bluuebunny/crossref_metadata_2025 huggingface dataset:
Balancing Food Costs and Women's Health through Nutrition: A Journey of Mathematical Modeling
Authors: Arun Kumar Chaudhary
Published: January 2025

This article aims to investigate how a Linear Programming (LP), issue might be defined with an emphasis on women's health management.  Next, we'll create a mathematical model that minimizes the expense of a healthy meal throughout the day by optimizing the diet program relying on the no. of calories required for daily intake (during mensuration), diet budget and the healthiest meal distribution throughout a day. After that, we'll solve the problem using the R program to get optimized results.
Reliable Email Spoofing Detection using Enhanced Cybersecurity Approaches
Authors: Deepak Mane
Published: January 2025

In today’s modern era Email spoofing is a serious danger to email communication because it allows hackers to impersonate legitimate senders and change email headers like the email ID and IP address. And email headers are vital parts of any emails that carry important details about the sender, recipients, email delivery method, and email content. These headers are essential for email management and communication since they fulfill a variety of functions, such as email filtering, security, and traceability. So, if the non-legitimate user sends an email then mainly any of the important fields in headers is mismatched. So the proposed mechanism in this paper provides a  reliable email spoofing detection at the server level, utilizing cutting-edge cyber security practices and artificial intelligence, in response to this expanding concern.
A Review: Word Embedding Models with Machine Learning Based Context Depend and Context Independent Techniques
Authors: Kapil Adhar Wagh
Published: January 2025

Natural language processing (NLP) has been transformed by word embedding models, which convert text into meaningful numerical representations. These models fall into two general categories: context-dependent methods like ELMo, BERT, and GPT, and context-independent methods like Word2Vec, GloVe, and FastText. Although static word representations are provided by context-independent models, polysemy and contextual subtleties are difficult for them to capture. These issues are addressed by context-dependent approaches that make use of sophisticated deep learning architectures to produce dynamic embeddings that are impacted by the surrounding text. This review highlights the machine learning underpinnings of these paradigms while examining their development, approaches, and comparative advantages. We examine their benchmarks, applications, and the trade-offs associated with various use cases. The study also identifies future research directions, such as hybrid embeddings and multimodal learning, and highlights contemporary issues, such as scalability and interpretability. The goal of this thorough review is to assist practitioners and researchers in choosing and refining embedding strategies for a variety of NLP tasks.
Skin Cancer Detection and Classification Using Deep Learning Approach
Authors: Yashwant S. Ingle
Published: January 2025

Skin cancer is among the most common and potentially lethal types of cancer globally, highlighting the urgent necessity for precise and effective screening techniques. This research presents an innovative method for skin cancer diagnosis employing an encoding-decoding technique with convolutional neural networks (CNNs). The encoding-decoding approach enables effective feature extraction from skin lesion images. Concurrently, we utilize CNN architectures, including DenseNet201, VGG16, and Xception, to categorize skin lesions into seven classifications. We meticulously assess the methodology using an extensive dataset, and experimental findings validate the efficacy of the suggested strategy in precisely detecting different forms of skin cancer. Moreover, comparative research of several CNN designs yields significant insights into their strengths and weaknesses for skin cancer diagnosis. This research enhances the domain of computer-aided diagnostics for skin cancer, presenting optimistic opportunities for early identification and better patient outcomes.
Mathematical Modeling for Enhancing Business Strategies in the Hotel and Restaurant Industry: Implications for Economic Stability
Authors: Andriy Kruhlyanko
Published: January 2025

This paper explores the application of mathematical modeling as a strategic tool to enhance business operations in the hotel and restaurant industry, with a focus on promoting economic stability. The primary objective is to identify and analyze key factors influencing business performance, including customer satisfaction, operational efficiency, and resource allocation. Using quantitative methods, we developed various mathematical models to simulate different business scenarios and assess their potential impact on profitability and sustainability. The study employs data from industry case studies and surveys to inform the models, allowing for a comprehensive analysis of trends and patterns. Results indicate that mathematical modeling can significantly improve decision-making processes by providing insights into optimal pricing strategies, inventory management, and workforce allocation. The findings underscore the importance of adapting to changing market conditions and consumer preferences through predictive analytics. Overall, the integration of mathematical modeling into business strategies enhances operational effectiveness and contributes to the long-term economic stability of hotels and restaurants. This paper stresses industry stakeholders' need to embrace data-driven approaches to navigate challenges and capitalize on opportunities in a competitive landscape. Mathematical modeling helps businesses in the hospitality sector improve resilience and adaptability, driving sustainable growth.
Economic and Mathematical Modeling of Energy-Saving and Resource-Saving Innovations in the Project Approach to Crisis and Strategic Management of Hospitality Industry Enterprises Based on Corporate Law and Public Administration: Application of Numerical M
Authors: Oleksii Hutsaliuk
Published: January 2025

