bluuebunny/crossref_metadata_2025_split huggingface dataset:
Untargeted metabolomic profiles reveal widespread metabolic perturbations and identify candidate biomarkers in aminoaciduric canine hypoaminoacidemic hepatopathy syndrome
Authors: Loftus John P. , Rubio M. Elena Diaz , Yant Jillian , Bichoupan Allison , Zhang Sheng , Miller Adam J. , Center Sharon A. , Ruiz Maria D. R. , Macho Luis P. 
Published: October 2023

Abstract

OBJECTIVE
To identify metabolites and metabolic pathways affected in dogs with aminoaciduric canine hypoaminoacidemic hepatopathy syndrome (ACHES) compared to healthy control (CON) dogs of similar ages and breeds. To improve our understanding of ACHES pathophysiology and identify novel candidate biomarkers associated with ACHES.

ANIMALS
A prospective case-control study. Privately owned dogs with ACHES (n = 19) and healthy (CON) dogs (n = 9) were recruited between February 18, 2015, and April 18, 2018.

METHODS
A prospective case-control study. Plasma and urine were collected from ACHES and CON dogs. The Cornell University Proteomics and Metabolomics Core Facility conducted an untargeted metabolomic analysis.

RESULTS
After controlling for age, sex, and weight, 111 plasma and 207 urine metabolites significantly differed between ACHES and CON dogs. Data reduction and cluster analysis revealed robust segregation between ACHES and CON dogs. Enrichment analysis of significant compounds in plasma or urine identified altered metabolic pathways, including those related to AA metabolism, cellular energetics, and lipid metabolism. Biomarker analysis identified metabolites that best-distinguished ACHES from CON dogs, including pyruvic acid isomer and glycerol-3-phosphate in the plasma and an alanine isomer and choline in the urine.

CLINICAL RELEVANCE
Our findings provide an in-depth analysis of metabolic perturbations associated with ACHES. Several affected metabolic pathways (eg, lipid metabolism) offer a new understanding of ACHES pathophysiology. Novel candidate biomarkers warrant further evaluation to determine their potential to aid in ACHES diagnosis, prognosis, and treatment monitoring.
Deep neural networks for multimodal perception and human-computer interaction technology in art design
Authors: Zhang Yamin 
Published: October 2023

Abstract
The first part of this paper examines the aesthetic and application advantages of art design using human-computer interaction technology and develops a multimodal perceptual human-computer interaction system for art design. Multimodal data is obtained using multi-scale convolutional kernels for acoustic feature extraction and deep convolutional neural networks for multiple interaction image feature fusion. Finally, a test analysis is conducted to verify the system's effectiveness in this paper. According to the results, the system has an average wake-up success rate of 99.51% and a wake-up response time of 0.3665 seconds. Implementing human-computer interaction technology and deep neural networks in art design is effective and promotes the development of art design.
Analysis of mental health status assessment of college students based on interpolation and fitting algorithm
Authors: Ou Juhu 
Published: October 2023

Abstract
For common psychological problems among college students, the current mental health assessment methods are analyzed, and the least squares method of curve fitting is chosen for this paper. Based on the interrelationship between personality traits and mental health and the correlation between perceived campus climate and mental health inequalities, factors affecting the mental health status of college students were identified. The OLS and 2SLS algorithms, as well as the stepwise addition of control variables, were used for empirical testing, and a binary regression analysis model was established for individual UPI score values and mean UPI score values, and the results showed a peer effect coefficient of 0.654, which verified the existence of a significant positive peer effect of campus environment on college student’s mental health level.
The embodiment and influence of game theory in the choice between the realization of self-worth and social value in life
Authors: Ma Tianjiao 
Published: October 2023

Abstract
The application and influence of game theory in the choice between achieving self-worth and social value in life. Different game rules dictate the decision to maximize self-interest, and self- and social values are considered to reach the Pareto optimal state. A reasonable allocation of self-value and social value is achieved using the crosswalk game as a reference. The results show that using the game theory model can yield a maximum value of 0.0001 for the rate of change, indicating an interaction between life self-value and social value, which helps to make subsequent timely responses to improve self-cultivation and strengthen normative behavior.
A study on the psychological assessment of young children based on multivariate statistical analysis
Authors: Tang Shangjie , Cao Yuewang 
Published: October 2023

