bluuebunny/crossref_metadata_2025_split huggingface dataset:
DNA Methylation Biomarkers in Cancer Diagnostics
Authors: Ivana Baranova , Marek Samec , Renata Pecova , Martin Pec , Zuzana Dankova 
Published: April 2024

Abstract
Cancer development and progression are characterized by intricate genetic and epigenetic alternations, with DNA methylation playing a crucial role in this transformative process. Recent advancements in DNA methylation mapping technologies have significantly contributed to the identification of novel cancer biomarkers. We provide a short overview of Conformité Européenne-marked in vitro diagnostic tests designed to detect DNA methylation changes in oncology practice.
The Role of Selected Matrix Metalloproteinases in the Pathogenesis of Gliomas
Authors: Katarina Dibdiakova , Jozef Hatok , Renata Pecova , Martin Pec , Andrea Evinova 
Published: April 2024

Abstract
The progression of tumor formation is a multifactorial process that involves changes at different levels. Within this intricate molecular and cellular landscape of tumorigenesis, specific enzymes, namely matrix metalloproteinases (MMPs), emerge as pivotal contributors. They may influence this process at the level of tissue remodelling, angiogenesis, changes in cell signalling, invasion and metastasis, but may also serve as prognostic markers or therapeutic targets. Although the function of MMPs has been known for a long time, their specific role in the pathogenesis of brain tumors has only begun to be investigated in the last three decades. Deregulation of MMPs expression, frequently observed in brain tumor tissue, is associated with malignant phenotype, dependent on the grade of malignancy and associated with a worse prognosis. They participate in tissue remodelling under physiological and pathological conditions, which predetermines their action especially in the process of invasion and migration of tumor cells into the surrounding tissue. Related to their potential in tumor progression, they are also being investigated as possible targets in anticancer therapy. It would be very difficult to characterize in detail the role of all known MMPs in the context of brain tumor pathogenesis, so we have selected those that have an essential effect in this issue.
Characterization of Plasmids Isolated from Bacterial Flora of CRC Patients – Proof of Concept
Authors: Vincent Lucansky , Renata Pecova , Martin Pec , Marek Samec 
Published: April 2024

Abstract

Introduction
Colorectal cancer (CRC) is one of the leading oncogenic disorders, both in terms of incidence and mortality. The etiology of the disease is certainly multifactorial. Various risk factors like alcohol consumption, smoking, CRC family history, inflammatory bowel disease, hormone therapy, aspirin/nonsteroidal anti-inflammatory drugs use, higher body mass index, consumption of red and/or processed meat, insufficient physical activity, and decreased intake of fruit and vegetables have been pointed out; however, there is not enough support evidence for a single particular causative mechanism. Recently, gut bacterial microbiota has been shown to influence significantly the pathogenesis of CRC. However, little attention is paid to the putative impact of plasmids in gut flora.

Material and methods
We have designed and tested the workflow for semi-selective isolation and amplification of random circular sequences. The exploitation of rolling circle amplification (RCA) with a random hexamers protocol is crucial for the outcome.

Results
Our results suggest that it is possible to isolate and amplify plasmid DNA from gut flora and further process, sequence, and identify them.

Discussion
Little is known about the interactions between bacterial plasmids and human cells. The collection of plasmid sequencing data and the comparison of CRC patients and healthy control sequences can be the first step to elucidating this phenomenon.
Metabolic Flexibility: Targeting Mitochondrial Dynamics in Cancer Therapy
Authors: Andrea Evinova , Beata Cizmarova , Michal Pokusa , Renata Pecova , Martin Pec , Katarina Dibdiaková 
Published: April 2024

Abstract
Various types of cancer exhibit distinct sensitivities to anticancer drugs, reflecting their unique developmental pathways. Exploiting mitochondrial dysfunction in cancer cells presents novel therapeutic opportunities. In recent years, advancements have underscored the significance of oxidative phosphorylation, fatty acid oxidation, and glutamine addiction in cancer cells, shifting attention towards mitochondrial metabolism. The foundation of mitochondrial medicine lies in comprehending targeted mitochondrial therapy and the development of specific mitochondrial drugs. Certain alterations within mitochondrial metabolism lead to the accumulation of oncometabolites, serving as epigenetic regulators, or an increase in reactive oxygen species production, both of which contribute to tumorigenesis. Importantly, the substantial involvement of mitochondrial metabolism in tumorigenesis offers potential avenues for exploiting as strategies for cancer therapy. Integrated pharmacological approaches targeting mitochondrial metabolic pathways are imperative for an effective eradication of tumor masses and a concurrent elimination of small subpopulations of mitochondria. The significance of mitochondria in cancer is undeniable, offering numerous appealing targets for both tailored and personalized cancer therapy.
Deep Learning Algorithms for Sports Data Analysis and Prediction in Sports Competitions
Authors: Wei Shuguang 
Published: April 2024