This study investigates how energy and resource-saving innovations can enhance crisis and strategic management within the hospitality industry through mathematical modeling and analysis. We specifically explore the integration of these innovations with principles of corporate law and public administration, employing quantitative methods to assess their impact and effectiveness. Utilizing regression analysis and factor analysis’ numerical techniques, we identify key trends and relationships among variables such as the adoption of energy-efficient technologies ((X_1)), implementation of resource-saving practices ((X_2)), and employee training programs on sustainability ((X_3)). The regression model is expressed as (Y = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_3 + \epsilon), where (Y) represents operational cost reduction. Qualitative data were analyzed using NVivo to systematically code and identify themes from interviews. Findings indicate that adopting energy-efficient technologies can lead to a reduction in operational costs by as much as 30%. Furthermore, organizations that implement resource-saving practices tend to experience increased customer satisfaction and loyalty, measured by a satisfaction index (S = \frac{\sum_{i=1}^{n} C_i}{n}). This study provides insights into the mathematical approaches that elucidate the relationship between energy efficiency, corporate law, and public administration. The use of quantitative methods enhancine the strategic decision-making processes within hospitality enterprises.
Learning Technology as a Supporting Infrastructure for Learning in the 4.0 Era in the Department of Building Engineering Education, Faculty of Engineering, Manado State University
Authors: Djubir R.E. Kembuan
Published: January 2025

In an educational institution, learning outcomes are an important indicator of the success of the teaching and learning process. This study aims to analyze the implementation of Learning Technology as a Supporting Infrastructure for Learning in the 4.0 Era at Universities. The research method used is quantitative research with a correlational approach to find the influence of each variable. This study found a significant positive influence between learning facilities and student learning outcomes. In addition, the results of this study also found a direct positive and significant influence between learning facilities and student learning independence. Another result is a direct positive and significant influence between learning facilities and student learning commitment. This study can conclude that lecturer performance, learning facilities, learning commitment, and student learning independence positively influence student learning outcomes.
Controllability Analysis of Coal Mill Pulverizer Model
Authors: Jaita Sharma
Published: January 2025

The most essential need for today’s human life apart from food, air and water is electricity. Our life gets stuck if there is a cut-off for a few hours. The Electricity is produced in various types of power plants. Our interest is to study the process of coal-fired power plants. Coal pulverizer or coal mill play a very important role in the performance and reliability of any sub-critical or super critical coal-based power plant. The coal mill plays a crucial role in the thermal power generation process, where coal is ground into a fine powder to be used as fuel in boilers. Over the years, the modeling of coal mills has undergone significant evolution, driven by advancements in mathematical modeling techniques, computational capabilities, and a deeper understanding of the complex physical and chemical processes involved. This article discussed the controllability of coal mill pulverizer process. Controllability of this process is obtained through modern control theory.
Relatively Prime Domination Number in Quadrilateral Snake Graphs
Authors: A. Anat Jaslin Jini
Published: January 2025

A set S⊆V is said to be relatively prime dominating set if it is a dominating set with at least two elements and for every pair of vertices u and v in S, (deg⁡(u),deg⁡〖(v))〗=1. The minimum cardinality of a relatively prime dominating set is called relatively prime domination number and it is denoted by γ_rpd (G). If there is no such pair exist, then γ_rpd (G)=0. For a finite undirected graph G(V,E) and a subset  V, the switching of G by  is defined as the graph  (V,  ) which is obtained from G by removing all edges between  and its complement V-  and adding as edges all non-edges between  and V- . This article delves into the discussion of the relatively prime domination number on quadrilateral snake graphs and their complements. The findings reveal that for quadrilateral snake graphs, the relatively prime domination number γ_rpd(G^v) equals either 2, 3 or 4. Similarly, for alternate quadrilateral snake graphs, the γ_rpd(G^v) is determined to be 2, 3 or 4. In the case of double quadrilateral snake graphs, the relatively prime domination number γ_rpd(G^v) is established as 2, 3, 4, 6 or 7, while for double alternate quadrilateral snake graphs, it is 2, 3, 4 or 5. Notably, the complements of quadrilateral, alternate quadrilateral, double quadrilateral, and double alternate quadrilateral snake graphs exhibit a relatively prime domination number of 2.
AutoETL: A Nonlinear Deep Learning Framework for ETL Automation
Authors: G. Sunil Santhosh Kumar
Published: January 2025

This study presents a nonlinear framework for automating Extract, Transform, Load (ETL) processes. The framework uses natural language processing techniques, transformer-based models, and reinforcement learning to convert unstructured data into structured formats. It focuses on creating and refining transformation rules based on data patterns. The research addresses challenges in automating ETL processes, particularly the need to handle complex data relationships without manual input. The TPC-DI dataset is used to test the framework, which transforms financial newswire data into a structured warehouse format. The process follows ACID and OpenClass standards. The framework includes data preparation through tokenization and normalization. A transformer-based model processes sequences to identify patterns. Reinforcement learning refines transformation rules using feedback. The methods ensure structured data alignment measured through metrics like Intersection over Union (IoU), mean average precision (mAP), and mean squared error (MSE). The results show consistent performance across various data thresholds, highlighting its ability to handle diverse data patterns. This research outlines a method to automate data handling while reducing manual involvement, with potential applications across domains.
Improving Retinopathy Classification Using Optimized Support Vector Machines and Deep Learning Techniques
Authors: K. Deepthi
Published: January 2025

