bluuebunny/crossref_metadata_2025 huggingface dataset:
Mathematical Insights and Applications of K-Means Clustering in Diabetes Data Analysis
Authors: Sofi. A. Francis
Published: January 2025

K-Means clustering is a powerful and adaptable technology that has transformed industries like operations research and healthcare due to the quick development of machine learning and data analytics. Using K-Means, this study examines a real- world dataset of 1050 diabetic patients that has been divided into groups based on important clinical criteria such as HbA1c levels, random blood sugar (RBS), post- prandial blood sugar (PPBS), and fasting blood sugar (FBS). Utilizing operations research methods, our goals are to pinpoint unique patient groups, improve individualized care plans, and maximize the use of available resources. The effectiveness of K-Means clustering in diabetes management is demonstrated in this study, which also provides important insights for individualized healthcare and shows the algorithm’s superiority over other machine learning techniques.
Capability of Reactive Power Compensation of Vehicle-to-Grid (V2G) Technology using off-board EV Battery Charger
Authors: Chirag Arora
Published: January 2025

This study investigates the potential for V2G in reactive power compensation, one of its lesser-known capabilities. With a focus on V2G-enabled electric vehicles, the research explores using an off-board EV battery charger, connected to the grid, to supplement or replace power transmission for RPC. It presents a control strategy for EV chargers, based on the analysis of nonlinear residential load currents and PCC voltages, to align voltage patterns and predict active load current components. At the system's core is a three-phase bidirectional inverter and a DC/DC bidirectional converter, interconnected through a DC link. Simulation results confirm the method's effectiveness, which involves modifying the control algorithm and power stage for efficient RPC. Additionally, a 20 kVA off-board charger  simulation model was developed in MATLAB/Simulink to validate the proposed control strategy for Grid-to-Vehicle (G2V), V2G, and RPC operations. The proposed framework optimizes power flow and improves grid stability, power quality, and efficiency through V2G technology. Advanced control techniques, such as dynamic charging profiles during charging, minimize reactive power losses and extend battery life, making the grid more reliable and efficient. The charger control methodology adheres to the latest IEEE Standard 1459-2010.
Fault-Tolerant Strong Metric Dimension of Rooted Product Graphs
Authors: H. Prathab
Published: January 2025

The concept of fault tolerance in graph theory is critical in designing robust networks, ensuring that essential graph properties are preserved despite failures of vertices or edges. In this paper, we investigate the fault-tolerant strong metric dimension of rooted product graphs, a class of graphs derived by attaching multiple copies of a rooted graph to each vertex of a base graph. We extend the notion of a strong metric dimension by considering scenarios where the strong resolving set remains effective even after a specific number of vertices have failed. We provide exact values for specific families of rooted product graphs and demonstrate how the fault-tolerant property varies with the structure of the root and base graphs.
New General Complex Integral Transform on Time Scales
Authors: Dipali Kaklij, Dinkar Patil
Published: January 2025

Introduction: We begin by defining the New General Complex Integral Transform [13], and then we present a New General Complex Integral Transform on Time Scales . To solve a variety of dynamic equations with beginning values or boundary conditions that are represented by integral equations, integral transform methods are frequently employed. In order to solve dynamic equations, the New General Complex Integral transform on Time Scale is presented in this article.
Objectives: Within the Laplace Transform class, we provide the New General Complex Integral transform on Time Scales in this study. We examine this transform's characteristics. An initial value problem with a dynamic form of the equation is the primary focus of this research.
Methods: Differential equations of any order and the integral of a function can both be solved using the New General Complex Integral Transform on Time Scales. By establishing the convolution theorem, the idea of convolution is examined in further detail.  
Results: This integral transform is used for solving higher order initial value problems and integral equations.
Analysis of Students’ Performance by Fuzzy Inference Technique
Authors: Bikram Baro
Published: January 2025

The performance of a student in a subject includes two main factors: one is performance in a single chapter and other is performance in the remaining chapters. In this article, performance of the class x standard students in mathematics subject in special reference to the chapters of the subject is discussed. As per as the syllabus of NCERT class x mathematics subject, seven branches namely algebra, geometry, trigonometry, mensuration, co-ordinate geometry, probability and statistics are considered in most of the institutions. The potentiality to monitor to develop and progress of students’ performance in a particular subject is a critical issue to the overall academic performance of a student. This study tries to approach this issue by considering the advantage of fuzzy inference technique in order to classify the data obtained from students’ scores. It is very necessary to analyse the above-mentioned two factors for the good performance of each subject and to get good result of the student in any institute. To analyse the performance of individual student, in this paper a methodology to improve the above-mentioned two factors is adopted. The whole data obtained from student’s response test is divided into various ranges through fuzzy logic system. In this papers, fuzzy logic system, rules and membership functions, linguistic variables and the application of performance analysis methodology with the help of fuzzy logic system are included. Two membership functions namely, trapezoidal and triangular membership functions are considered. Fuzzification of input data (students’ scores) by creating a fuzzy inference system (FIS) chapter wise, next each FIS output is passed to another FIS with one output of the final FIS. The performance value is calculated based on the scores of the ‘remaining chapters’ with or without the scores of ‘one chapter’ of the same subject.
Elliptic Curves over p-adic field Q_p and its p-adic point addition
Authors: P. Anuradha Kameswari
Published: January 2025

