bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence
Authors: Moon Jun-Ho , Shin Hak-Kyun , Lee Ju-Myung , Cho Sung Joo , Park Ji-Ae , Donatelli Richard E. , Lee Shin-Jae 
Published: November 2023

ABSTRACT

Objectives
To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI).

Materials and Methods
Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared.

Results
On average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively.

Conclusions
PLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.
The Value of Nursing Competence and Staffing for Patients in Critical Care: A Scoping Review
Authors: Stafseth Siv K. , Flatland Sigbjørn , Granerud Ellen , Berg Jan , Hansen Britt Sætre 
Published: November 2023

Background: Nurse competence and staffing in critical care are of interest to hospital managers and politicians, especially if a relationship exists between educated nurses and patient outcomes. Observational data can describe and measure the impact of critical care nursing practices in intensive care units and may be linked to outcomes.
Objective: The aim of this study was to provide an understanding of how levels of nursing competence and staffing in intensive care units are described and associated with nurse-sensitive outcomes.
Methods: A scoping review was conducted. Electronic databases, including CINAHL, Medline, Embase, the British Nursing Index, and SweMed+, were searched for English language studies from 2006 to 2023. The inclusion criteria were nurses working in critical care, nurse staffing ratio or assessment, and nurse-sensitive outcomes for intensive care patients.
Results: Twenty-one studies were included. As only few studies mentioned the educational level of nurses and the studies used several job titles, it was difficult to interpret nursing competence. Studies that used valid tools for workloads often reported significant results of nursing staffing related to nurse-sensitive outcomes. Adverse events increased with higher levels of nurse workload, and some events were also affected by the level of competence. Lower mortality rate was linked to more nursing hours per patient.
Conclusion: We found associations between education, nurse staffing, and nurse-sensitive outcomes in intensive and critical care units. To highlight its effects, advanced nursing competence in critical care and research must be better described. Patients’ need for nursing competence must be assessed using validated tools.
Gypsum amendment influences soil and plant chemical composition temporarily
Authors: Yli-Halla Markku , Taskinen Antti , Ekholm Petri 
Published: November 2023

Field application of gypsum (CaSO4 × 2H2O) is a tool to decrease phosphorus (P) load to watercourses. Effects of gypsum application (4 tons ha-1) on soil and crop composition were monitored up to four years after the treatment in the Savijoki catchment, southwestern Finland. Eleven amended fields and 17 control fields were analyzed for easily soluble P, Ca, Mg, K, S, pH(H2O), electrical conductivity (EC) and loss-on-ignition. Plant samples were analyzed for most nutrients and Se. Downward S transport was monitored in two fields. In the first spring after gypsum application, but not later, S concentrations and EC were substantially but not harmfully elevated in the plough layer, with no other significant effects on soil composition. Sulfur leached out slightly less rapidly from clay than from the coarse mineral soil. Plant S concentration increased for the first and the second year. In conclusion, gypsum application did not have adverse effects on soil or crop chemical composition in this survey of predominantly clay soils.
Application of University English Teaching Model Based on Artificial Intelligence Technology
Authors: Leng Jinying , Lin Xiaoqing 
Published: November 2023

Abstract
This paper is based on cloud computing technology in artificial intelligence and a clustering algorithm controlled to divide teaching resource attributes and reduce packet loss rates in the system. Data mining is used to reduce the number of iterations and improve processing efficiency, a learner model is designed, and the teaching process and assessment tasks are constructed to realize the teaching model. Two natural classes of students at the University of S. underwent an instructional experiment. The results showed that the experimental class reached about 70% of the overall students in the achievement range of 85-95 and 95-100. The college English teaching model based on artificial intelligence technology’s feasibility and effectiveness are proven by the significant improvement in performance compared to the control class.
Modelling simulation of college vocal music teaching development path based on big data analysis
Authors: Li Ya 
Published: November 2023

Abstract
This paper uses big data analysis to determine user similarity and calculate the bias in selecting nearest neighbors. The temporal factor is introduced to fully reflect the changing status of users’ interest degrees so that the recommendation accuracy can be significantly improved. According to the nearest neighbors’ rating of experimental teaching resources, the collected data on the effectiveness of vocal music teaching in colleges and universities are clustered, and the results are reflected using degree weights and biases for updating. It was discovered that seven samples had actual student vocal rating values above 0.5, and the overall vocal test scores could reach 86 or higher. To be more energetic in contemporary times, vocal music teaching in colleges and universities should be reformed and innovated to incorporate big data analysis technology.
Reform and Innovation of Civic and Political Education Based on Deep Enhanced Learning Methods
Authors: Cui Chen 
Published: November 2023