Retinopathy is one of the primary causes of blindness, making early detection and diagnosis challenging. Conventional techniques of retinopathy classification depend on manual observation and rule-driven methods, which may be time-consuming and error-prone. Machine learning algorithms, such as Support Vector Machines (SVMs) and Deep Learning (DL), have recently exhibited efficacy and substantial accuracy enhancements in classification. But problems like overfitting, parameters adjustment and the requirement for huge annotated datasets can limit the power of these models. Therefore, in this paper, we are proposing enhanced classification of retinopathy by-helping Support Vector Machines and deep learning model. More specifically, we present a hybrid approach which combines a deep convolutional neural network (CNN) for feature extraction with a finely-tuned SVM for classification. To improve the generalization ability of the SVM, we utilize a grid search approach to optimize the SVM parameters. We validate our method on a publicly available retinopathy dataset which shows a strong gain in classification accuracy over existing methods. We present our approach, with the results showing that, compared to traditional SVM and CNN-based models, we achieve an accuracy of 95%, while reducing false positives. Providing early detection and alleviating the burden on healthcare staff, this hybrid approach provides a more robust and scalable method for automated screening of retinopathy. Overall, we have presented a novel model that now automates retinopathy prediction to a point where it could be useful in the clinic.
Artificial Intelligence and Machine Learning based Farmer’s Friendly Soil Fertilizer Recommendation System Through Expert Analysis
Authors: Anil Kumar Ghadiyaram
Published: January 2025

One of the key components of the Indian monetary system is agriculture and as is well known, farming supports about 60% of the Indian population and accounts for a sizable portion of the country's GDP. As time goes on, India is becoming one of the world's most significant food exporters. The demand for meals has increased as a result of the growing population. Many educated individuals who work as farmers now also work in agriculture. A promising strategy for increasing crop productivity and maximizing use is precision farming which utilizes machine learning (ML) and Internet of Things (IoT). However, due to a decline in productivity, farmers are still facing issues with significantly lower incomes. Even farmers are making poor crop, fertilizer, and soil choices. This study offers a novel solution to this problem by employing professional analysis and ML and artificial intelligence (AI) techniques to create a farmer-friendly soil fertilizer recommendation system. After analyzing the records, the AI machine uses this suggested technique to provide farmers with answers based on expert review and a thorough historical record set. Therefore, farmers may find the crop suggestion systems to be highly advantageous. Additionally, a number of factors, including pH, nitrogen, phosphorus, potassium, and rainfall, can affect crop output. Therefore, we are offering a method in this paper for using AI and ML algorithms to increase crop yield.
Optimized Energy-Efficient Routing and Adaptive Coverage in Wireless Sensor Networks for Multi-Access Edge Environments
Authors: Sreeram Sivadasan
Published: January 2025

The growing demand for edge computing necessitates optimizing wireless sensor networks (WSNs) for efficient real-time data processing. Achieving energy efficiency and dynamic coverage in WSNs, particularly in multi-access edge environments, is a key challenge. This paper introduces a framework with three core algorithms to enhance network coverage and routing in such environments. The first algorithm uses Delaunay Triangulation to detect coverage gaps, identifying areas with insufficient sensor deployment. To address these gaps, the second algorithm applies a dynamic node placement strategy, where additional sensors are strategically positioned, optimized using clustering and refined Delaunay Triangulation to integrate new nodes. The third algorithm proposes an adaptive tree-based routing protocol using a Minimum Spanning Tree (MST) to conserve energy by rerouting data through energy-efficient paths. Together, these algorithms form a resilient WSN system that adapts to environmental changes, improving data reliability and system performance. Simulation results demonstrate significant improvements in network lifetime and efficiency within multi-access edge environments.
Economic Efficiency of Innovative Development in Production Enterprises Based on the Use of Energy Resource-Saving Systems in the Context of Digitization: An Applied Nonlinear Analysis Perspective
Authors: Olena Druhova
Published: January 2025