Public-key cryptosystems with the Elliptic curve  over finite field  are an alternative to RSA with finite fields. In the context of improving the efficiency of cryptosystems with elliptic curves, the study of elliptic curves  over p-adic number field  was consequential. In this paper we first obtain all the points in the elliptic curve  over p-adic number field  as lifts of the points in  to  and then  to  and so on and then to evaluate the arithmetic on , we first proceed to describe the implementation of the arithmetic of points on  to points on  and then give the algorithms for the computations.
Effect of Aluminium and Steel Fibers on Flexural Performance of Reinforced Concrete Beam
Authors: Giridhar N.Narule
Published: January 2025

Introduction: Fibers are widely used in concrete to reduce water absorption, surface permeability, curling, shrinkage cracks, and improve durability and strength. Steel and aluminium fibers can be easily extracted from scraped materials and affordable compare to other fibers. In this study, the combined steel and aluminium fibers are mixed in concrete with percentage varies from 0.5 to 2.5.  The fifteen fibrous reinforced concrete beam were prepared to analyses the impact of steel and aluminium fibers on the flexural behavior of the reinforced concrete beam. The test results of steel and aluminium fiber RC beam specimens are compared to concrete beam samples. In contrast to typical concrete beam specimens, the failure patterns seen in steel and aluminium fiber reinforced concrete specimens are different. When fibers are used, RC beams can support higher loads than regular reinforced concrete beams. The flexural strength of the beam specimens was improved by addition of the fibrous material in reinforced concrete beam. The study finds that adding steel and aluminium fibers to reinforced concrete improves its flexural strength as well ductility. It is also found that use of steel and aluminium fiber in concrete a good substitute to improve performance of flexural member.
Enhanced Customer Emotions Classifications in E-Commerce on Sentiment Analysis with Convolutional Neural Network
Authors: Kamalakannan. R
Published: January 2025

In the rapidly evolving e-commerce landscape, accurately interpreting customer emotions from textual reviews is crucial for enhancing user experience and informing business strategies. Traditional sentiment analysis methods often struggle with the complexity and nuance of human emotions expressed in text. This research introduces an advanced approach employing Convolutional Neural Networks (CNNs) to improve the classification of customer emotions in e-commerce platforms. By leveraging CNNs' ability to capture local features and patterns within textual data, our model effectively distinguishes between subtle emotional cues, leading to more precise sentiment categorization. Experimental results demonstrate a significant improvement in classification accuracy over conventional techniques, underscoring the potential of deep learning models in sentiment analysis applications within the e-commerce sector.
Comprehensive Review on Stress Detection using EEG Signals and Machine Learning Techniques.
Authors: Amol S. Chaudhari
Published: January 2025

Stress significantly impacts mental and physical health, making accurate detection essential for enhancing productivity and well-being. Electroencephalography (EEG) has emerged as a pivotal tool for stress analysis, offering a non-invasive, cost-effective, and temporally precise method to evaluate brain activity. This paper reviews advancements in stress detection using machine learning (ML) and deep learning (DL) techniques applied to EEG data. Traditional stress detection methods, such as self-reports and subjective surveys, face limitations in reliability and scalability. In contrast, ML and DL models, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and hybrid architectures, demonstrate superior feature extraction and classification accuracy. Studies have reported classification accuracies exceeding 95% using advanced signal processing and hybrid frameworks. However, challenges remain in optimizing computational efficiency, reducing algorithmic complexity, and validating models on diverse, real-time datasets. This review highlights the potential of innovative feature extraction techniques and hybrid ML-DL models to address these gaps, paving the way for robust, scalable, and real-time stress detection systems. By overcoming current limitations, future research can significantly contribute to mental health management and preventive care strategies. This paper provides a comprehensive overview of recent advancements and outlines future directions in EEG-based stress detection research.
Comparative Analysis of Optimal Allocation of PV Units Considering Plug-In Hybrid Electric Vehicle Charging Demand in Radial Distribution System
Authors: A. Manjula
Published: January 2025