Abstract
Sports data analysis and prediction are essential for gaining a competitive advantage in today’s sports. Artificial Neural Networks (ANNs) have shown promising outcomes in several disciplines, including sports analytics. Sports data is dynamic and complex, making it difficult for standard ANNs to identify minute patterns in it. We introduce a new Puzzle-Optimized Artificial Neural Network (PO-ANN) in this work, which is intended for sports data processing and prediction. The PO-ANN is optimized using a puzzle-inspired method to enhance the network’s ability to identify and comprehend complex patterns in the data. The technique constantly modifies the weights and network topology, enabling the model to better react to the shifting dynamics of sports competitions. The Indian Premier League provided the dataset, which consists of 950 matches and 20 variables (IPL). We implemented our proposed PO-ANN and forecast accuracy in sports data analysis and prediction using Python. We performed a comparison analysis between our suggested PO-ANN approach and other existing methods, using numerous metrics, including MSE, MAE, and MAPE. The suggested POANN technique produced better outcomes than the previous approaches.
The Effect of the Concrete Structure Design Course Based on Formative Assessment
Authors: Zhang Maoyu , Wang Yanru , Li Ruige , Jiang Ruinian 
Published: April 2024

Abstract
The scientificity and rationality of the course assessment method, as a key factor determining whether the course objectives can be achieved, is closely related to the learning effectiveness and competence development of students. In the present study, an assessment method based on formative assessment and supplemented by summative assessment was implemented for the concrete structure design course, and a mechanism for tracking students’ learning status and providing timely feedback was constructed. We compared and analyzed students’ satisfaction, students’ overall assessment results, and students’ learning outcomes before and after the implementation of the proposed course assessment method. The result showed that there was a significant improvement in these three aspects after the implementation of the proposed course assessment method, which promoted student learning.
Music Therapy Music Selection Based on Big Data Analysis
Authors: Wu Chuyi 
Published: April 2024

Abstract
Music, as a carrier of emotional sustenance, can not only achieve spiritual resonance in the process of listening but also reflect the vitality of life in the rhythm of music. As an effective relief method, music intervention has far-reaching significance in the development of psychotherapy. In the practice of music therapy, music selection is particularly important, and the rationality and rigor of music selection directly affect the therapeutic effect. The process of music selection in music therapy is in common with Internet music recommendation, so it has theoretical and practical value to apply personalized music recommendation algorithms to music therapy. In this paper, driven by big data, a music recommendation model based on an improved collaborative filtering (CF) algorithm is proposed, which combines the psychological adjustment of users’ music preferences and different music rhythm features to select music for music therapy and provides theoretical support for music therapy selection. The results show that the construction of music resources for music therapy based on an improved CF algorithm can greatly improve the music selection process of music therapy.
AI-Driven Decision Support System for Green and Sustainable Urban Planning in Smart Cities
Authors: Xu Can 
Published: April 2024

Abstract
This study focuses on innovative practices in sustainable urban planning, demonstrating significant advancements in key areas such as solar panel efficiency, rainwater harvesting capacity, community garden space, and bike-sharing station accessibility through in-depth experimentation and analysis. The research results show that the energy conversion rate of solar panels reached 25%, surpassing the market standard of 24%, which is crucial for enhancing self-sufficiency in energy in urban areas. The rainwater harvesting system performed well, achieving a capacity of 600 liters per square meter, slightly below the market rate of 650 liters, but still demonstrating significant potential in dense urban environments. Additionally, our project provided 3 square meters of community garden space per resident, exceeding the market average, effectively promoting urban greening and improving residents’ quality of life. In terms of transportation, our experimental model featured 1.5 bike-sharing stations per 1000 residents, better than the market data of 1.2 stations, contributing to the development of sustainable urban transportation. These outcomes not only showcase the potential of sustainable urban planning but also provide practical references and guidance for future urban development.
Online Public Opinion Analysis Model Based on Long Short-term Memory Network and Expectation Maximization
Authors: Zhou Chunliang 
Published: April 2024