The aim of this study is to explore the economic efficiency of innovative development in production enterprises through the implementation of energy resource-saving systems, particularly in the context of digitization. This research employs a mixed-methods approach, utilizing quantitative data analysis through software tools such as SPSS and qualitative interviews with industry experts. Surveys were conducted among production enterprises to gather insights on energy-saving practices and their economic impacts. The study found that enterprises implementing energy resource-saving systems experienced a significant reduction in operational costs, improved productivity, and enhanced sustainability metrics. The data indicated an average cost savings of 15% and a productivity increase of 20% post-implementation. To further analyze these findings, the study incorporates Applied Nonlinear Analysis to model the complex relationships between energy resource-saving systems, operational efficiency, and productivity outcomes. This analytical framework allows for a deeper understanding of how nonlinear interactions among various factors influence economic performance in production settings. The findings suggest that the integration of energy resource-saving systems not only contributes to economic efficiency but also aligns with broader sustainability goals. The study concludes that digitization plays a crucial role in optimizing these systems. This research is beneficial for stakeholders in the fields of industrial engineering, environmental management, and corporate sustainability. This study introduces a comprehensive framework for assessing the economic impacts of energy resource-saving systems in production enterprises, contributing new insights into the intersection of digitization, sustainability, and nonlinear analytical methods.
Pebbling on Crisscross Sequence of m Complete Graphs
Authors: J.Jenifer Steffi
Published: January 2025

This paper investigates the pebbling number, the two pebbling property, the t-pebbling number, and the 2t-pebbling property of a crisscross sequence comprised of m complete graphs. By analyzing these pebbling properties, we aim to gain insights into the efficiency and feasibility of pebbling operations within this specific graph structure.
Endomorphisms and Their Role in Novel Derivations of Hilbert Algebras
Authors: Aiyared Iampan
Published: January 2025

This study introduces and explores left--derivations, right--derivations, and -derivations of type  and type  within the framework of Hilbert algebras, focusing on their fundamental properties and structural significance. We show that the kernel of an -derivation, , forms a near filter and operates as a subalgebra of the Hilbert algebra . These findings enhance the understanding of Hilbert algebras and offer new perspectives on the role of derivations in algebraic logic.
QoS-Aware and Energy-Efficient Network Design for Green Wireless Communication
Authors: Duda Sudhakar
Published: January 2025

With the sharp rise in the number of devices and high data rate demand, the concepts of cell splitting and frequency reuse have become integral to cellular communication. However, these strategies increase the number of base stations, which in turn raises radiation levels and energy consumption. Consequently, green network design has become an important aspect of cellular communication. GREEN NETWORK stands for Globally Resource Optimized Energy Efficient Network; this research focuses on optimal resource allocation for creating an energy-efficient cellular network with improved performance. By switching-off some base stations during non-peak hours, significant energy efficiency can be achieved. We developed an adaptive base station switch-off algorithm that selects a low traffic, medium traffic, or high traffic switch-off method based on current traffic conditions. During low and medium traffic conditions, switching-off some of the existing base stations significantly saves power without affecting quality of service (QoS).
Degree of Approximation of the Conjugate of Functions Belonging to LIP (α,r)-Class by (C,1)(E,q)(E,q) Means of Conjugate Fourier Series
Authors: Rupesh Kumar Mishra
Published: January 2025

This research paper is related to the degree of approximation of the conjugate of 2π-periodic function belonging to the Lip(α,r)(0<α≤1,r≥1)- class by using (C,1)(E,q)(E,q) means of the conjugate Fourier series. Our result may be useful for the coming researchers in the future.
Multimodal Hand Gesture Recognition Using Surface Electromyography and Inertial Measurement Units with 3D-CNN and Transfer Learning
Authors: Anju Markose
Published: January 2025

Hand gesture recognition (HGR) is pivotal for enhancing human-computer interaction (HCI) by enabling intuitive interfaces across various domains, including virtual reality, robotics, and smart environments. Traditional vision-based HGR methods often encounter challenges such as occlusions, lighting variations, and computational inefficiencies. To address these issues, this study proposes an innovative approach that integrates surface electromyography (sEMG) and inertial measurement units (IMUs) for capturing both muscle activity and motion dynamics directly from users' hands. The methodology begins with rigorous data collection and preprocessing steps tailored for sEMG and IMU data, focusing on noise elimination and standardization to ensure data quality. Advanced feature extraction techniques, including time-domain and frequency-domain analyses, are employed to extract discriminative features from both sensor modalities. The fused sEMG and IMU data streams are then fed into a 3D convolutional neural network (3D-CNN) architecture, leveraging transfer learning to enhance model performance and generalization capabilities. Experimental results showcase the efficacy of the proposed methodology in achieving high accuracy and robustness in gesture recognition tasks. Performance metrics such as accuracy, precision, recall, and F1 score are extensively evaluated, demonstrating superior performance compared to traditional vision-based methods and existing multimodal approaches. Real-time testing further validates the system's responsiveness and reliability, confirming its suitability for real-world applications. This research contributes to advancing HGR systems by leveraging multimodal sensor data and deep learning techniques, thereby facilitating more natural and efficient human-computer interactions. The scalability and adaptability of the proposed methodology make it a promising candidate for diverse HCI applications, underscoring its potential to transform user experiences in interactive technologies. Future work will focus on refining the system's architecture, expanding the gesture vocabulary, and exploring novel applications in healthcare, gaming, and beyond.
Application of a Non-Mixture Cure Rate Model for Analyzing Survival of Patients with Brain Cancer
Authors: Awaz Shahab Mohamad
Published: January 2025