The growing adoption of distributed generators (DGs) and plug-in hybrid electric vehicles (PHEVs) is driving significant changes in the operation of distribution systems. This study aims to optimize the integration of Photovoltaic (PV) distributed generators deployed in radial distribution networks to support load requirements by PHEVs. To achieve this, a optimization approach is applied, focusing on minimizing voltage variations, and reducing energy losses. The impact of PHEV charging demand on distribution system performance is analyzed under peak, off-peak, and stochastic charging conditions. PV unit placement is then optimized using the advanced Electric Eel Foraging Optimization (EEFO) algorithm, which accounts for PHEV demand variability within the distribution network. Standard IEEE 33-bus and IEEE 69-bus test systems are employed to conduct comprehensive case studies, examining the effect of varying numbers of PV units on the system’s performance. Comparative evaluations highlight the EEFO algorithm’s effectiveness in solving the PV allocation problem, outperforming DE, GWO optimization methods.
Deep Learning Assisted Intelligent Fall Detection Mechanism: An IoT and Healthcare Integration of Elderly People
Authors: A.Anusha Priya
Published: January 2025

In the context of elderly living, occurrence of fall can create critical health deterioration and it is a significant health concerns during their stay in home. As a result, having reliable and timely prediction that pays high attention can help them in their daily life. In this research, the information obtained from a home environment with the assistance of Internet of Technology (IoT) is classified by utilising a feed forward neural network (FFNN).   Information enters at the input unit will flows via layer across the network until it reaches the output unit. There is no feedback amongst layers during normal operating conditions, which is when it functions as a classifier. This is why they're referred to as FFNN. A significant element in fall detection is detecting human posture, because fall events are similar to falling posture. A fall can be effectively identified by a distinguishing feature. The proposed system can clearly detect the normal lying posture and lying after falling and it can efficiently detect action motion as well as recognize the action posture. The performance of the proposed and exiting is compared where FFNN achieves 98% detection accuracy, which utilises standard benchmark data sets.
Heart Disease Prediction using Machine Learning and Deep Learning Approaches: A Systematic Survey
Authors: Kayam Saikumar
Published: January 2025

Cardiovascular disease is still a major killer around the world and is caused by inappropriate life styles and nutritional processes. Early diagnosis and subsequent prevention of diseases have ensured that healthcare is a significant research area, more so where heart diseases are concerned. Due to the expansion of electronic data in today’s world, healthcare departments are overburdened with extensive varieties of medical data, which requires proper modes of managing and extracting valuable information. This article performs a systematic literature review of the data mining, machine learning and deep learning approaches used for heart disease prediction, especially in proving the efficiency of the techniques in identifying the hidden patterns in the large health datasets for early diagnosis. It shows how these computational methods help to find patterns related to risks and prevent them with the needed interventions. Furthermore, common issues with the existing prediction models are discussed, including the lack of focus on interpretability, generalizabity, and scalability of the models. This survey concludes by presenting directions for future research to reduce the error rate, real-time analysis in a clinical environment, and personalized analysis. This guidance will be useful to plan better and improve these directions to allow the reduction of deaths due to this pathology.
Optimizing Medical Image Classification Using Diverse Feature Extraction Methods for Brain Tumor
Authors: Aarthi D
Published: January 2025

Detecting brain tumor early and accurately is crucial for effective treatment and better patient outcomes. Three feature extraction methods are used in this study: Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), and Gray Level Histogram Features (GLHF)—to classify MRI images of the brain. GLCM measures the relationship between pixel intensities to capture texture information, while GLRLM finds patterns of pixels with similar gray levels, showing areas of texture. The histogram method summarizes the overall intensity in the image, highlighting differences between normal and abnormal areas. A Support Vector Machine (SVM), a classifier designed to differentiate between brain tissue affected by tumors and normal tissue, processes these retrieved features. Using a typical MRI dataset, the research assesses how well each feature extraction method supports accurate classification by the SVM. By comparing their performance, the study identifies which technique is best for distinguishing between healthy and tumor regions in the brain. This analysis offers valuable insights into improving brain tumor detection, potentially benefiting clinical diagnosis and treatment planning.
A Novel Approach Using Artificial Intelligence for Early Prognosis of Chronic Kidney Disease in Asian Population
Authors: Anindita Khade
Published: January 2025

Purpose: The goal of this research project is to address the increasing prevalence of chronic kidney disease (CKD) and the challenges associated with its early detection. The study proposes a new approach that combines Linear Discriminant Analysis (LDA) to reduce the number of features and Artificial Neural Networks (ANN) for accurate and early identification of CKD. The primary objective is to create a smart decision-making system that can help nephrologists in India diagnose CKD in the early stages.
Method: The study is based on a dataset collected from DY Patil Hospital in Navi Mumbai. It consists of around 500 records with 21 attributes. To improve accuracy and reduce prediction time, the proposed model combines LDA and ANN. Feature selection is carried out using Recursive Feature Elimination (RFE), which identifies Creatinine, BUN, and Urea as crucial factors. The methodology includes preprocessing, hyperparameter tuning, and classification. Statistical analyses, such as hyperparameter values, Friedman's Test, and parallel computing evaluation, are used in the comprehensive methodology.
Results: The proposed hybrid model, referred to as a Hybridized LDA with ANN (HLDANN), outperforms traditional classifiers like SVM, LR, RF, DT, KNN, and even a standalone ANN. The model achieves an accuracy of 93.22% on a real-time dataset, surpassing other algorithms. Precision, recall, and F1 score metrics further validate the effectiveness of HLDANN. Parallel computing analysis demonstrates reduced prediction time with an increase in worker nodes.
Conclusion: Based on the study, the hybrid model is an effective method for detecting CKD at an early stage. It provides significant accuracy improvements compared to existing methodologies. The key features identified in the study align with known biomarkers, confirming the model's reliability. To further enhance the model's robustness, future research could explore additional biomarkers, diverse data sets, and non-invasive techniques. The proposed algorithm shows promise as a valuable tool for the medical community that could contribute to early CKD diagnosis and improved patient outcomes.
Binomial Labeling and Catalan Labeling of Four Intersection Mirror Lagoon Step Graph and Four Intersection Opposite Lagoon Step Graph
Authors: J.Vasanthi
Published: January 2025

