tzu-Wen
Published: September 2022

Southeast Asia is one of the most popular tourist destinations in the world, and its tourism model is mainly based on ecological and sustainable tourism characteristics. It is a special practical exploration of this region. However, with the rapid development of the tourism industry, the structural contradictions in the tourism industry have become increasingly prominent, coupled with the weak link of tourism development, making the development of tourism unbalanced. Therefore, this study proposed a platform-related algorithm based on machine learning to build the integration of eco-sustainable tourism and marketing in Southeast Asia and used a collaborative filtering algorithm and a route generation algorithm to recommend information or predict specific needs for specific tourists. The experimental results of this study showed that the difference between different loss functions of the improved transfer algorithm based on machine learning is not significant, which indicated that the method proposed in this study is robust to the choice of a loss function. Compared with the hinge loss function and the logarithmic loss function, the exponential loss function has the highest average classification accuracy of 0.91, of which the average accuracy of the logarithmic loss function is 0.85, and the average classification accuracy of the hinge loss function is 0.79. Therefore, applying the migration algorithm to the architecture of the platform is conducive to the stability of the dataset and to the better display of pages for tourists.