Abstract
With the rapid dissemination of online public opinion, its emotions are easily transmitted to the general public. It possesses a certain level of social mobilization capacity and can impact the stability of society. To characterize the sentiment trend of social network information and determine its influence, we have proposed a method based on long short-term memory (LSTM) networks and expectation maximization(EM). This model employs a long short-term memory network for data training, obtaining the number of positive public opinions through word-to-word matching. Based on the expectation-maximization method and Jensen’s inequality, the lower bound of the maximum likelihood function is iteratively computed, ultimately achieving convergence of this likelihood function. This convergence value is then used for sentiment analysis. Our study utilizes 10,000 valid pieces of data from the Sina microblog as experimental data. By comparing our model with the K-MEANS model and the EM model, the results indicate significant improvements in the accuracy and convergence of our model. Our research discovers that the influence of public opinion increases as the compensation value for adoption rises, and the probability of public opinion generation gradually increases with the length of user registration years, eventually slowing down.
Research on Civil Aviation Passenger Transportation Accident Symptoms Based on Deep Learning
Authors: Huang Zhousheng , Tang Weizhen , Tian Qiqi 
Published: April 2024

Abstract
In the process of civil aviation safety production, countries and organizations have unified the establishment of a mandatory accident reporting system. In addition, in order to prevent aviation accidents and accident symptoms to the maximum extent, and to obtain as detailed information as possible in the first time when accidents, accident symptoms and other civil aviation unsafe incidents occur, Countries and organizations should also establish a non-punitive voluntary aviation incident reporting system to complement this. NASA in the 1970s began to establish the aviation voluntary reporting system, the system ASRS later became the world’s first implementation of the aviation safety voluntary reporting system. This paper is a comprehensive study on the voluntary reporting system for civil aviation safety. By using data mining technology, the 6W structured expression model of aviation safety accident report is established on the basis of ASRS system for aviation safety accident report, and the information of different dimensions and different data structures is studied completely and systematically. According to the data types in the 6W structured representation model, two data mining methods of multi-dimension analysis and text classification are selected.
Research on Deep Learning-based English Vocabulary Teaching Aid System in Colleges and Universities
Authors: Chen Leilei , Yu Fangfang , Wu Jiqin 
Published: April 2024

Abstract
Currently, English teaching in colleges and universities faces the challenge of low efficiency in vocabulary teaching. Deep learning technology, an advanced artificial intelligence technology, provides new ideas for solving this problem. Constructing an efficient teaching aid system can achieve personalized vocabulary learning and significantly improve learning efficiency and effectiveness. Aiming to address the efficiency and effectiveness problems in English vocabulary teaching in colleges and universities, this study develops an English vocabulary teaching assistance system based on deep learning. Deep learning algorithms allow the system to analyze students’ learning habits and abilities and provide personalized vocabulary learning suggestions. The research constructs neural network models, uses big data to explore students’ learning behaviors, and develops an intelligent pushing mechanism. The system in this paper achieved face recognition accuracy of 81.08% and 98.84% on the FER and C.K. datasets, which verify the model’s effectiveness. Through the teaching experiments conducted on two classes in Y school in W city, the average score of the English vocabulary test of the class taught with this system increased from 75 to 93, significantly higher than that of the traditional teaching methods. This auxiliary system can improve the effectiveness and efficiency of English vocabulary learning, which also offers new technical support for English teaching in colleges and universities.
Analysis of Chinese Higher Calligraphy Education in the Context of Big Data Era
Authors: Fu Weigang 
Published: April 2024

Abstract
The development of modern digital technology provides a new way for the inheritance and innovation of the art of calligraphy. Digitization has dramatically enriched the dissemination carrier of calligraphy, enabling calligraphy education resources to be widely disseminated across physical boundaries, and realizing the personalization and intelligence of calligraphy education through the intelligent calligraphy cultural resources service model. The application of Digitization in higher calligraphy education analyzes the effective use of educational resources and promotes innovation in calligraphy education models through methods such as multi-layer linear regression. This study focuses on the digital dissemination of calligraphic educational resources, the intelligent calligraphic cultural resource service model, and the use of digital resources in higher calligraphic education. It has been found that teachers’ ability to apply digital resources is positively correlated with their academic qualifications. The educational background improves their application ability by 0.5 times. The study found a significant correlation between learning interaction activities and learning effectiveness by enhancing the random forest algorithm and analyzing students’ behavioral data. Higher calligraphy education should fully utilize big data technology to promote the digital transformation of educational resources and meet the needs of calligraphy education in the new era.
Analysis of the enhancement of computer simulation on physical education teaching in colleges and universities
Authors: Hou Yuanyuan , Ouyang Bo 
Published: April 2024