Let G={V(G), E(G)} be the graph with ‘n’ vertices and ‘e’ edges. In this paper we introduce two new graphs namely Four Intersection Mirror Lagoon Step graph and Four Intersection Opposite Lagoon Step graph. We investigate the Binomial Labeling and Catalan Labeling of Four Intersection Mirror Lagoon Step graph and Four Intersection Opposite Lagoon Step graph.
A Smart Waste Management: an Adaptive Algorithm for Classification of Degradable and Non-Degradable Waste using Machine Learning Model
Authors: Manohar K. Kodmelwar
Published: January 2025

Urban waste management poses significant challenges due to the growing volume and complexity of waste generated in cities. Effective classification of waste into degradable and non-degradable categories is critical for optimizing waste disposal, promoting recycling, and mitigating environmental impacts. This report presents a comprehensive exploration of an adaptive machine learning algorithm for image-based waste classification, designed to enhance municipal waste management systems. The proposed approach integrates advanced image processing techniques with machine learning models, such as Convolutional Neural Networks (CNNs) and their efficient variants, to classify waste with high accuracy. A citizen reporting interface allows users to capture and upload waste images, which are processed in real-time by a centralized system. The adaptive algorithm incorporates preprocessing steps, feature extraction, and model inference to deliver actionable insights, enabling municipal authorities to deploy appropriate waste collection mechanisms promptly. Through a detailed literature review, the report examines existing methodologies, benchmarks their performance, and identifies key challenges, including data quality, scalability, and integration with urban infrastructure. Case studies from global implementations illustrate the transformative potential of AI-driven waste management systems. Furthermore, the report outlines future directions, such as leveraging advanced deep learning architectures, real-time deployment using edge computing, and expanding classification to include hazardous and recyclable waste categories. This work contributes to the growing field of smart waste management by demonstrating how machine learning can address critical urban challenges. By fostering collaboration between technology and sustainability, the proposed framework lays the foundation for cleaner and more sustainable cities.
Algebraic Topology in Modern Cryptography: A Cross-Disciplinary Perspective
Authors: R.Venkata Aravinda Raju
Published: January 2025

In order to clarify how topological ideas might improve cryptographic techniques, this study explores the relationship between algebraic topology and contemporary cryptography. The work provides new insight into cryptographic diversity by examining algebraic structures and their uses. It suggests that rearranging cryptographic pieces using algebraic binary relations can result in systems that are safer and more efficient. The approach demonstrates the ramifications of using topological concepts to address current cryptographic problems by combining theoretical studies with real-world applications. The study also emphasises the value of interdisciplinary approaches by exposing possible developments in data integrity and secure communications. The results highlight how crucial it is to incorporate mathematical frameworks into cryptography, which could lead to the development of innovative cryptographic solutions in a world that is becoming more digital. This approach promotes more multidisciplinary research by establishing algebraic topology as an essential tool for improving the resilience and versatility of cryptographic systems.
Rethinking Blockchain for IoT
Authors: Ganesh Gandal
Published: January 2025

This paper explores the rethinking of blockchain architecture for the Internet of Things (IoT) to improve data security, integrity, and processing efficiency. An API gateway collects, prepares, and sends data to blockchain nodes for verification when integrating IoT devices with a blockchain network. Accuracy is ensured by updating the distributed ledger with verified data. Within the blockchain, smart contracts automate actions in response to IoT data situations, carrying out tasks automatically and recording results. Large or supplemental data is stored off-chain to preserve efficiency and scalability, while basic transactional data is kept in the distributed ledger. Direct connection between blockchain nodes via a peer-to-peer (P2P) network promotes fault tolerance, consensus, and data consistency. This architecture utilizes the secure and immutable properties of blockchain technology to address common IoT challenges, offering a robust solution for contemporary IoT applications.
Meningkatkan Filantropi Melalui Crowdfunding: Usulan Fitur Donasi Otomatis di Dompet Dhuafa
Authors: Annida Rizki Luthfi Astuti  , Rahmatun Nazila  , Siti Muslichah  , Ala Asmaul Husna  , Sumarsono
Published: January 2025