Abstract
The use of an initial state value function and an optimal strategy are used in this paper to solve educational problems based on deep reinforcement learning. Deep reinforcement learning’s approximate function is defined, and the matrix model is created by training tuning using learning methods like gradient descent. To analyze the modeling process of reinforcement learning, reward values are added to the Markov decision transfer matrix and the expected value of cumulative returns is calculated. The weights are trained using the Bellman equation to enhance the algorithm’s stability. In evaluating the effect of reform and innovation in Civic Education, the teacher education concept is rated as 10 points. The reform and innovation of civic education, combined with deep reinforcement learning, can promote the reform of education and teaching modes, improving the efficiency and quality of education.
Analysis of Civic and Political Quality of Preschool Education Curriculum Based on SWOT Analysis and Its Corresponding Strategies
Authors: Li Weiyan , Zhang Yiwei 
Published: November 2023

Abstract
Based on SWOT analysis, this paper combines data mining algorithms to analyze the quality of preschool education curriculum Civics and propose corresponding strategies. First, the internal and external strengths, weaknesses, opportunities, and threats to the quality of preschool education curriculum Civics and Politics are identified through SWOT analysis. Then, data mining algorithms are used to extract numerical attribute features and categorical attributes, and adaptive optimization methods are used to derive adaptive weighted edge vectors to obtain the probability density function of the quality of course ideology. Finally, the study found that the overall satisfaction of teachers and students was more than 70% based on a SWOT analysis. Corresponding countermeasures are proposed to provide an important reference for developing preschool education programs.
Design of virtual simulation practice teaching platform for ideological and political course based on Perceptron model
Authors: Tao Enqian , Jiang Hong 
Published: November 2023

Abstract
To improve the teaching effect of Civics courses in colleges and universities, this paper uses multi-attribute regression to compute the nonlinear representation ability, combined with the perception machine deep learning network, to get the teaching resources and interaction mechanism of Civics courses. The weighted k-shell decomposition method is used to determine the weighted undirected graph of the Civics and Political Science course, and the shortest path between nodes is used to map the link to complete the teacher’s teaching feedback and personalized teaching. The results show that the hit rate and normalized gain rate of the proposed method in the Civics course dataset in colleges and universities are as high as 0.58 and 0.39, respectively, and the performance of the Civics course in colleges and universities is excellent, which indicates that the proposed method can promote the cultivation of students’ quality of Civics and politics, and it has great significance in the field of teaching and learning.
Employment Education and Psychological Work of Students in Lower Colleges and Universities Based on the Hierarchical Analysis Method
Authors: Gong Nanhui , Yang Bosen 
Published: November 2023

Abstract
This paper establishes a platform interaction model in the context of Internet+ to enhance the motivation of college students to take the initiative to receive employment and entrepreneurship guidance and training. A psychological intervention model regarding hope, optimism, self-efficacy, and resilience is constructed. A new method of fuzzy number ordering determined by the limit coefficient can be obtained by defining the limit coefficient of triangular fuzzy numbers. Hierarchical analysis was used to analyze the employment choice dilemma faced by fresh university graduates and to guide them in making the correct employment choices. The results show that the total mean score of positive psychological capital is 119.99, which indicates that the level of positive psychological capital among college students is high. Employers are very satisfied with the graduates’ operational application ability, which reaches 35%.
Current Situation and Mode Innovation of Physical Education Teaching in Colleges and Universities under the Perspective of Deep Learning
Authors: Wang Hui 
Published: November 2023

Abstract
This paper analyzes the current situation of physical education teaching in colleges and universities from the perspective of deep learning and innovates the teaching methodology. A multidimensional data array is used to quantify students’ interests and hobbies to create a recommendation module for intelligent sports learning materials. Using the SIFT feature extraction algorithm, identify the extreme value point of the detection target and develop the sports target extraction module. Through distance transformation and morphological features, determine the skeleton image features to construct a college sports teaching model using deep learning. The results show that the model in this paper enables the coverage rate of sports courses to reach over 97.20%, with a fluctuation of no more than 1.5%. Deep learning has contributed to some innovation in college sports teaching.
Effective combination and application path of computer big data analysis and cloud computing network technology
Authors: Liu Shuye 
Published: November 2023

