bluuebunny/crossref_metadata_2025 huggingface dataset:
WAYS TO INCREASE PRODUCTIVITY AND OVERCOME OBSTACLES IN THE INDUSTRY SECTOR OF ARMENIA
Authors:   Armenian State University of Economics, KHACHIKYAN SOS , AVAGYAN GAYANE , VARDANYAN KNARIK , PETROSYAN GARIK , MIRUMYAN AIDA , GEVORGYAN SATENIK , STEPANYAN SILVI
Published: February 2025

The industry is considered one of the crucial branches of the RA economy for
several reasons, primarily due to its extensive scope playing a considerable
intersectoral role in the economic system of the republic. Moreover, as one of the
driving forces of the RA economy, it can present significant opportunities in terms
of digitalization, communication perspectives and technological modernization.
The high level of productivity of the industry is attributed to the mining sector
which has generated great outcomes.
Effectiveness of Motivational Programme on Knowledge regarding Brest Self Examination among Self Help Groups in selected area in Mangalore
Authors: Dsouza Jacintha Jyothi , K. Supreetha
Published: February 2025

Every year we celebrate the women's day. Inspiring the women of today to stride ached in life. In India, women are becoming more aware of their health status as a result of modern education, electronic, print media and health agencies. While women have made progress in most of the field but still, she tends to neglect her own health. Though in the present age women are aware of their problems, the readiness to seek help from health personal is hindered by economic constraints, social stigma and rigid superstitious beliefs regarding health problems. Hence it is necessary to provide information to women regarding their health problems through in the available community resources. Breast cancer is one of the commonest causes of death in many developed countries in the middle age women and is becoming frequent in the developing countries too. Screening for early detection and diagnosis of diseases and health condition in an important public health need. Breast self- examination (BSE) is a process where by women examine their breasts regularly to detect any abnormal swelling or lumps in order to seek promote medical attention. Breast self -examination (BSE) is a safe, effective easy, private and an economical screening method involves no cost or require any specific equipment in the early detection of breast cancer. Women aged 20 years and older are recommended to perform BSE. Prevention or identification of breast cancer at an early stage is very importance in saving lives as well as improving the quality of life. Prevention is better than cure. The present investigation was aimed to evaluate the effectiveness of structured motivational programme on knowledge regarding Breast self-examination among women. The research design used in the study was pre – experimental one group pre-test post-test and non-probability convenient sampling technique was used to draw sample. The data was collected from 43 women using the structured knowledge questionnaire and motivational programme regarding BSE was conducted and post-test was administered after 7days using the same structured knowledge questionnaire to find out the effectiveness of motivational programme. The results revealed that the overall mean percentage knowledge score in the pre-test and post-test were 27.1% and 63.1% respectively. The statistical paired ‘t’ test (t= 20.24) was greater than the table value at 0.05 level of significance which shows that there is significance difference between the pre-test and post-test level of knowledge. There is no significant association between pre-test knowledge scores and selected socio demographic variables such as age, religion, educational status, occupation, location, marital status, income and previous information and examination of the breast.
On( sw) ̃-D Sets and Associated Separation Axioms in Topological Spaces
Authors: Chuleshwar Patel
Published: February 2025

In this article, we introduced the concept of semi-weakly- (-sets based on weakly- open sets to obtain some semi-weakly- separation axioms. From theseIn this article, we introduced the concept of semi-weakly- (-sets based on weakly- open sets to obtain some semi-weakly- separation axioms. From these new concepts of separation axioms, we obtained axioms of semi-weakly-, semi-weakly-, and semi-weakly-spaces. We also derived some characterizations new concepts of separation axioms, we obtained axioms of semi-weakly-, semi-weakly-, and semi-weakly-spaces. We also derived some characterizations
A Nonlinear Approach to FECG Signal Enhancement Using an Ensemble of Convolutional Neural Networks
Authors: Yojana Sharma
Published: February 2025

Fetal electrocardiogram (FECG) denoising is a vital aspect of prenatal monitoring, critical for precise fetal health evaluation. While convolutional neural networks (CNNs) are widely recognized for their strength in processing multidimensional data, their adaptation for one-dimensional signals like FECG remains an emerging frontier. This article introduces a novel ensemble CNN approach specifically designed to enhance FECG signals by leveraging CNN architectures hierarchical feature extraction capabilities. Extensive testing on FECG datasets with varying noise levels highlights the model’s robust performance, showing significant improvements in performance metrics, including signal-to-noise ratio (SNR) and mean squared error (MSE). The ensemble CNN approach excels at isolating FECG components from noise, achieving enhanced signal clarity and fidelity. These finding reveal the promising potential of ensemble CNN-based techniques to advance FECG denoising, thereby enhancing the accuracy of fetal health assessments in clinical applications.
Integrating Data Analysis and Predictive Analytics into an Advanced Inventory Management System using Machine Learning and Deep Learning to Address Backorders
Authors: Bhushan Jadhav
Published: February 2025