Perkembangan teknologi tidak terlepas dari modernisasi. Modernisasi memposisikan diri sebagai bentuk globalisasi yang berkelanjutan. Indonesia sangat heterogen, dan seluruh aspek masyarakat, budaya, dan kehidupan menjadi semakin beragam seiring dengan globalisasi. Perkembangan teknologi menjadi bahan modernisasi keberagaman, guna menyesuaikannya dalam konteks ketergantungan terhadap teknologi baru salah satunya komputasi sosial. Crowdsourcing merupakan bagian dari komputasi sosial dan menjadi metode yang semakin populer di kalangan institusi dan bisnis di semua sektor dengan memanfaatkan dukungan komunitas. Penelitian ini menggunakan salah satu jenis crowdsourcing yaitu crowdfunding yang diterapkan dalam apikasi dompet dhuafa. Tujuan dari penelitian ini adalah meningkatkan keberlanjutan organisasi pelayanan sosial melalui dukungan pemanfaatan platform crowdfunding dompet dhuafa. Metode dalam penelitian ini dengan mnggunakan UML dan Use case diagram untuk merancang sistem yang akan menampilkan menu donasi otomatis dan memungkinkan pengguna untuk memilih kategori donasi otomatis, mengatur waktu donasi, dan melihat saldo yang tersedia. Hasil penelitian berupa rancangan pengintegrasian fungsi donasi otomatis ke dalam aplikasi Dompet Dhuafa telah sesuai dengan kebutuhan fungsional sistem.
Analisis Resepsi Remaja Perempuan Mengenai Peran Ayah dalam Video Klip “Saat Kau Telah Mengerti”
Authors: Wibowo Fauzan Afif , Dyah Kusumawati
Published: January 2025

Fathers have a role as head of the household and are involved un childcare. The presence of fathers for their children has closeness that is not only physical but emotional. Especially for girls. This study aims to determine the reception of adolsecent girls in Jakarta regarding the role of fathers in the video clip entitled “Saat Kau Telah Mengerti”. This research uses the contrutivism paradigm with qualitative approach. The research method used reception analysis, and Stuart Hall’s encoding decoding theory to see three audience positions of dominat hegemony, negotiation and oppotition. The result showed that three informants were in the dominant hegemony position regarding the role of the father because they had an attachment to the informant’s experience, while two informants were in the negotiation position because of the informant's experience of being abandoned by his father.
Pengembangan Fitur Analisis Data dan Visualisasi Informasi pada E-Learning Moodle
Authors: Felix Aristo  , Samosir Ridha Sefina
Published: January 2025

Moodle merupakan media learning management system yang bersifat open source atau bisa dikembangkan lagi sesuai kebutuhannya. PT GML Performance Consulting menggunakan Moodle untuk training atau workshop bagi perusahaan partnernya. Berdasarkan penggunaan Moodle saat ini terdapat fitur yang perlu ditambahkan untuk mendukung kegiatan training atau workshop tersebut. Penelitian ini bertujuan untuk mengembangkan fitur analisis data dan visualisasi informasi pada Moodle sebagai fitur tambahan. Penambahan fitur pada Moodle ini akan berbentuk plug-in dengan menggunakan metode prototyping. Adapun tehnik analisis data yang digunakan untuk penambahan fitur adalah tehnik clustering menggunakan algoritma K-Means. Pengujian terhadap pengembangan sistem E-Learning Moodle menggunakan metodologi black box testing.
Rezension von: Seng, Eva-Maria u.a. (Hrsg.), Prähistorische Pfahlbauten im Alpenraum
Authors: Brunecker Frank , Seng Eva-Maria
Published: January 2025

Eva-Maria Seng/Helmut Schlichtherle/Claus Wolf (Hg.): Prähistorische Pfahlbauten im Alpenraum. Erschließung und Vermittlung eines Welterbes. Berlin: De Gruyter 2019; 225 S., 70 Abb, 69,95 EUR
Rezension von: Hamm, Berndt, Spielräume eines Pfarrers vor der Reformation
Authors: Schenk Susanne , Hamm Berndt
Published: January 2025

Berndt Hamm: Spielräume eines Pfarrers vor der Reformation. Ulrich Krafft in Ulm (Veröffentlichungen der Stadtbibliothek Ulm 27). Ulm 2020; XX + 451 S., 36 farbige Abb., geb., 39,80 EUR
Improving International Trade Practices Through Digital Management
Authors: Liu Hongfei
Published: January 2025