Abstract
In this paper, based on the data mining algorithm to obtain a collection of big data attributes, the information entropy detection is used to analyze the data distribution characteristics, and the clustering center is extracted. Normalize the feature data, calculate the judgment matrix in the mining process based on the condition set, and perform normalization. The redundant features are extracted from the data to be mined, and the weight vector value of each feature is obtained to obtain the processed feature subset. The results show that the importance score and security score are above 6. The combination with cloud computing network technology in computer big data analysis helps improve data processing efficiency and provides more reliable data support for social and economic development.
Application of 3D visual perception-driven design of velvet flowers in traditional dresses
Authors: Fan Li 
Published: November 2023

Abstract
This paper firstly analyzes the specific application of velvet flowers on women’s dresses from the perspective of fashion aesthetics and with the understanding of the background and development trend of dresses. Secondly, based on 3D visual perception, we propose the optimization of the normalization method and loss function optimization, which can improve the migration speed and generation quality of velvet flower design, and then seamlessly fit it to the dress base map by Poisson fusion to realize the intelligent design of velvet flower in dresses driven by 3D visual perception. Then, the color features and attributes of the floral in the dress are studied, and the HSV color space model is used to extract the high-frequency colors and attributes of the dress. Then, the floral design is digitized, and the dress design is driven by 3D visual perception is studied and analyzed. The results show that the loss value of the dress design style migration network trained based on AdaN is the smallest, and the loss value is reduced by 0.19 compared to the dress design style migration network using IN and 0.31 compared to BN. The velvet flower design on the dress design base map can be implemented based on the foundation provided by this study.
Reinforcement Learning Technology Applied to the Innovative Model of Informatisation of Classroom Teaching in Educational Economics
Authors: Chen Liu , Lyu Jie 
Published: November 2023

Abstract
This paper constructs the architecture of the online teaching platform for education economics based on the MVC pattern coupled with three-tier architecture. The optimal value function is solved through the experience playback mechanism and the strategy function of reinforcement learning, and the value function is used to realize the update of the deep Q learning parameters, which is used to realize the guided teaching of students in the platform. A test analysis was conducted to verify the effectiveness of using the online teaching platform for education economics courses. The results show that the waiting time for teaching resource requests in the 0-3s range of this paper’s platform is 29.71% and 51.56% lower than that of the B/C and Web platforms, respectively. The mean value of students’ learning attitude toward the platform was 108.558, and the highest satisfaction score was 0.7088. The online teaching platform can effectively improve students' learning attitude and promote the innovation of information technology in education economics classroom teaching.
A model of integrating college students’ consumerism education and ideological and political education by applying multivariate data fusion
Authors: Liu Pei 
Published: November 2023

Abstract
This paper explores the relationship between college students’ consumption views and their consumption views under Civic Education in the context of multiple datasets. Secondly, a multi-agent, multi-source heterogeneous data collection model is used to collect data on consumption view and Civic Education. A CNN model is designed to study the correlation between consumption view and Civic Education. The CNN and LSTM multi-grain data classification models are used to fuse the features of consumer outlook and Civic Education, respectively. Then, build an online learning platform for college students’ consumerism. Finally, the platform’s fusion effect and learning results for college students were analyzed and studied. The average JS index of data fusion was 93.32%, and the fusion effect was good. The total score of students’ pre and post-test consumerism scores increased from 86 to 100, and the effect of college students’ consumerism education was positive.
The Construction of English Education Resource Base for Colleges and Universities Applicable to Entropy Power-Cloud Modeling
Authors: Zhang Yunyun 
Published: November 2023

Abstract
In this paper, firstly, the weights of evaluation indexes are determined using the entropy weight method, and the characteristics of the cloud model are analyzed to establish the entropy weight-cloud evaluation model. Then, the general structure of the educational resource library system and the system architecture were established based on the utilization requirements, and the functional classification of the educational resource library system was realized. Finally, the entropy-weight-cloud model was used to analyze the motivation for using the resource library, the performance of the system, and the user’s satisfaction with its use. The results show that the mean value of the performance evaluation index is stable between 3.8 and 4.5, the standard deviation is between 0.789 and 0.978, and the users are consistent with a higher performance evaluation index. The mean values of resource quality range from 3.293 to 3.87, and the mean values of system support services range from 3.38 to 3.59. Users’ satisfaction with the English education resource base is high. The construction of educational resource libraries, crucial for reforming university education, is provided as a reference in this study.
Optimisation of Master’s Degree Programmes in Translation Based on Multi-Attribute Decision-Making Approach
Authors: Tang Wenqian , Chen Lidong , Xie Gengquan 
Published: November 2023