The evolution of ICT technology has benefited various businesses and applications in a variety of ways. The inventory management system is one of those that has used a variety of techniques to improve logistics and inventory management performance. The novel research introduces an advanced inventory management system that intends to improve operational efficiency in the retail industry by providing an intuitive interface for managing purchases, invoicing, client information, product catalogues, and sales numbers. The underlying inventory control systems automatically compute and apply reorder levels and safety stock quantities. Machine learning and deep learning are new technologies employed in current analytics due to their enormous benefits.As a result, the purpose of this research paper is to highlight a combined approach of providing analytical business intelligence via data visualization platforms, as well as predictive analytics via machine learning and deep learning, in order to demonstrate a significant advancement toward the automation and optimization of inventory governance processes. The proposed approach demonstrates how combining traditional software development with cutting-edge data analysis techniques can result in a more efficient, scalable, and informative inventory management solution. It addresses common retail difficulties like stockouts and overstocking by utilising powerful machine learning (ML) and deep learning (DL) techniques. Finally, empirical testing and real-world applications are used to evaluate the suggested approach, which shows significant advances in inventory optimisation as well as other benefits such as enhanced demand forecasting, cost reductions, and operational efficiency. The findings show that machine learning and deep learning have the potential to transform inventory management practices.
Sentinel Os: Strengthening Operating System Security with Comprehensive Hardening Measures
Authors: Shailaja Uke
Published: February 2025

In today's era of escalating cyber threats and pervasive digitization, cybersecurity is essential. India faces a surge in cyberattacks, affecting both government and private sectors. SentinelOS addresses the dynamic threat landscape, remote work challenges, regulatory compliance, supply chain vulnerabilities, and data breach costs. Its deep learning capabilities and robust intrusion detection systems counter sophisticated attacks. SentinelOS ensures business continuity, global awareness, and protection for education, healthcare, and e-commerce sectors. With an intuitive GUI, custom firewall, SNORT integration, and advanced intrusion detection, SentinelOS fortifies organizational cybersecurity, adhering to industry standards and providing scalability and user customization.
Changing and Unchanging Secure Integer Domination in Graphs
Authors: Gowtham Priya L,
Published: February 2025

An Integer dominating function on a graph G is a function f : V (G) → W such that for every vertex
 v ∈ V (G), . For any function f : V (G) → W and any pair of adjacent vertices with f(v) = 0 and u > 0, the function  is defined by  (l) = 1,  (l) = f(u) − 1 and  (l) = f(l) if . A secure integer dominating function on a graph G is defined as an integer dominating function  which satisfies that for every vertex v with f(v) = 0, a neighbour u with f(u) > 0 such that is an integer dominating function. The weight of f is w(f) = . Minimum weight among all the secure integer dominating function on G is secure integer domination number on G. This paper is devoted to initiating the study of SIDF of a graph. In particular, we have studied the changing and unchanging behavior of the graphs.
An Integer dominating function on a graph G is a function f : V (G) → W such that for every vertex
 v ∈ V (G), . For any function f : V (G) → W and any pair of adjacent vertices with f(v) = 0 and u > 0, the function  is defined by  (l) = 1,  (l) = f(u) − 1 and  (l) = f(l) if . A secure integer dominating function on a graph G is defined as an integer dominating function  which satisfies that for every vertex v with f(v) = 0, a neighbour u with f(u) > 0 such that is an integer dominating function. The weight of f is w(f) = . Minimum weight among all the secure integer dominating function on G is secure integer domination number on G. This paper is devoted to initiating the study of SIDF of a graph. In particular, we have studied the changing and unchanging behavior of the graphs.
 
Objectives: We propose a novel generalization of domination, which incorporates additional security and broader applicability. This refined framework offers new possibilities for research and practical implementation.
Objectives: We propose a novel generalization of domination, which incorporates additional security and broader applicability. This refined framework offers new possibilities for research and practical implementation.
A Generalized Study of Zero Divisor Graphs of Boolean Rings ℤ 2n = ℤ2 × ℤ 2 × · · · × ℤ 2
Authors: S. G. Jakkewad
Published: February 2025

The Zero-divisor graph of a commutative ring R is defined as a graph in which the vertices represent the non-Zero Zero divisors of R, and two vertices x and y are connected if and only if x× y = 0. In this study, we focus on examining the Zero divisor graphs of Boolean rings and deriving insights from their graphical representations.
Prioritizing Critical Success Factors for Biotech Start-Ups in India: Integrative Analysis and Strategic Implications
Authors: Avinash Kumar Vivekanand Mishra
Published: February 2025