With the rapid development of information technology, digital management has become an important means to improve enterprise operational efficiency and optimize resource allocation. In the field of international trade, traditional practical operating methods have problems such as cumbersome procedures, lagging information, and high costs. Current international trade practices also face information asymmetry, complex processes, and low efficiency, which seriously restrict the international competitiveness of enterprises and urgently need to be innovated through digital management. This paper aims to explore how to improve the level of international trade practices through digital management methods, in order to provide strong support for the internationalization strategy of enterprises. First, this paper analyzes the application background and current status of digital management in international trade, and then summarizes the achievements and shortcomings of existing research. Next, this paper designs an international trade practice model and builds an information platform. Then, through experiments, this paper analyzes the effect of digital management on the improvement of the level of international trade practice. Finally, the results show that after the optimization of digital management, its operational efficiency is improved and the practical level is improved.
Big Data-Driven Smart Tourism Service Quality Monitoring and Enhancement Strategies
Authors: Li Congcong , Wen Xin
Published: January 2025

The rapid evolution of the tourism industry globally has led to an increasing emphasis on service quality and tourist experience. To meet these demands, big data technology has been extensively adopted in the realm of smart tourism. This paper delves into the application of big data in smart tourism, focusing on its role in service quality monitoring and enhancement strategies. This paper delves into the crucial topic of enhancing service quality in smart tourism through big data. The research aims to comprehensively understand the role of big data in smart tourism, establish effective monitoring mechanisms, and formulate practical improvement strategies. By integrating literature research, case studies, and empirical analysis, it is found that big data plays an indispensable role in uncovering tourist behavior patterns, optimizing tourism enterprise operations, and enabling seamless travel experiences. The real - time monitoring system based on big data can accurately evaluate service quality, predict potential failures, and drive continuous improvement through feedback loops. Moreover, strategies such as large - scale customization, collaborative innovation, and talent development can significantly elevate the overall level of smart tourism.
Research Progress in the Treatment of Myopia by Traditional Chinese Medicine
Authors: Lin Chaoyi , Cao Ping
Published: January 2025

With the development of the economy, the widespread use of electronic products, the increase of eye time, the incidence of myopia continues to rise, showing the characteristics of young age, rapid progress and high degree, the incidence of myopia in China ranks first in the world, the form is severe, and the prevention and control of myopia is imminent. The medicine of the motherland has a long history and many methods in the treatment of myopia, which has obvious advantages. This article reviews the specific contents and clinical effects of Chinese medicine in recent years, such as internal medicine, acupuncture, ear point pressure, acupressure, moxibustion, etc., hoping to contribute to the future Chinese medicine treatment of myopia.
The Influence of Ventricular Premature Contraction on Cardiac Structure and Function
Authors: Wang Hao , Li Lihua
Published: January 2025

Ventricular premature contraction (VPC) constitutes a frequently encountered cardiac arrhythmia in clinical settings. This review aims to summarize the effects of VPC burden, origin site, morphology, QRS duration, and coupling interval on cardiac structure and function.
Clinical Efficacy of Ten-flavored Rhubarb Powder on Postoperative Perianal Abscess Observation
Authors: Ma Yuanyuan , Wan Shui
Published: January 2025

Objective: To investigate the postoperative treatment of perianal abscess with decaphyllum dahuang san and observe its clinical efficacy. Methods: 62 patients with perianal abscess requiring surgical treatment admitted to the Department of Anus and Intestines of Wuhu Hospital of Traditional Chinese Medicine from June 2023 to June 2024 were included, and a simple random allocation method was applied to randomly divide the subjects involved in the study into a study group and a control group, with 31 cases in each group. The patients in the study group were treated with postoperative sitz baths with ten-flavored rhubarb powder, while the patients in the control group were treated with sitz baths with potassium permanganate solution, and the healing time of the trauma, postoperative pain, and edema of the patients in the two groups were compared. Results: The patients in the study group and the control group were effectively cured, and the difference in wound healing time between the two groups was statistically significant (P<0.01), with the healing time of the control group being slower than that of the study group; the difference in postoperative pain and edema between the two groups was also statistically significant (P<0.05), with the clinical efficacy of the control group being lower than that of the study group. Conclusion: The clinical efficacy of ten-flavored rhubarb powder after perianal abscess surgery is good, which effectively reduces patients' postoperative pain and edema, shortens wound healing time and saves medical resources, and is worth promoting its use.
Visual Analysis of Literature Related to TCM Treatment of Low Back Pain based on Citespace
Authors: Li Fen'e , Fan Ping
Published: January 2025