The results of the discovery of very good treatments for all types of cancer in recent years have led to a decrease in mortality. We deal with these observations as cure or immune and models for survival data which involve cure fraction are known as cure rate models or long-term survival models. Methods and Materials, during the past few decades the statistical methods for survival analysis of data have found applications in wide range of fields especially in medical researches, The aim of this research is to study the most factors affecting of brain cancer in Erbil city using Weibull parametric and Logistic-Cox non mixture cure survival models for modelling and identifying the most affecting factors of brain cancer in our data. The data used in this research was obtained from Rzgari Hospital for Cancer in the Kurdistan Region of Iraq – Erbil. The results for our data showed that Extent and Histopathology are the most important factors affecting the brain cancer.
Classification of Maps on Intervals That Exhibit Dense Periodic Points
Authors: Suja N Thomas
Published: January 2025

In this paper, continuous maps on interval with dense set of periodic points such that the periods being set of natural numbers are studied.These maps named as Peri- odically Rich maps could be divided into two classes on Interval, such as the maps with period four points having increasing periodic orbit and the maps with two fixed points such that image of a subset of Interval does not belong to the same subset. Notion of bigness of Periodically Rich maps is also discussed.
Fruit Volume Unveiled: A Creative Exploration of Non-Destructive Mathematical Modeling Techniques, a Review
Authors: Leela Santi Parige
Published: January 2025

Accurate estimation of fruit volume is crucial for agricultural productivity, supply chain management, and quality control. This study presents a comprehensive exploration of non-destructive mathematical modelling techniques aimed at estimating fruit volume, focusing on innovative approaches that preserve the integrity of the fruit during measurement. We systematically review existing methodologies, including geometric models, imaging techniques, and machine learning algorithms, highlighting their strengths, limitations, and practical applications. Through a meta-analysis of empirical data, we assess the effectiveness and accuracy of these methods across various fruit types. Our findings reveal a trend toward integrating multi-sensor data and advanced computational techniques to enhance precision. This exploration not only provides insights into current practices but also suggests future directions for research and application in agricultural technology, paving the way for sustainable and efficient fruit production.
Objectives: The objective is to evaluate and synthesize various measurement methodologies for assessing fruit integrity, aiming to identify best practices and propose innovative approaches that can improve accuracy and efficiency in agricultural technology.
Methods: study demonstrate a growing emphasis on automation and real-time data collection, which are crucial for optimizing yield and minimizing waste in fruit cultivation.
Results: indicate a significant improvement in measurement accuracy when utilizing hybrid approaches that combine traditional methods with innovative technologies.
Conclusions: In conclusion, the integration of innovative measurement techniques stands to significantly transform the agricultural landscape, promoting better yield assessments and quality control while ensuring that fruit integrity is maintained throughout the process
Performance Evaluation of CFO and Channel Estimation in Hybrid Tone OFDM Signals
Authors: Shaik Riyazuddien
Published: January 2025

Introduction: There is a growing need for high-accuracy signal processing in 5G and future 6G networks. Reconfigurable Intelligent Surface (RIS)-assisted OFDM is a promising solution to enhance channel quality. Hybrid tone OFDM (HT-OFDM) introduces a new approach to improve carrier frequency offset (CFO) and channel estimation.
Objectives: This paper aims to develop an efficient method for joint CFO and channel estimation in HT-OFDM. The goal is to minimize the mean square error (MSE) and improve estimation accuracy.
Methods: The method starts with correlation and least-squares techniques to get initial CFO and channel estimates. A hybrid tone is then inserted into OFDM symbols. This step reduces noise and enhances accuracy by lowering MSE. Simulations and theoretical analysis validate the approach.
Results: At 20 dB SNR, the method reduces MSE to 0.02 when 100 RIS elements are used. The CFO estimation error drops to 0.005 for a CFO value of 0.5. Increasing pilot subcarriers to 32 at 30 dB SNR further lowers MSE to 0.01. Additional hybrid tone symbols show significant improvements in high-SNR conditions.
Conclusions: The proposed method effectively enhances CFO and channel estimation. It achieves significant MSE reduction and improves accuracy in RIS-assisted HT-OFDM systems. This makes it suitable for advanced 5G and 6G networks.
The Role of Internationalization and Innovation in Enhancing Organizational Performance: Evidence from SMEs in Wuhan, China
Authors: Guo Chen
Published: January 2025