Biotech start-ups in India face a complex landscape marked by rapid technological advancements, significant capital requirements, and stringent regulatory environments. Identifying and prioritizing critical success factors (CSFs) is essential for these ventures to strategically allocate resources, enhance their competitive positioning, and sustain long-term growth. This paper presents an integrative analysis to identify the most impactful CSFs for biotech start-ups in India. The research employs a mixed-methods approach that combines expert opinion, data analysis, and multi-criteria decision-making frameworks to rank and evaluate the importance of various CSFs. Key factors include innovation capabilities, access to funding, regulatory compliance, talent acquisition, and strategic partnerships. The study provides strategic implications and recommendations for start-up founders, investors, and policymakers to support decision-making processes and foster a thriving biotech ecosystem. The findings aim to serve as a guide for aligning strategic efforts with high-priority success areas, ensuring sustainable development and resilience in a competitive industry.
A Hybrid Model for Detecting Fake Profiles in Online Social Networks: Enhancing User Trust
Authors: Rohini Bhosale, Vanita Mane
Published: February 2025

The multiplication of fake profiles in online social systems (OSNs) has developed as a basic challenge, debilitating client believe and security. This paper presents a novel cross breed show planned to distinguish fake profiles in OSNs by combining progressed machine learning methods to improve discovery precision and keep up client believe. Our approach coordinating both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, known for their quality in preparing successive information, into a crossover show that leverages the qualities of each method for more compelling location. Employing a dataset collected from Twitter, we conducted a comparative examination of different machine learning models, counting Naïve Bayes, Irregular Woodland, AdaBoost, and Support Vector Machines (SVM), to assess their execution in identifying fake profiles. In expansion, repetitive neural systems, such as LSTM and GRU, were tried separately some time recently being combined into the proposed crossover demonstrate. Each model's execution was assessed based on accuracy, review, F1 score, and exactness. Our findings illustrate that the cross breed LSTM-GRU show outflanks conventional machine learning calculations and person repetitive models, accomplishing prevalent discovery exactness and decreasing untrue positives. By saddling the complementary qualities of LSTM's capacity to capture long-term conditions and GRU's computational proficiency, the half breed demonstrate offers an progressed arrangement to fake profile location. This inquire about gives a comprehensive system for distinguishing fake profiles in OSNs, pointing to move forward the keenness of client intuitive and upgrade believe in online stages. The proposed show has critical suggestions for OSN security, especially in moderating the rising risk of false accounts.
Exploring RNN Model Implementations for Predicting Sunflower Disease Outbreaks
Authors: Rupali Sarode, Arti Deshpande
Published: February 2025

The development of diseases in oilseed fields is influenced by meteorological conditions and the pathogen's predilection for susceptible hosts. This research examines forecasting models for two sunflower diseases: Alternaria leaf blight and powdery mildew. The disease percentage for the Alternaria leaf blight and powdery mildew is predicted for the Kharif and Rabi seasons, respectively. For prediction, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BILSTM), and Simple Recurrent Neural Network (SimpleRNN) models are employed. These models incorporated six parameters: precipitation, maximum temperature, minimum temperature, maximum and minimum relative humidity, and disease percentage. Meteorological data and disease percentages were obtained from India (Marathwada region). After the experimental study, the results indicate that the SimpleRNN model demonstrated superior performance for Alternaria Leaf Blight with a Mean Squared Error (MSE) value of 0.11%, while for Powdery Mildew, LSTM exhibited the best performance with an MSE value of 0.32%. Each model exhibits unique performance characteristics, and all models are evaluated using the same dataset. A non-parametric Friedman test is employed to statistically validate the differences in performance, followed by a Nemenyi Test as a post-hoc analysis. This approach enables a side-by-side comparison of the average performance across all models.
Cost-Effective Industrial IoT Model using Cloud Environment
Authors: Satyajeet Shinge
Published: February 2025

The integration of Industrial Internet of Things (IIoT) technologies into industrial processes has revolutionized manufacturing and production sectors by enabling real-time data collection, analysis, and decision-making. However, the scalability and cost considerations associated with cloud-based IIoT implementations remain significant challenges for industries seeking to maximize the benefits of this transformative technology. This paper presents a comprehensive analysis of a cost-effective cloud model designed specifically for Industrial IoT applications. This paper proposes an approach that optimizes resource utilization, reduces operational expenses, and maintains high-performance standards, ensuring the seamless functioning of IIoT systems within budget constraints. The study begins by examining the current state of IIoT cloud deployments, highlighting the prevalent cost-related challenges faced by industries. Subsequently, a cost-effective cloud model is introduced, which leverages advanced technologies such as containerization, edge computing, and server less computing to optimize resource allocation while minimizing infrastructure overheads. To evaluate the proposed model, a series of experiments are conducted in a simulated industrial environment, measuring performance metrics, scalability, and cost-effectiveness. The results demonstrate that the cost-effective cloud model significantly reduces both capital and operational expenditures, making IIoT implementations more accessible to a wider range of industries.
Dynamic Navigation using AI for Autonomous Vehicle Systems
Authors: Chaitali Chandankhede
Published: February 2025