Abstract
To extract the main characteristics of the original psychological assessment data, we first set up random variables in this paper. The probability values of the parameters are obtained based on significance tests after decomposing the covariance moments. The regression coefficients of the data are predicted using logistic regression after normalization. The accuracy of the assessment was improved by optimizing the parameters of different assessment indicators. Ninety-five children in a kindergarten were selected, and the highest score in the hyperactivity test based on multivariate statistical analysis intervention was 4, and the score indicators of the undesirable factors test all decreased substantially. Implementing psychological assessment of young children based on multivariate statistical analysis can effectively improve their adverse psychological problems and thus improve their psychological health.
An Exploration of the Reform of English Informatisation Teaching in Colleges and Universities Based on Deep Learning Model and Microteaching Mode
Authors: Zhang Boyu 
Published: October 2023

Abstract
In this paper, we use the cross-layer connectivity of residual networks in deep learning to convert convolutional and fully connected layers into sparse connections and cluster sparse matrices into relatively dense subspaces. Extracted features are used to perform target class prediction and regression of target coordinates using a target detection algorithm to meet the demand for real-time target detection. The model's use resulted in a head-up rate of 83.57% in the classroom, with the least serious students at 0.8 and above. Deep learning technology can enhance students' learning experience in English classrooms by providing personalized learning and a deep learning environment.
Research on heritage and innovation of minority landscape cultural heritage based on VR technology
Authors: Yan Xue 
Published: October 2023

Abstract
The distance between sample sets is calculated in this paper using VR technology by combining the K-means clustering algorithm to select data objects. Continuous optimization of the objective function leads to optimal clustering, expressed as the clustering effect. The output is performed at the cluster center of each data point, and the density function of the target nodes is sorted based on the given parameters to calculate the neighborhood map of the data points and create the matrix. Based on the perception degree test before and after the innovation of landscape cultural heritage inheritance by VR technology, the overall perception degree of residents and tourists rose to 8 after the innovation. VR technology can help expand the protection and inheritance of minority landscapes and promote the sustainable development of traditional culture.
A study on the influence of fashion advertising style on consumer perception based on big data technology
Authors: Luo Jing 
Published: October 2023

Abstract
This paper establishes a dataset through big data mining algorithms, obtains data objects in the subspace according to data outlier characteristics, and derives the subspace outlier probability formula. This paper establishes a dataset through big data mining algorithms, obtains data objects in the subspace according to data outlier characteristics, and derives the subspace outlier probability formula. The data distribution characteristics are analyzed using entropy detection, and the multi-information entropy data are extracted for clustering detection by inputting the number of database samples for differential evolution. The adaptive search method is used for feature extraction of big data information flow, and text data with similar characteristics are divided into uniform fuzzy clustering centers to mine the optimal clustering indicators. Four categories of fashion photography styles were obtained by clustering fashion advertisement photos through data mining. Big data technology positively impacted the influence of consumers' consumption concepts, as their social self-concept increased from 4 to 10 points.
Research on the construction and practice of the evaluation system of teachers’ morality in colleges and universities based on statistical analysis method
Authors: Liu Kai 
Published: October 2023

Abstract
In this paper, observations and data analysis of multiple impact variables are performed by cluster analysis algorithm, and data sets are set up using statistical analysis methods to calculate similarity measures to obtain weight values. The evaluation indexes are categorized to constrain index data analysis, and the outlier degree of this data object is calculated based on the attribute value weights. Factor scores were obtained for each sample in the evaluation index data set by the statistical analysis method to derive the optimal automated evaluation model. The results show that the indicators of the satisfaction evaluation system are all 60% and above, and the highest indicator of teachers’ love and dedication is 90% and the statistical analysis method can build a teacher ethics evaluation index system that meets the actual needs.
Assessing the Sustainability Level of European Cultural and Creative Industries Based on Entropy Method
Authors: Liang Chenxi , Abdela Rawi , Meng Ziyu 
Published: October 2023

Abstract
This paper aims to highlight the sustainable development level of European cultural and creative industries and their impact on national economic growth and establish a sustainable development assessment model based on the entropy weight method. The influence of different quantiles on the decision results is eliminated by standardizing the indicators. The objective evaluation method utilizes the entropy principle to avoid bias caused by human factors. The comprehensive evaluation method of sustainable development is utilized to gauge the trend and direction of enterprise development. It has been found that the annual added value of the cultural and creative service industry in Europe in 2022 is 226.976 billion yuan. It is an effective development strategy to promote economic growth in Europe, with an increase of 10.8% over the previous year.
Research on the transformation and innovation of visual art design form based on digital fusion technology
Authors: Du Yingbei 
Published: October 2023