Objective: To understand the research content and progress of low back pain and explore the development trend and hot spots in this field by combing and visualization analysis of the literature on the treatment of low back pain with traditional Chinese medicine. Methods: CNKI was used to search the literature, and CiteSpace software was used to visualize the authors, research institutions and keywords in the field. Results: A total of 748 literatures were included, and the number of published papers increased year by year, began to decline in 2018, and began to rise in 2022. Institutional cooperation took Beijing University of Chinese Medicine as the hub of institutional cooperation. The author cooperation team is mainly composed of Ni Lijun, Zhang Liguo and others. The high-frequency keywords include lumbar disc herniation, traditional Chinese medicine, low back pain, etc. There are 10 keyword clusters, including "pain" and so on. The words with high intermediate centrality were "lumbar disc herniation" and so on. There are 25 prominent words in the emergent analysis, and the emerging hot spots are "lumbar function", "Chinese medicine heat election Bao" and so on. Conclusion: The scope of institutional cooperation and authors' cooperation in this research field is limited. "Famous doctor's experience" and "brain edema" have become research hot spots since 2005 and 2007 respectively, while "hydropneumatosis", "Zhang Zhongjing" and "macular edema" will become new research hot spots.
Progress of Research on Chinese Medicine Intervention in Ferroptosis for Osteoarthritis
Authors: Li Mengyuan , Wang Li
Published: January 2025

Osteoarthritis is a common chronic joint disease that brings great pain and life burden to patients. In recent years, ferroptosis, a novel mode of programmed cell death, has gradually gained attention in the pathogenesis of osteoarthritis. As a traditional treatment method, Chinese medicine has the advantages of multi-targeting and overall regulation in regulating ferroptosis in osteoarthritis. This review discusses the mechanisms of TCM in regulating ferroptosis-related signaling pathways, anti-oxidative stress, inhibition of inflammatory responses, and the key regulators and regulatory pathways of ferroptosis in osteoarthritis, and comprehends the current status of the research on the targeting of active ingredients of TCM to inhibit ferroptosis, so as to provide reference for the research on the mechanism of TCM in treating osteoarthritis and for the research and development of drugs.
Exploring the Pathophysiological Mechanisms of Acute Gastrointestinal Injury in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: An Integrative Review
Authors: Luo Mingsheng , Wan Dong
Published: January 2025

Acute gastrointestinal injury (AGI) complicating acute exacerbations of chronic obstructive pulmonary disease (AECOPD) represents a complex multisystem pathophysiological phenomenon involving systemic inflammation, hypoxemia, microcirculatory dysfunction, and gut microbiota dysbiosis. Inflammatory mediators, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL- 6), along with hypoxia-inducible factor-1 alpha (HIF-1α), serve as key drivers of gastrointestinal dysfunction. Furthermore, microcirculatory disturbances and dysregulated gut-lung axis interactions exacerbate gastrointestinal barrier impairment. This review systematically analyzes the pathophysiological mechanisms of AECOPD complicated by AGI, focusing on inflammation, hypoxia, microcirculatory dysfunction, and gut microbiota imbalance, aiming to provide a theoretical foundation and reference for clinical management.
Clinical Analysis of Two Cases of Myotonic Dystrophy Patients Treated with Chinese Medicine
Authors: Zhang Yan , Yang XiongJie
Published: January 2025

Myotonic dystrophy (DM) is a group of multi-systemic autosomal dominant genetic disorders characterized by muscle weakness, myotonia, and muscle atrophy. This study clinically analyzed two patients with myotonic dystrophy, both diagnosed with “Wei” disease in traditional Chinese medicine (TCM), with syndromes of liver-kidney deficiency and spleen-stomach weakness, respectively. The treatment methods included moxibustion, acupuncture at specific points, and a combination of TCM decoctions with modifications. After treatment, the patients showed improvement in their symptoms. This study provides new insights into the TCM treatment of DM.
Research on the Inheritance and Innovation of Craftsman Spirit in the Training of Skilled Talents in Higher Vocational Colleges in the Post-epidemic Era
Authors: Zhu Xiaolan , Liu Bo , Kou Heung
Published: January 2025

In the post-pandemic era, vocational colleges face new challenges and opportunities in the training of skilled talents. This article provides an in-depth analysis of the training of skilled talents and the inheritance and innovation of craftsmanship in educational practice. By interpreting the current educational background, the article explains the new trend of development in vocational colleges and the impact of the pandemic on the training methods and content of skilled talents. Furthermore, it reveals the importance and essential requirements of craftsmanship in the training of skilled talents, and proposes the necessity of integrating craftsmanship into the process of training skilled talents. This article mainly discusses the ways of inheriting and innovating craftsmanship to meet the demands of the new era, aiming to provide theoretical support and practical guidance for vocational colleges in cultivating skilled talents that can adapt to social development. The research results show that combining the strategies of inheriting craftsmanship in teaching reform and curriculum development not only helps enhance students' professional skills and innovation abilities but also strengthens their sense of professional identity and responsibility.
An Empirical Study on the Application of ChatGPT in Writing Learning for English Major Students
Authors: Deng Xiaolan
Published: January 2025