The rapid advancement of autonomous driving technologies offers great potential for improving safety, efficiency, and accessibility in transportation. However, current systems face challenges such as poor real-time adaptability, limited obstacle detection capabilities, and difficulty recognizing lane markings, especially in dynamic environments. Additionally, the high hardware cost and lack of user-friendly interfaces impede the widespread adoption of these technologies, particularly in resource-constrained regions. Addressing these challenges requires innovative solutions that are both robust and cost-effective.
Our research introduces an AI-powered system that addresses these issues by providing a scalable and reasonably priced dynamic navigation solution. The framework integrates state-of-the-art computer vision techniques for real-time object recognition and lane detection, utilizing the Arduino and Raspberry Pi platforms. While YOLO (You Only Look Once) allows for quick and precise object recognition, the Canny algorithm guarantees accurate lane tracking. Effective obstacle avoidance and traffic signal recognition are made possible by the hybrid model. The system also has gesture recognition capabilities, which allow for natural user engagement without the need for physical interfaces. This invention improves adaptability and usability in a variety of settings and user needs. The system's excellent performance in lane tracking, object detection, and obstacle avoidance was proven by extensive testing in both simulated and real-world scenarios. Gesture recognition further enhanced the system's usability in challenging situations.
Enhanced Traceability and Transparency in Medical Supply Chain Management Using Blockchain-Based Customized Smart Contracts
Authors: Nitin Shivale
Published: February 2025

The medical supply chain faces challenges like counterfeit drugs, lack of transparency, and inefficient traceability. Blockchain technology, with its decentralized and secure architecture, offers a promising solution. By implementing customized smart contracts, stakeholders can ensure data integrity, improve trust, and streamline supply chain operations, enhancing overall efficiency and reliability. Counterfeit medicines and limited visibility across the medical supply chain pose significant risks to patient safety and public health. Traditional systems lack robust mechanisms to verify authenticity or ensure transparency. The need for a secure, efficient, and scalable system to trace and validate medicines in real-time has become crucial. A blockchain-based framework was developed using customized smart contracts for traceability. Each medicine is registered with unique identifiers stored on the blockchain. QR codes link physical products to the digital ledger, enabling real-time tracking and user verification. The system uses consensus mechanisms to validate transactions and ensure authenticity. Proof of Work (PoW) ensures robust security by requiring significant computational effort for transaction validation, deterring malicious activities. Proof of Stake (PoS) enhances scalability and energy efficiency, validating transactions based on a node's stake in the network. Together, these algorithms provide a balance of security, speed, and sustainability. The proposed system significantly improves traceability and transparency in the medical supply chain. Counterfeit detection rates increased, and transaction validation times decreased due to optimized algorithms. User feedback confirmed enhanced trust and reliability. The solution demonstrates scalability and adaptability for broader applications in healthcare and beyond.
Enhanced Reinforcement Algorithm for Topic Categorization Using Machine Learning Method
Authors: M. Karthica
Published: February 2025

Information seekers rely heavily on search engines to extract relevant information because of the Internet's exponential development in users and traffic. The availability of a vast amount of textual, audio, video, and other content has expanded search engines' duty. Users of the Internet can obtain pertinent information about their query from the search engine by using factors like content and link structure. It does not, however, imply that the information is accurate. The link structure of web sites is analyzed using Web Structure Mining (WSM), and their content is analyzed using Web Content Mining (WCM), which determines how well the ranking module performs.A multitude of content-based recommender systems are currently in use, and they are well-researched in both text acquisition and filtering. These systems recommend documents based on text analysis. The management information system can benefit from Web Content Ming technology. Web content mining is the process of extracting or mining knowledge or useful information from web pages. The purpose of this work is to investigate web content extraction technology enhanced Reinforcement algorithm which anticipates user interest by analyzing the page according to user view related topic.This ERIM procedure involves locating web sites linked to user queries and using hyperlinks to locate a collection of related web pages and find the topic categorization using machine learning method.
Enhancing Search Functionality-based Customer Analysis Expending Big Data in E-commerce
Authors: Pudi Sreelakshmi
Published: February 2025

Customer behavior analytics has been included in many systems, but the field is still young and undeveloped, with much room for improvement. Big data is one of the newest and most promising technological breakthroughs. It can fundamentally alter how businesses analyze and turn consumer behavior into insightful knowledge. It is possible to analyze data effectively even using decision trees. This research attempts to investigate the benefits of implementing BDA in e-commerce to vendors and customers based on the significance of big data analysis and its advantages to e-commerce operations. By enhancing client loyalty, BDA gives electronic sellers, or "E-vendors," the competitive edge they need to comprehend consumer behavior and boost revenue. Additionally, recommendation systems built on BDA personalize users' search and buying experiences. Nevertheless, there are drawbacks to using BDA in e-commerce, namely the development of a shopping addiction. In addition, orientations that leverage BDA and the application of quantum computing to address current e-commerce issues are investigated to improve the evidence for decision-making that may prove useful in subsequent studies.
PATIENT SATISFACTION OR HAPPINESS DIRECTLY IMPACTS THE HOSPITAL'S REPUTATION AND SUCCESS. 4. OBJECTIVES OF THE STUDY
Authors: Anu. A. Natraj
Published: February 2025