Abstract: This study explores the dynamic relationship between internationalization and innovation, and their joint impact on the organizational performance of small and medium-sized enterprises (SMEs) in Wuhan, Hubei Province, China. Based on data from 404 SMEs, the research analyzes the structural links between internationalization (INT), innovation (INN), and organizational performance (OP), offering valuable insights into how these factors contribute to the success of SMEs in a globalized business environment. The theoretical framework integrates the Revised Uppsala Internationalization Process Model, the Innovation-Related Internationalization Model, and the Resource-Based View (RBV) Theory. Findings reveal that innovation plays a vital mediating role in the relationship between internationalization and organizational performance, highlighting that SMEs can improve performance not just through entering global markets, but by embedding innovation within their operations. The study shows that firms that combine internationalization strategies with innovative practices achieve superior outcomes in terms of financial growth, operational efficiency, and market competitiveness. This research provides practical recommendations for entrepreneurs, policymakers, and academics. Entrepreneurs are encouraged to synchronize internationalization with innovation to build competitive advantages, while policymakers are urged to support initiatives that foster both innovation and international expansion among SMEs. Academically, the study fills a gap in the literature by examining the empirical relationship between these variables in the Chinese SME context. Future research opportunities are proposed, including expanding the study to different industries and regions, integrating qualitative approaches, and considering external factors influencing internationalization and innovation. This work contributes to theory and practice by offering a robust framework for understanding how SMEs can strategically leverage internationalization and innovation to enhance performance in an increasingly interconnected global market.
Exploring the Impact of Job Satisfaction on Educator Performance in Private Universities in Anhui, China
Authors: Wang Li
Published: January 2025

This study examines the crucial link between job satisfaction and teaching performance among educators at private universities in Anhui Province, China. As competition in higher education intensifies and educational paradigms evolve, the well-being of faculty members has become a key factor in driving both individual and institutional success. Using a mixed-methods approach, the research integrates quantitative surveys with qualitative interviews to capture a wide range of perspectives from educators. The results highlight a strong positive relationship between job satisfaction and teaching performance, indicating that educators who feel valued and supported are more likely to demonstrate higher levels of engagement, effectiveness, and commitment. The study identifies several key factors contributing to job satisfaction, including a supportive work environment, fair workload distribution, professional development opportunities, and effective communication between faculty and administration. In contrast, workplace stressors were found to negatively impact teacher morale and productivity, emphasizing the need for institutions to foster a positive organizational culture. Based on these findings, the study provides actionable recommendations for university leaders and policymakers to enhance job satisfaction, thereby improving teacher performance and overall educational outcomes. By prioritizing educator well-being, private universities in Anhui can create an environment that not only enhances individual performance but also supports the long-term success and sustainability of the educational system. This research offers valuable insights for higher education stakeholders seeking to improve institutional effectiveness through a focus on teacher satisfaction.
Analysis of AI-Powered Student Assistance Chatbot for admission through MHT-CET
Authors: Kavita A. Sultanpure
Published: January 2025

AI technologies are revolutionizing the admissions process in higher education by making it more accessible and supportive. This paper presents an AI-Powered Student Assistance Chatbot designed to guide applicants through the Maharashtra Common Entrance Test (MHT-CET) admissions. The chatbot provides real-time, personalized responses to queries on eligibility, application deadlines, and available courses, ensuring accurate and timely assistance. The chatbot not only reduces the administrative burden but also enhances student engagement by offering a seamless conversational interface. Insights from leading chatbot implementations are integrated to improve its reliability and user experience. Additionally, data analysis of frequently asked questions enables institutions to optimize their services based on evolving student needs. This research highlights the transformative role of AI chatbots in streamlining the admissions process, bridging the gap between technology and human interaction for an enriched student experience.
The sphenofrontal suture: Age, gender characteristics, and correlation with craniometric parameters
Authors:   Azerbaijan Medical University, Abdullayev Anar
Published: January 2025

The purpose of the study was to study the length of the sphenofrontal suture in age and gender aspects, as well as its correlation with
craniometric parameters.
Methods. The research material consisted of 200 skulls. There were 20 skulls of adolescence age, I adulthood age 68, II adulthood
age 72, and elderly age 40. In total, there were 86 male skulls and 114 female skulls. The statistical significance of the difference between
the groups' indicators was assessed by the Student-Bonferroni t-test, F-Fisher tests, nonparametric Mann-Whitney U-test, and nonparametric
Kruskal-Wallis H-test. A non-parametric ρ-Spearman's rank correlation was used in the study.
Results. Analysis of male skulls by age did not reveal a statistically significant difference for the length of the left sphenofrontal suture
(PF = 0.177; PH = 0.142). Also, on male skulls, the length of the right sphenofrontal suture values did not have a statistically significant difference
(PF = 0.916; PH = 0.936). On female skulls, the value of the studied parameter also did not differ statistically across age periods: for the left
sphenofrontal suture (PF = 0.643; PH = 0.688) and for the right sphenofrontal suture (PF = 0.956; PH = 0.880). The left sphenofrontal suture’s
length differed statistically significantly between male and female skulls only in adulthood age II (PF < 0.001; PU < 0.001). The length of the
left sphenofrontal suture had a statistically significant direct correlation with a large number of craniometric parameters (maximum cranial
length, nasio-occipital length, maximum cranial breadth, basion-bregma height, cranial base length, maxilla-alveolar breadth, minimum
frontal breadth, upper facial breadth, nasal height, nasal breadth, left orbital height, right orbital height, parietal chord, mastoid height and
biasterionic breadth). The length of the right sphenofrontal suture was statistically significantly directly correlated with cranial base length, left
orbital height, right orbital height, parietal chord, and biasterionic breadth.
Conclusion. Considering that isolated premature synostosis of the sphenofrontal suture has been increasingly identified in clinical
practice in recent years, study is of not only theoretical but also practical interest.
Diagnóstico y tratamiento integral de un paciente pediátrico con intrusión dentaria y fractura de la tabla ósea vestibular. Reporte de un caso