Abstract
The integration of computer simulation and modeling into physical education represents a significant shift in teaching methodologies within higher education, prompted by the advancements of the digital era. This study scrutinizes the effectiveness of such technologies in augmenting student learning outcomes and pedagogical efficiency in PE courses. Through the application of virtual reality and precise movement modeling, the research contrasts the impacts of traditional teaching with those of computer-assisted simulations. Findings demonstrate that computer simulation significantly improves students’ physical fitness (by 15%) and skill proficiency (by 20%) and markedly increases their engagement with the subject matter. Additionally, the ability to practice in a simulated, hazard-free environment reduces the time required to master skills. These results support the broad implementation of simulation technologies in physical education, promising enhanced student motivation, skill development, and academic interest.
Exploring the Thinking Transformation and Development of Graphic Design in Mobile Digital Media
Authors: Huang Zheng 
Published: April 2024

Abstract
The transition of graphic design from traditional print to mobile digital media has revolutionized the technology and the creative process. This evolution demands designers adapt their strategies to meet the challenges of smaller screens and varied user behaviors. This study explores how graphic design has transformed the mobile digital era, emphasizing the shift towards user interaction and visual communication efficiency. An empirical analysis of hundreds of designs reveals that innovative design methods significantly outperform traditional ones, increasing user engagement and communication effectiveness by 30% and 50%, respectively. The findings underscore graphic designers’ need to evolve their thinking and methodologies, enhancing interactivity and visual appeal to thrive in the mobile digital landscape.
Combination of Efficient Informationized Music Teaching and Traditional Music Culture in the Internet Era
Authors: Li Yanhui 
Published: April 2024

Abstract
Music teaching in education is undergoing a profound change under the development of information technology. Integrating information technology not only enhances teaching efficiency, but also expands the reach of teaching resources. The challenge for educators is to combine the use of modern information technology with the preservation and promotion of traditional music culture. This study discusses how to effectively combine efficient informatization teaching methods with China’s rich traditional music culture in the Internet era, intending to provide new perspectives and strategies for the innovative development of music education. By comparing and analyzing the learning effectiveness of the experimental group (using informatized teaching methods) and the control group (using traditional teaching methods), the study shows that the experimental group’s average improvement rates in music comprehension, skill mastery, and creativity reached 20%, 30%, and 40%, respectively, which were higher than those of the control group. The combination of informatization teaching tools and strategies in this study can significantly improve students’ interest and effectiveness in learning traditional music culture, injecting new vitality into conventional music education.
Digital Twin Based Resource Optimization and Dynamic Decision Making in Air Terminal Yards
Authors: Qiu Yicheng 
Published: April 2024

Abstract
With the growth of globalized trade, the efficient operation of airports is crucial. In this paper, we study the use of digital twin technology to optimize the yard resources of airports, which can improve their operational efficiency and decision-making quality. The study first constructs a digital twin framework for intelligent yard scheduling, which contains physical and virtual space and the connection between them. Data is collected through the physical perception module, while the virtual space is used for real-time simulation and optimized decision-making. The research methodology includes mixed integer programming and adaptive immune cloning algorithms for optimizing yard resource allocation and box space allocation. The experimental results show that the method can effectively reduce the total transportation distance in the air terminal yard operation and improve operation efficiency. The experiments demonstrate an average improvement in operational efficiency of around 20% and a cost reduction of around 15%. It is concluded that digital twin technology has apparent advantages in allocating resources in air terminal yards, which can achieve efficient utilization of resources and dynamic optimization of decision-making.
Research on the synergistic evolution of tourism economy and urban ecological resilience based on coupling model in the context of ethnic arts
Authors: Wang Jiaxiang 
Published: April 2024

Abstract
This study delves into the crucial relationship between the tourism industry and urban ecological resilience in areas renowned for ethnic arts, highlighting the dual objectives of cultural promotion and environmental sustainability. We employ a coupled model that integrates multi-indicator group evaluation and entropy weight method to assess the synergy between tourism economic growth and urban ecological health in the M city cluster from 2009 to 2022. Findings reveal a notable improvement in their coupling coordination, with an average degree of 0.65, suggesting a mutually beneficial interaction. Analysis underlines urban ecological resilience—especially its resistance component—as the most robust, achieving a score of 0.54. The research identifies vital misalignments hindering their synergistic development and offers strategic recommendations for harmonization. This study enhances our understanding of balancing tourism development with ecological conservation, providing valuable insights for policymakers.