This study examines the training effectiveness of front office staff on service quality at super specialty hospitals with special reference to Bengaluru city, India. Front office staff plays a crucial role in shaping the patient's first impression and reflection of overall experience later. Effective training is essential to enhance their skills, knowledge, and attitudes towards delivering exceptional service quality. This research aims to assess the impact of training programs on the performance of front office staff, focusing on key service quality dimensions such as responsiveness, empathy, assurance, and reliability. The study utilizes exploratory approach, using quantitative surveys from 150 patients visiting super specialty hospital were selected conveniently in Bengaluru city. Findings indicate that well-structured training programs significantly improve service quality, leading to higher patient satisfaction and better hospital reputation. Recommendations for hospital administrators include investing in continuous training programs and adopting a patient-centric approach to service delivery.
Optimizing Decision-Making in Supply Chain Management Using Machine Learning and Mathematical Modeling Techniques
Authors: Kiran Kumar Reddy Penubaka
Published: February 2025

Supply Chain Management (SCM) is advancing through the incorporation of machine learning and mathematical modeling methods, improving decision-making effectiveness. The aim of this study is to improve the supply chain functions through four algorithms: Long Short Term Memory (LSTM), Support Vector Machine (SVM) Genetic Algorithm (GA), and Reinforcement Learning (RL). A result of the forecasting experimental data shows that LSTM achieved 94.3% accuracy forecast which exceeds SVM’s 87.8%. GA provides 23% improvement in savings with respect to conventional optimization techniques and RL brings a 15% increase in delivery efficiency through real-time decision making. Comparison of these techniques with existing methods reveals the greater efficiency of hybrid models that combine information from AI with more traditional means. In addition, the research evaluates how blockchain and digital twin technologies improve transparency, and security in supply chains. Despite these issues, the results show that supply chain efficiency can be markedly improved using AI powered decision making. As future studies, combined optimization frameworks and supply chain management ways that focus on sustainability should be investigated to have robust and environmentally friendly supply chains. By arguing that smart supply chain decision needs to be based on empirically validated theoretical assumptions, this research sheds important light on how scholars and industry experts can try to improve supply chain performance through smart decision making.
Analysis of Teaching Practices in Mathematics and Language Teachers of Public Institutions in Valledupar
Authors: Maria Nelly Rodriguez Estrada
Published: February 2025

This article underscores the importance of teaching practices in mathematics and language teachers as a fundamental tool that goes beyond writing simple numbers and formulas on a blackboard. Their objective is to analyze the teaching practices of mathematics and language teachers in Valledupar. When identifying the methodologies used by teachers in the teaching of mathematics and language in current education and when analyzing their effectiveness, it is observed that, in the classroom, a traditional approach that hinders students' deep and meaningful learning continues to predominate. Despite the fact that teachers are aware of innovative methodologies, there is resistance to change due to factors such as lack of resources, training and institutional support.
This qualitative, descriptive, non-experimental research is based on semi-structured interviews and observations; A sample of 10 students and 15 teachers from 5 public institutions of Valledupar - Cesar was taken, where the great knowledge of teachers about innovative tools that come before their usual pedagogical practices was evidenced, a continuous improvement in the teaching work is proposed, where active tools are involved with the intention of promoting skills and abilities in students,  that help them improve their quality of life. It is based on pedagogical theories such as the processes of mathematical thinking from the initial age of Margarita Marín, the significant learning of David Ausubel, the flipped classroom of Ana Elena Coto, the STEAM approach of Chris Beers and the logical, social and critical thinking of Clavijo and Fresneda, a call is made to educators to imagine the teaching of mathematics.
Exploring Highly Influential Features and Model Comparison for Predicting Coronary Heart Disease: A Novel Approach Using SHAP
Authors: S. Meenakshi
Published: February 2025

Forecasting coronary heart disease (CHD) is essential for improving healthcare outcomes and holds significant importance in identifying individuals at risk and mitigating severe health outcomes. Many prior studies have analyzed complete datasets without isolating the most influential features, which hinders the interpretability and efficacy of classification models. This research focus to address this gap by identifying key features that have been underexplored in past research on heart disease and evaluating multiple classification models based on these significant features. Using SHapley Additive exPlanations (SHAP), the most impactful features were extracted from a dataset comprising 1888 instances with 14 attributes and two outcome classes (Yes and No), indicating CHD risk. Key features such as 'thal,' 'chest pain type (cp),' The slope of the ST segment as shown during maximal exertion, ’(slope)', 'ca,' and 'oldpeak' were prioritized for further analysis. Classification models, such as XGBoost, Decision Tree, K-Nearest Neighbours (KNN), and Logistic Regression were then assessed using this reduced feature set. Among these, XGBoost achieved the highest performance, with an 90.21 percent accuracy, 90.05% precision, 90.53% recall, and 90.29% F1 score. KNN, with an accuracy of 89.68%, came in second while the Decision Tree also yielded strong results with 90.21% accuracy. By leveraging a focused subset of critical features, this study demonstrates how classification models can be improved to produce results that are easier to understand and more effective. These findings pave the way for the development of a prototype tool to aid in heart disease detection, providing value to both research and clinical applications in cardiovascular diagnostics.
Revolutionizing Educational IT: A DevOps-Enabled Cloud Framework for Next-Gen Learning
Authors: Bhushan Jadhav
Published: February 2025