Abstract
In this paper, first of all, in the quality assessment of the teaching quality of the translation course, it is proposed to use a multi-attribute decision model to assess the quality of the set of alternatives for all the courses and to use the existing decision ideal points to rank and select the best of a set of optimized solutions for the translation course through the hesitant fuzzy number and objective optimization model. Finally, to explore the optimization problem of the curriculum, a research analysis was conducted on MTI master’s students who recently graduated from the institution. The results show that by optimizing the MTI course specifically, the students’ positive interest (p
1 = 0.03) and independent inquiry interest (p
2 = 0.01) are less than 0.05, showing significant differences (p<0.05). The special optimization scheme of the MTI course proposed in this paper can reflect some problems worth pondering in the curriculum and provide a guiding reference for optimizing the MTI course in schools.
Characteristics of life space remodelling in tourism-based villages during rural gentrification based on the coupled coordination degree model
Authors: Xu Yuhui , Chang An , Han Lu 
Published: November 2023

Abstract
This paper first takes the process of rural gentrification as the main line, selects the village of Annoyed bun in Hohhot City, Inner Mongolia Province, as the case area, conducts research on the characteristics of the remodeling of the living space of the village of the annoyed bun, and proposes the theoretical system of rural gentrification, the governance structure and the mechanism. Secondly, the research on tourism-based villages based on the coupling coordination degree model is carried out to determine the evaluation factors in the coupling index system of tourism-based villages according to the needs of tourists and then construct the evaluation index system of the coupling coordination degree model. Then, the entropy value method is used to calculate the weight of each index, and on the result of the weight of each index, the coupling coordination degree formula can be used to derive the comprehensive evaluation index and the fusion coordination degree value, and then analyze the example of the tourism-type village based on the coupling coordination degree model. The results show that on the value of the degree of integration and coordination, in the three years from 2015 to 2017, Hohhot City, Hohhot City, annoying package village ecology and tourism-type villages mainly show the role of mutual promotion, Hohhot City, towards the direction of mutual promotion between the two. This study promotes the revitalization, reconstruction and reorganization of village life, which objectively becomes a new path for constructing new rural areas and rejuvenation in China.
Exploring the development of students’ sports interests in college smart physical education based on cluster analysis
Authors: Gao Baohua 
Published: November 2023

Abstract
This paper firstly analyzes the smart campus, gives the six-dimensional assessment model of the smart campus, and understands that college smart sports are a new type of manifestation of smart sports in colleges and universities. Secondly, it constructs the intelligent sports education platform model of colleges and universities based on cluster analysis and utilizes the K-means algorithm to calculate the metrics and analyze the data by clustering. Finally, taking the college students majoring in physical education at University B as the research object, the students’ sports interest cluster analysis was conducted based on the cluster analysis of the student’s application of the smart sports education platform. The results show that the horizontal coordinate K=3 tends to level off, which is the recommended optimal K value, and the clustering can be completed by using the K-Means clustering method, and the average standardized scores of each clustered variable of athletic students’ sports interests are the highest, and all of them are positive, which are 0.6, 1.2, 1, 0.8, and 0.85, respectively.
Integrative Development of Modern Literary Works and Traditional Culture Combined with Semantic Association Network Modeling
Authors: Su Hong 
Published: November 2023

Abstract
The coupling framework of modern literary works and traditional culture is first discussed in this paper, and the intrinsic connection between them is examined. Secondly, a semantic-associated information extraction network model is constructed using LSTM and attention mechanism, and the target semantic fusion is achieved through semantic space conversion and semantic-associated information extraction. Finally, the dataset and empirical analysis confirm the SAIEDMMA model’s effectiveness. The results show that the F1 value of the SAIE-DMMA model on the Total-Text dataset and ICDAR2015 dataset is 85.59% and 87.72%, respectively. The traditional culture of folk culture has the highest degree of integration in modern literature, and the growth of its literature from 2017 to 2021 is 1,273 books. This shows that the semantic correlation information extraction network can analyze the semantics of the integration of modern literary works and traditional culture, and it can also be used to promote the effective inheritance and development of traditional culture so that modern literary works are closer to public life.