Abstract
This paper constructs a three-dimensional object visual perception space using digital fusion technology to enhance the impact of art design works. A large amount of homogeneous or different visual art design information collected is fused, and data processing and fusion methods are used to reduce redundant data, establish a reasonable knowledge base to explain the information and derive the maximum support degree. The mean square error is calculated according to the adaptive weighted data fusion, and the trust degree function and likelihood function are introduced to obtain the total trust degree of visual art design innovation types. The results show that the beauty indexes become nonlinear, the fitted correlation indices are all greater than 70%, and the digital fusion technology can prompt the artworks to be more outstanding in visual effects.
Analysis of Adaptive Morphology for Visualization of Information Communication in Advertising Design
Authors: Bian Zhuo 
Published: October 2023

Abstract
This paper uses adaptive morphology to construct an adaptive elliptic structure element that changes adaptively according to the image’s local neighborhood information. To calculate convolution, a Gaussian kernel with the outer product of the image gradient is used to achieve the diffusion of the linear matrix values of the initial matrix. The spatial distance of the pixels determines the size of the structural elements, and the pixel points that do not exceed the threshold are selected. The adaptive morphology method resulted in a 27.7 difficulty level for visual communication design, and the algorithm’s accuracy was 87.18%. Advertising design can break through dimensional limitations in visual space through adaptive morphology to generate interaction, indicating a path for development.
Data decision based promoting effect of non-destructive detection skill competition on detection technology course teaching in colleges and universities
Authors: Sun Xudong 
Published: October 2023

Abstract
The construction of a non-destructive testing skills competition system is based on data decision technology in this paper. The distribution of each data pheromone is initialized using the Euclidean distance algorithm, and the probability value of each parameter is calculated using weighting coefficients. Then, the decision tree algorithm is used to sort the gain nodes of each information feature value, and accurate data classification is achieved by continuously updating the information entropy value. According to the final results, the students’ passing rate in the competition is about 79.8% on average, and their cognitive rate has increased by about 54.3% on average. The NDT skills competition system has helped improve the teaching quality of testing technology in colleges and universities.
Analysis of civil and commercial law protection based on decentralized Internet
Authors: Sheng Kun 
Published: October 2023

Abstract
To promote fairness and justice in law, it is important to explore the protective role of civil and commercial law. The relationship between civil and commercial law and public opinion supervision is examined through Internet public opinion. A decentralized Internet public opinion index system has been established, and a better random network public opinion dissemination model is proposed through parameter inversion of the SIR model and network simulation. The protection of transaction security and information rights by civil and commercial law is the focus of the evolution of Internet public opinion cases. The promotion of industry self-regulation is enhanced by 14.63% when civil and commercial law intervenes. On the flip side, it aids in protecting personal information, and the likelihood of personal information leakage is reduced by 25.48%. This study can effectively reference the implementation of civil and commercial law in the Internet era.
Research on the use of computer-based art elements in digital special effects film and television
Authors: Liu Huichen , Jiao Linyi 
Published: October 2023

Abstract
This paper explores the application of “color” elements by combining SD and GoogLeNet models to build a dataset of color emotion imagery for film and television, generating a color emotion adversarial network based on conditional convolution, and designing a deep learning-based color emotionalization method. A feature extraction method is used to explore the application of graphic elements in digital effects films and television. Color features were extracted using the color histogram and color moments, the texture was extracted using the CLBP algorithm, and EEG signals were extracted using the CSP method. After analysis, it is concluded that as the film and TV timeline progresses, the value of RGB of color increases from (10,200) to (30,250), and the hue becomes heavier to pave the way for the restless and negative emotions later. The primary effects of graphical emotions were significantly smaller than 0.05 and were capable of causing emotional changes.
A study on the construction of an online interactive teaching platform for English in colleges and universities based on the fusion of multi-source and multi-objective statistical information
Authors: Sun Ying 
Published: October 2023

Abstract
This paper firstly combines English teaching in colleges and universities and big data technology and proposes the information technology of English teaching in colleges and universities based on the fusion of multi-source and multi-objective statistical information and conducts research on the traditional teaching optimization algorithm and multi-task teaching optimization algorithm to promote English teaching in colleges and universities. Secondly, in the “teaching” phase, teachers and teaching assistants teach according to the difference between their average level and the class average to increase the value of each student’s factor and increase individual exploration and group diversity in the “learning” phase. Then, the English teaching platform was designed and studied regarding system structure and platform function, simulated and analyzed. The results show that the efficiency of the multitasking optimization algorithm is increased by 21.85% compared with the traditional teaching optimization algorithm for the number of concurrent users from 10 to 100 and 16.46% when the number of concurrent users is 100, which has a positive effect on students’ English learning efficiency.
Research on the innovation path of corporate financial management based on improved Adaboost regression algorithm and analysis of management effectiveness
Authors: Li Tingting , Lu Ping , Cao Ying , Xu Wen 
Published: October 2023