In the current scenario, the reliance on information and communication technology (ICT) in education has a number of limitations. Historically, the education sector of managing was extremely labor-intensive and manual, thus creating many a problem regarding the use of educational solutions. The institutes are trying to broaden their scope of functioning with limited infrastructure to meet the demands of the future since education is gaining significant importance.
Emerging technologies such as DevOps and cloud computing can play a prominent role in lessening such adversities and serve the educational sector well. While DevOps refers to different technologies used to automate multiple applications and services-using multiple tools-for continuous integration, continuous testing, continuous deployment, and continuous monitoring, cloud computing is the supply of computing and storage by network-accessible hot-shared resources that match demand. A wide range of commercial cloud systems is extremely compatible with the technologies of DevOps.
This strategy would, therefore, resolve all issues presently encountered by the provision of new educational opportunities and benefits related to cost savings, ease of execution, automated processes, reduced resource usage, etc. The advantages in terms of the benefits for education from the integration of cloud and DevOps solutions are unexcelled in education. This research paper aims to develop a workable automated framework that relieves the education of time-consuming tasks. It discusses the implementation details of the proposed framework using the open-source private cloud platform OpenStack and DevOps tools such as Jenkins and Ansible. The performance characteristics of OpenStack and the DevOPs tools used are also discussed in this paper.
Ciberagresión y su relación con factores de contexto escolar y bienestar subjetivo
Authors: Vera Noriega José Ángel , Rodríguez Carvajal Claudia Karina , Bustamante Castro Ivett Alejandra , Flores Partida César Alan
Published: February 2025

Ante al aumento de los casos de ciberagresión y a la creciente exposición de los niños y adolescentes a las tecnologías de la información y la comunicación, dada la complejidad del fenómeno del ciberacoso, se hace énfasis en abordarlo con un enfoque ecológico, con base en la premisa de que la conducta de los individuos se debe a los factores que influyen en los diversos sistemas que los rodean, teniendo en cuenta constructos de un gran impacto en el contexto educativo como el clima escolar y, a nivel personal, el bienestar subjetivo. Por lo tanto, la presente investigación tiene como objetivo analizar la relación entre los datos personales, el bienestar subjetivo y el clima escolar percibido, junto con los distintos actores involucrados en la ciberagresión de estudiantes de primaria. Corresponde a una investigación de corte cuantitativo con diseño no experimental ex post facto, con un diseño transversal. Se obtuvo una muestra conformada por 1.505 estudiantes (796 = 52,9% mujeres y 709 = 47,1% hombres) correspondientes a 101 escuelas primarias públicas. Entre los resultados más relevantes está que existe una marcada tendencia a disminuir el valor de la media conforme avanza el grado de cada actor, viéndose reducidos los aspectos positivos de las dimensiones de clima escolar y bienestar subjetivo; por ello se destaca la importancia del estudio de factores relacionados con el contexto, como los personales, para obtener una comprensión más completa del fenómeno del ciberacoso.
CLIL: An International Overview of Practices, Challenges and Opportunities in Education
Authors: Hu Hengzhi
Published: February 2025

Book Review: International Perspectives on CLIL, edited by Chantal Hemmi, Darío Luis Banegas. Cham, Switzerland, Palgrave Macmillan, 2022, xxxviii + 300pp., ISBN 978-3-030-70097-3 (softcover).

Undeniably, Content and Language Integrated Learning (CLIL) as a pedagogical approach with a dual focus on content and language learning has manifested itself in a variety of contexts, forms and practices with immense rigour and sustainability in the past decades. As CLIL is gradually being accepted as an avenue to accommodate internationalisation at home, the book entitled International Perspectives on CLIL serves as an opportune presentation of CLIL theories and practices in multilingual and multicultural contexts. Mindful of the competing interpretations of CLIL, this book embraces the diversity of CLIL and foregrounds how it has grown and developed both theoretically and empirically.
Neurophysiology of ACL Injury.
Authors: Stańczak Mikołaj , Swinnen Bram , Kacprzak Bartłomiej , Pacek Artur , Surmacz Jakub
Published: February 2025