//Diagnosis and comprehensive treatment of a pediatric patient with a dental intrusion and fracture of the vestibular bone table. A case report
Authors: Cutura Nuñez Natalia E. , Cerati Ariel , Pérez Campos Cristian F. D. , Gruce María
Published: January 2025

Objetivo: Presentar un caso clínico en el cual se realizó el tratamiento integral para abordar una intrusión severa de las piezas dentarias 11, 21 y 22 y fractura de la tabla ósea vestibular, restableciendo la función y la estética de las piezas dentarias traumatizadas.
Caso clínico: Se presenta un niño de 10 años que, tras sufrir un accidente en una motocicleta, ingresa a la guardia odontopediátrica del Hospital Interzonal Especializado Materno Infantil “Don Victorio Tetamanti”, presentando intrusión severa de las piezas dentarias 11, 21 y 22, acompañado de fractura de la tabla ósea vestibular. Se describe el tratamiento integral realizado, que involucró la reposición de las piezas dentarias afectadas y su ferulización, su seguimiento, tratamientos endodónticos, evolución a largo plazo, rehabilitación y posterior tratamiento de ortopedia. Un diagnóstico correcto, acompañado de un tratamiento precoz y oportuno, así como un abordaje transdisciplinario, son fundamentales para alcanzar el éxito y minimizar las complicaciones de los traumatismos dentarios.
Palabras clave: Férula dental, fractura alveolar, fractura coronaria, intrusión dentaria, lesión bucal, traumatismo dental.

//Aim: To present a clinical case in which an integral treatment was carried out to address a sever intrusion of the 11, 21 and 22 teeth and fracture of the vestibular bone table, restoring the function and aesthetics of the traumatized teeth.
Clinical case: A 10-year-old child who, after suffering a motorcycle accident, enters the pediatric dentistry guard of the “Don Victorio Tetamanti” Interzonal Specialized Maternal and Infant Hospital, presenting by severe intrusion of teeth 11, 21 and 22, accompanied by a fracture of the vestibular bone table. The emergency treatment carried out is described, which involved the replacement of the affected teeth and their splinting, their follow-up, long-term evolution, rehabilitation and subsequent orthopedic treatment. A correct diagnosis, accompanied by early and timely treatment, as well as a transdisciplinary approach, are essential to achieve success and minimize the complications of dental trauma.
Key words: Alveolar fracture, buccal injury, crown fracture, dental intrusion, dental splint, dental trauma
Innovación en servicios públicos:
Authors: Aguirre Inga Ramiro , Umaquinga-Criollo Ana
Published: January 2025

La implementación de una “Innovación en Servicios Públicos: Aplicación Web para la Recaudación Ciudadana con Enfoque ISO/IEC 25010”, surge como una medida esencial para enfrentar los desafíos inherentes al proceso de recaudación manual de servicios ciudadanos: agua potable, instalación del servicio de agua potable, mantenimiento de cometidas, reconexión de cometidas, recolección de basura, alcantarillado, en la sociedad. La adopción de SCRUM, reconocida por su enfoque ágil y adaptable, es clave en la ejecución de este proyecto. Al emplear SCRUM, se facilita una gestión eficaz del proceso, con entregas iterativas y una estrecha colaboración entre desarrolladores y usuarios finales. Este enfoque iterativo permite ajustes continuos y mejoras durante el desarrollo, garantizando que la plataforma final satisfaga las necesidades específicas de la comunidad Santo Domingo N°1 de la provincia de Pichincha, cantón Cayambe. Al integrar SCRUM y las métricas de usabilidad conforme a la ISO/IEC 25010, se busca no solo acelerar los procesos de pago, sino también proporcionar un software de alta calidad que cumpla con las expectativas de los socios y las autoridades locales.
The strategic process and competitiveness of a consulting company in the year 2022, case: JC Ingeniería y Consultoría S.A.C.
Authors: Salazar Fausto , Condori Juan , León Cayo , Bartra Arturo
Published: January 2025