With the widespread use of artificial intelligence tools such as ChatGPT in English writing, the academic community has begun to pay attention to its potential in improving writing efficiency and quality. However, there is still a lack of research on its application in actual teaching, especially in terms of grammatical accuracy and academic integrity. This study aims to explore the impact of ChatGPT on English writing teaching, especially its role in improving students’ writing efficiency, autonomous learning ability and writing quality. In the study, students in the experimental group use ChatGPT to obtain writing suggestions, grammar revisions, content expansion and other support during the writing process. The researchers conduct a before-and-after comparative analysis by recording students’ writing time, revision time, completion rate and teacher scores. The results showed that after using ChatGPT, students’ writing efficiency was significantly improved, the average writing time was shortened by 35 minutes, and the revision time was also reduced accordingly. In terms of teacher ratings, teachers’ scores on student writing increased by an average of 1.26 points. The results of the study indicate that AI tools play a positive role in improving writing quality. In addition, students’ independent learning ability and problem-solving ability had also been significantly improved, with an average increase of 1.89 points and 1.36 points, respectively. Overall, this tool has significant advantages in improving writing efficiency and quality, and can effectively support students’ independent learning. However, in terms of academic integrity and grammatical accuracy, further in-depth exploration and optimization are still needed.
A Comprehensive Study of the Need for Emotional Health and Mental Health Support Programs in Educational Institutions
Authors: Ray Debmalya
Published: January 2025

The mental health of students and teachers is a critical concern that significantly impacts their overall well - being, academic performance, and ability to function effectively within educational settings. This study aims to comprehensively assess the mental health status of students and teachers in an educational institution by employing two well - established instruments: the Mental Health Questionnaire and Beck's Depression Inventory (BDI). The study involved a total of 439 participants, comprising 186 students and 253 teachers, providing a broad perspective on the mental health challenges within the institution. The Mental Health Questionnaire was designed to gather insights into the multifaceted challenges faced by participants in their lives, academic pursuits, and relationships both within and outside the institution. The BDI, developed by Aaron T. Beck in the 1960s, is a widely used instrument for measuring the presence and severity of depressive symptoms through 21 multiple - choice questions. Key findings from the study indicate alarmingly high levels of emotional distress among students, with 61.7% experiencing severe to crisis - level emotional distress, significantly higher than the global average of 30.19%. Moreover, 28.3% of students expressed suicidal intentions, which is notably higher than the global average of 16.9%. Additionally, 65.7% of students reported severe to crisis - level emotional turmoil, and 46.9% experienced a severe to crisis - level complete loss of motivation. Issues related to the mind - body connection were reported by 57.8% of students, while 33.3% felt lonely, which aligns with the global average of 33%. Anger issues were noted by 17.1% of students, and 9.6% faced challenges related to friendships. The need for mental health support was overwhelmingly evident, with 99.4% of students expressing the need for a dedicated mental health program. Furthermore, 45.5% of students indicated a need for personal guidance and mentorship, 79.1% believed counseling for parents is required, and 90.1% thought counseling for teachers is necessary. The BDI analysis revealed that 39.24% of students fall within the minimal to no depression range, 16.46% in the mild depression range, 20.25% in the moderate depression range, and 21.51% in the severe depression range. This distribution highlights that over half (53.22%) of the student population is experiencing some level of depression, necessitating urgent and proactive mental health interventions. The study underscores the importance of implementing comprehensive mental health programs within educational institutions to address the high prevalence of emotional distress and depressive symptoms. Proactive mental health interventions are essential to fostering a supportive learning environment, ensuring that students and teachers can achieve their full potential both academically and personally.
Research on Optimization of Training Path for Intelligent Financial Talents in Digital Economy Era
Authors: Zhang Yijun , Yuan Dan
Published: January 2025

At present, as an important support for cultivating and expanding new quality productivity, the rapid development of digital economy has caused changes in employment structure and employment quality, and the transformation of financial talents is imminent. This paper expounds the impact of digital economy on the accounting industry, points out the existing problems in the training of financial talents in colleges and universities, and believes that colleges and universities can optimize the training path of intelligent financial talents from the aspects of changing the orientation of talent training, adjusting the course structure system, emphasizing the training of “finance + technology” composite ability and strengthening the construction of teachers.
RMB Internationalization and Currency Co-movement of Belt and Road Initiative Countries: Evolution and Impact Analysis
Authors: Cai Jieting
Published: January 2025

Since the launch of the pilot program for RMB settlement in cross-border trade in 2009, the process of RMB internationalization has advanced steadily, achieving significant milestones in cross-border trade and investment settlement, the development of offshore RMB markets, and currency swap agreements. Between 2013 and 2023, the exchange rate co-movement between the RMB and the currencies of Belt and Road Initiative (BRI) countries gradually increased, and the RMB’s position in the global currency market has continued to rise. To examine the degree of linkage between the RMB and the currencies of BRI countries, as well as the evolution of its monetary influence, this paper first selected 91 representative currencies from BRI countries, estimates the exchange rate linkage between the RMB and other currencies, and applies the PMFG algorithm to construct an exchange rate co-movement network for analysis. Furthermore, the paper explores changes in the RMB’s influence during the progression of the BRI and compares its influence with that of the U.S. dollar and the euro in the region.