The neurophysiology of ACL injury extends beyond the mechanical rupture of the ligament to encompass profound alterations in the central and peripheral nervous systems, impacting sensorimotor integration and neuromuscular control. The ACL, densely populated with mechanoreceptors, plays a critical role in joint proprioception, dynamically regulating knee stability through complex neural circuits that connect to the spinal cord and brain. When disrupted by injury, these neural pathways contribute to delayed muscular activation, altered motor planning, and compromised joint stability. Such neuromechanical deficits increase the likelihood of reinjury and highlight the need for comprehensive neuroplastic rehabilitation. Neuroplastic therapy, employing tools like external focus strategies, stroboscopic glasses, smartboards, and virtual reality, aims to restore and enhance neural connectivity, sensory integration, and motor coordination. These advanced tools target distinct phases of motor learning, promoting automaticity and resilience in movement patterns. By integrating visual-cognitive, proprioceptive, and reflexive controls, this therapeutic approach not only accelerates recovery but also optimizes performance and reduces the risk of re-injury, representing a paradigm shift in ACL rehabilitation.
The therapeutic effect of ozone–enriched propolis oil extraction in cats with gingivitis
Authors: Ekren–Aşıcı Gamze Sevri , Kal Umut , Berberoğlu Seda , Kıral Funda
Published: February 2025

In cats, gingivitis treatment typically involves professional dental cleaning under anesthesia, pain management, and antibiotic administration to reduce inflammation. Considering the disadvantages of antibiotic administration, the necessity for alternative acute treatment protocols arises. It is believed that enhancing the antibacterial, anti–inflammatory, and local anesthetic effects of propolis with ozone could shorten the treatment duration, help reduce the risks associated with gingivitis, and also support the overall health of the cat. The study included 20 domestic cats diagnosed with the causative agent of gingivitis presented to private clinics. The cats included in the study were grouped based on bacterial agents (n=10; Female=4, Male=6) and viral agents (n=10; Female=5, Male=5). Propolis extracted with ozone–enriched oil was administered in spray form for 14 days (d). Before the application and on the 7 th and 14 th d of the treatment, the gingival indices of the cats were recorded, scored, saliva samples were collected, and photographs were taken. The levels of VEGF and TNF– α in saliva were determined using a cat-specific ELISA kit. When the data were evaluated, the application of ozone–enriched propolis demonstrated a statistically significant reducing effect on the levels of TNF–α in saliva in both groups (P<0.01). Salivary VEGF levels showed a significant increase during application, especially in gingivitis caused by bacterial agents (P<0.05). In the viral group, application was found to be more effective in increasing VEGF levels during the first 7 d. In gingivitis caused by bacterial agents, the gingival index (GI) and plaque index (PI) decreased compared to pre–treatment values (P<0.05). In gingivitis associated with viral agents, the decrease in the GI was statistically significant, while the decrease in the PI was found to be non– significant. In conclusion, this study demonstrated that the application of ozone–enriched propolis might be an alternative treatment option for cats with gingivitis.
VACUUM SYSTEMS SEALING PROBLEMS IN ACCELERATOR TECHNOLOGY
Authors: Davtyan A.H.
Published: February 2025

With the development of modern science and technology, the use and modernization of particle accelerators are becoming more relevant and promising. Research and experiments in the field of elementary particle physics are carried out on various types of accelerator equipment. The modernization is based on improving both the characteristics of the entire accelerator and some parameters of its specific unit or even a sub-unit.
As it is known, almost all accelerator components have flanges that are used in different places for their intended purpose. In particular, for connecting waveguides of different diameters, connecting any unit measuring devices, vacuum gauges, or direct closure of the unit. The degree of sealing of the flanges has a strong influence on the beam parameters, since the degree of vacuum has a great impact on the working parameters of the beam. Austenitic stainless steel, high-strength aluminum alloys and dispersion strengthened copper alloys are used as materials for flange connections. These materials have limited parameters in terms of strength characteristics. Repeated assembly and disassembly of such joints leads to subsequent deformation of the flanges. Experiments have shown that during the sealing of flanges, not only the gasket, but also the flanges are plastically deformed. This phenomenon limits the multiple usage of flanges.
The work studies the currently known standard and non-standard models of ultrahigh vacuum flanges, gaskets and methods of their sealing. As the main task, a comparative analysis of the design of flange assemblies is carried out.
THE MECHANICS OF TECHNOLOGICAL INHERITANCE AT THE STAGES OF THE LIFE CYCLE OF MACHINE PARTS: KEY SCIENTIFIC TASKS
Authors: Blumenstein V.Y. , Makhalov M.S. , Krechetov A.A.
Published: February 2025