This study aims to establish the level of relationship between the strategic process and the competitiveness of a consulting company in the year 2022. The results obtained from the research will allow us to determine how the strategic process is being developed in the consulting company J.C. Engineering and Consulting S.A.C. and how this impacts its competitiveness. The research will help build an effective framework to analyze, review, and measure the impact of the strategic process on business competitiveness. It is concluded that the variables 'strategic process' and 'competitiveness of a consulting company' are positively related with a correlation coefficient of 0.696. This leads to the decision to reject the null hypothesis; that is, a significant positive relationship exists between the strategic process and the competitiveness of a consulting company in 2022.
Rezension von: Leube, Herbert, Familie Leube aus Altenburg und Gera
Authors: Wollmershäuser Friedrich R. , Leube Herbert
Published: January 2025

Herbert Leube: Familie Leube aus Altenburg und Gera (Deutsches Familienarchiv 162). Insingen: Verlag Degener & Co. 2020; 328 S., zahlreiche Abb., geb., 37,00 EUR
Rezension von: Maurer, Hans-Martin, Frühe Geschichtsvereine in Baden-Württemberg
Authors: Litz Gudrun , Maurer Hans-Martin
Published: January 2025

Hans-Martin Maurer: Frühe Geschichtsvereine in Baden-Württemberg (Geschichte Württembergs. Impulse der Forschung 4). Stuttgart: W. Kohlhammer Verlag 2019; 283 S., 29, teils farbige Abb., geb., 19,00 EUR
Exploration on the Clinical Experience of Professor Liu Chunying in the Treatment of Membranous Nephropathy based on "Mingmen Theory"
Authors: Xia Danyu , Liu Chunying
Published: January 2025

Membranous Nephropathy (MN) is one of the main pathological types of adult nephrotic syndrome and a common cause of chronic renal failure. The therapeutic effect varies from person to person. After years of clinical practice, Chief Physician Liu Chunying has accumulated rich experience, especially in the treatment of MN with integrated traditional Chinese and Western medicine. The purpose of this article is to explore the etiology and pathogenesis of membranous nephropathy from the perspective of ' Mingmen theory ' and the clinical medication experience of Director Liu Chunying, in order to provide new prevention and treatment ideas for traditional Chinese medicine in MN.
Research Progress of Repetitive Transcranial Magnetic Stimulation in Limb Rehabilitation in Patients with Stroke and Hemiplegia
Authors: Yang Zhen , Mei Chunli , Zhou Mengyao , Mu Rili , Song Shuangyuan
Published: January 2025

Stroke, also known as cerebrovascular accident. It is a cerebrovascular disease caused by damage or rupture of cerebral blood vessels, resulting in vascular failure and interruption of blood flow, resulting in cerebral cell ischemia and hypoxia. This kind of injury usually causes local brain dysfunction, and the symptoms last for more than 24 hours, which can lead to death in severe cases. Repetitive transcranial magnetic stimulation (rTMS) is widely used in the field of stroke rehabilitation of the upper extremities, but it is rarely described in the literature of the lower extremities. In order to overcome these problems, the challenges we must face in the future are to determine the best parameters and the most appropriate intervention time through a large number of clinical research centers, so as to provide patients with the best treatment plan in combination with the unity of the curative effect after intervention in various periods and various rehabilitation treatment methods.
The Staged Treatment of Diabetic Nephropathy Based on the Principle of “Using Collaterals for General Use”
Authors: Zhang Lijuan , Li Xiaohui
Published: January 2025

The basic pathogenesis of diabetic nephropathy collateral disease is the syndrome of deficiency and excess of deficiency and excess of deficiency and excess of deficiency and excess of deficiency and excess of deficiency and excess of deficiency and excess of deficiency and excess of deficiency and deficiency of spleen and kidney, and the obstruction of kidney collateral by water dampness, phlegm and blood stasis and toxin. Therefore, dredging collaterals is the key treatment method. Based on this, this paper proposes the methods of replenishing qi, nourishing Yin and dredging collaterals, activating blood circulation, removing blood stasis and dredging collaterals, promoting water and removing dampness and dredging collaterals, collecting wind and dredging collaterals, nourishing kidney and detoxifying collaterals, warming Yang and dredging collaterals, which provide new ideas and methods for the treatment of diabetic nephropathy.
Research on the Application of Visual Elements of Taishan Blessing Culture in the Design of Cultural and Creative Products
Authors: Du Weiwei
Published: January 2025

Objective: To explore the application and development direction of visual elements of Mount Tai blessing culture in the design of cultural and creative products. Methods: From the four dimensions of the explanation of Mount Tai blessing culture, the analysis and extraction of visual elements of blessing culture, the application conception of visual elements, and the design strategy of cultural and creative products, the design principles of cultural and creative products with the characteristics of Mount Tai blessing culture are interpreted. Conclusions: The design of cultural and creative products with the visual elements of Mount Tai blessing culture as the design theme is conducive to promoting the cultural, innovative and unique development of Mount Tai's cultural and creative industry.