Abstract
The first part of this paper investigates the implementation process of the Adaboost regression algorithm, initializes the training set weights to obtain the weak learner, and builds the final strong learner. Then, we explore the treatment of corporate financial management concept drift by assigning variance weights to financial data samples according to their temporal proximity to solve the problem of financial concept drift. Then, an optimized AdaBoost-SVM model is constructed to initialize the sample weights according to time and sample category, calculate the classification error, and build a weak classifier and a decision classifier based on the samples. Finally, the company's financial management costs, personnel, and model prediction accuracy were analyzed. The model predicts financial distress with a 78% accuracy, financial insolvency with a 93% accuracy, and financial normalcy with a 90% accuracy. This confirms the feasibility of this paper's methodology for corporate financial management.
Analysis of Fuzzy Semantic Translation in English Language and Literature Based on Bayesian Nonparametric Model
Authors: Cheng Kai 
Published: October 2023

Abstract
The development of machine learning provides a new path for translating fuzzy semantics into English and literature. In this paper, we propose translation points for fuzzy semantics based on the fuzziness of language by studying the linguistic features in English literature. The hierarchical PY process under the Bayesian nonparametric model is adopted to achieve word alignment in English translation, and the clustering process of English words is controlled by introducing discount and intensity parameters. Based on this, the parameter estimation of the word probability model is completed using Gibbs sampling, and the Moses decoder extracts the translation features. Based on monolingual word classification, this model reduces 6.85% in the average time consumed per sentence compared to the Beam algorithm and 9.57% compared to the A* algorithm.
Fourier transform-based protein fraction analysis of whole-seed wheat
Authors: Jia Dianyong , Xie Yuli , Chang Wei 
Published: October 2023

Abstract
The principle of the Fourier transform is explored in this paper to compare protein structure similarity, transforming protein structure into a distance sequence, and performing spectral analysis on a fast Fourier transform. Secondly, protein fraction classification and similarity analysis of whole-seed wheat protein fractions were performed using the fast Fourier transform. Fourier transform infrared spectra were analyzed using two parameters: diatomic vibrations and molecular leaps. Finally, the whole seed wheat protein fraction content test analysis was analyzed experimentally. The results showed that the spectral range of whole seed wheat protein fractions was selected from 10539 cm
−1 to 6080 cm
−1, the fraction of fractions was determined as 9, the parameter R
2 val was 0.9614. This paper provides practical reference material for researching whole-seed wheat protein fields.
Exploring the path of environmental visual design based on the perspective of new media
Authors: Yin Wenshan , Zhang Boyuan , Wu Xiaolu 
Published: October 2023

Abstract
This paper first constructs a convolutional neural network model by convolutional layer, activation layer and normalization layer and then uses the style migration algorithm and image semantic segmentation algorithm based on the convolutional neural network to construct an environmental visual design model based on the style migration algorithm. The experimental analysis of the effective application of the environmental visual design scheme under the new media perspective is conducted after data pre-processing. The results show that from the total loss function graph after weighting, the function decreases from 0.03 to 0.01 after a small fluctuation, and the style migration imaging tends to a stable state, and the model is optimal now. This demonstrates the exploration of environmental visual design and encourages the innovative development of visual design in the new media environment.
Analysis of the characteristics of skill-based street dance movements based on the improved K-means algorithm
Authors: Luo Yanping 
Published: October 2023

Abstract
This paper first analyzes the K-mean algorithm from the core idea, algorithm process and advantages and disadvantages, then further improves the K-mean algorithm by using Gaussian mixture distribution and constructs the skill-based street dance movement recognition model based on the improved algorithm. Finally, the street dance teaching video is used as an example for dance movement acquisition and data pre-processing, and the recognition accuracy analysis of the street dance movement dataset is conducted based on the improved K-mean algorithm. The average recognition rates of the recognition model in the four data sets of the data set were 72.34%, 74.65%, 73.15% and 86.70%, respectively. This shows that analyzing the characteristics of street dance movements using the improved K-mean algorithm is beneficial for optimizing and improving existing street dance movements.