Using the digital twins concept for product design and manufacturing technology can significantly reduce or even completely exclude the need to perform time-consuming and expensive full-scale testing. However, a critical condition for successful completion of this task is the strain accumulation and metal damage model adequacy.
Among the most adapted for solving such problems is the mechanics apparatus with a well-developed mathematical apparatus of deformable solids that allows to obtain adequate models of metal behavior under load. On its basis, the technological inheritance mechanics has been developed, within three life cycle stages are considered - cutting, surface plastic deformation and operational cyclic loading using a unified metal surface layer strain accumulation and plasticity reserve exhaustion methodology. At that, the metal plastic flow regularities in the deformation zone can be described in the loading programs form for any technological method accompanied by metal plastic flow.
The technological inheritance theory development directions are considered, including the range of surface plastic deformation complex-profile deforming tool creation. The reguliarity of using these tools is the creation of high hydrostatic pressure in the plastic deformation zone, which significantly increases the strengthening effect without destroying the metal of the surface of layer of the part.
An intensive surface plastic deformation method has been developed, which allows to control the loading program through the use of special profile shape deforming tools.
A conceptual model of transformation of residual stresses of the surface layer at the stages of production maintenance, as well as a model simplifying the processing by surface plastic deformation built on the basis of a through consideration of continually changing both by stages of production and maintenance and at each stage of the mechanical properties of the metal of the surface layer.
INCREASING THE EFFICIENCY OF TURNING AND BORING PROCESSES OF DIFFICULT-TO-MACHINE METALS WITH A CUTTER WITH A MULTI-STAGE BLADE
Authors: Chibukhchyan H.S. , Mnatsakanyan N.G.
Published: February 2025

In a severe market economy, the main task of manufacturers of modern technology is to fully improve the quality and reduce the cost of manufactured products, which is accompanied by an increase in the number of parts made from difficult-to-process materials, which are widely used in various branches of mechanical engineering: aircraft manufacturing, automobile manufacturing, shipbuilding and rocketry, etc. Mechanical processing of such metals by turning and boring is one of the most important industries for the manufacture of various machine parts from them and, undoubtedly, the main driving force behind the economic growth of production.
To solve these problems, a virtual laboratory has been developed in the LabView software environment to determine the total force of the circumferential component of the cutting force using multi-stage passing or boring cutters, which allows to determine the values of the total cutting forces during turning and boring and compare them with the cutting forces of the traditional turning or boring processes with a cutter.
The conducted studies have confirmed the results of well-known studies, according to which the use of a cutter with a multi-stage blade allows reducing the total cutting force by upto 2 times or more: It is confirmed that the use of a multi-stage cutter in the process of turning workpieces from the selected metals and alloys under study allows reducing the overall force load of the process of turning these materials upto 3 or more times:
The virtual laboratory developed in the LabView software environment, in comparison with real physical studies, allows one to obtain fairly close results in establishing the possibility of increasing the efficiency of the processes of turning and boring hard-to-cut metals with a cutter with a multi-stage blade.
Hristo Botev as a “Great Dead” according to “Misal” Literary Circle
Authors: Alexieva Anna
Published: February 2025

The paper is focused on how the image of the poet and revolutionary Hristo Botev was shaped according to the figures, belonging to the “Misal” (“Thought”) Literary Circle. There was no consent about how Botev should be thought. According to Pencho Slaveykov, he “could hardly be nominated as a good poet”. For Yavorov, Botev was “one of the gods, who ascended to the Earth”. Special attention was paid to the hierarchy of the “great dead people” (following the phrase by dr. Krastyo Krastev) and the place of Hristo Botev amongst them. Chronologically considered, in the first papers, written by Slaveykov and Krastev, published in the last decade of XIX c., the image of Botev was everything, but high ranked national hero. They put particular accent on his uncontrollability and anarchic attitude. He was also heavily criticized as a poet. The study states that all the variations of the way Botev was regarded by “Misal” figures could be explained by the memory about him, which was still personal experience based, communicative (Assmann), and its transformation into a cultural one, the foundation of the literary canon, was still to come. However, Botev was institutionalized as national hero in a relatively monolithic and positive narrative during the second decade of XX c., when communicative memory transformed into a cultural one. Therefore, in the late texts by dr. Krastev, Botev was nominated as genius, lifted considerably above all of his contemporaries.
Bulgarian Studies as One of the Slavic Studies at the Faculty of Arts of Comenius University in Bratislava
Authors: Mihaylova-Palanska Miglena , Stastna Natalia
Published: February 2025

The authors of the paper present the establishment and development of Slavic Studies and in particular Bulgarian Studies at the Faculty of Arts of Comenius University in Bratislava. Their history begins in 1921 with the Seminar of Slavic Philologies and continues in the Department of Slavic and Indo-European Studies, founded in 1964. The paper traces the traditions of the study of Bulgarian, Polish, Slovenian, Serbian and Croatian languages and cultures, as well as the opportunities for their study at the Department of Slavic Studies today. The paper focuses on the process of establishing Bulgarian Studies as a major and traces its current status – the study programmes, the compulsory and compulsory elective courses, the academic staff, etc. The article is dedicated to the 60th anniversary of the Department of Slavic Studies, the 100th anniversary of the birth of its founder Prof. Šimon Ondruš and the 100th anniversary of Polish Studies, which will be commemorated in the academic year 2024/2025.