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Numerical Evaluation of the Strength of Concrete Columns with Different Types of Confinement
The structural performance of reinforced concrete (RC) columns is significantly influenced by their cross-sectional shape and the confinement methods employed. Confinement is a widely adopted technique to enhance the load-carrying capacity and ductility of concrete columns, thereby improving their structural performance and seismic resistance. A large number of experimental and numerical studies are available to demonstrate the confinement effect of columns. This study presents a numerical evaluation of the strength of circular (316 mm diameter), square (280 280 mm), and rectangular (300 260 mm) RC columns confined by using various techniques. Modelling and analysis of columns are done in Ansys software. The confining strategies, adopted are varying the spacing of lateral ties, wrapping with different types of Fiber Reinforced Polymers (FRPs) and wrapping with FRP combined with lateral ties. The results show significant enhancement of compressive strength in the confined columns. Among the adopted confining strategies, CFRP with lateral ties gives maximum percentage strength enhancement (13.4%). The comprehensive understanding of the behaviour of the confined columns under axial load further leads to the study of confined columns under lateral and dynamic loads. This would contribute the safety and resilience of structures constructed specially in earthquake-prone regions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Digital Competencies in the Structure of Training of Advertising and Public Relations Specialists
The article examines the problem of the formation of digital competencies in the training of specialists in the field of media communications. An extensive review of the literature on the issue under study is given. The article examines the trends in the transformation of the profession of a communicator in the context of the intensive development of the latest technologies in the field of big data, gamification, artificial intelligence, etc. The article presents the research results on the basis of which an innovative educational program for training specialists in the field of advertising and public relations has been developed. The program includes modules related to general digital literacy, artificial intelligence resources, management in the digital environment, Internet marketing, technologies for working on various media platforms, as well as project activities using digital technologies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Digital and Artificial Intelligence in Education
The article examines the relationship between digital intelligence and artificial intelligence, with an emphasis on the pedagogical aspects of this problem. The author emphasizes that the creation of AI requires not only technical literacy, but also developed digital thinking, supported by clear ethical principles. Particular attention is paid to the role of modern education in the formation of such specialists - the school should educate not just IT specialists, but people who are able to responsibly use technology. The work analyzes the various levels of digital competencies required in professional activities, and also provides an overview of scientific publications on the implementation of artificial intelligence in the educational process. The study demonstrates how important it is to combine technological progress with humanistic values in the training of future personnel. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research on Effectiveness and Assistants in the Educational Process for Student Learning
The article discusses the topical issue of the use of AI assistants in the educational process of universities, which is becoming especially important in the context of the rapid development of digital technologies. Artificial intelligence, as one of the key tools of our time, is being actively introduced into the educational sphere, allowing us to optimize routine tasks and increase the effectiveness of the educational process. AI assistants are able not only to automate tasks such as checking assignments, drawing up curricula and analyzing student performance, but also to provide personalized recommendations for students, which significantly improves the quality of learning and creates an immersive environment for students. This article examines the role of AI assistants as a tool to support teachers in the educational process of universities. The main advantages of using them are considered, including improving the effectiveness of teachers, reducing the burden on teaching staff and the possibility of a more flexible approach to student learning. Attention is paid to the difficulties and limitations associated with the introduction of AI technologies, such as the need to adapt existing educational programs and the ethical aspects of using artificial intelligence. The purpose of the study is to analyze the effectiveness of using AI assistants in various educational environments, as well as to identify key factors for the successful integration of these technologies into the educational process. Based on the analysis, recommendations are offered for teachers on the optimal use of AI assistants, taking into account the specifics of educational tasks. The article will be useful both for researchers in the field of educational technologies using IT tools, and for practitioners interested in optimizing and improving the quality of the educational process. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research of Prospects and Challenges in Artificial Intelligence Technology Teaching and Learning
Recently, knowledge in the field of artificial intelligence in order to modernize various aspects of human activity has played a significant role. The exploration of the opportunities and difficulties associated with the development of artificial intelligence technologies is becoming an important area of research, as it profoundly affects our perception of work, education, medicine and other spheres of existence. New methods of machine learning, deep learning and reinforcement learning are being developed. These technologies are changing our understanding of how machines can learn and adapt to the world around them. The application of artificial intelligence covers many areas, including healthcare, finance, education and industry. In medicine, for example, AI can improve diagnostic accuracy and develop customized treatments. In education, it is possible to create personalized learning plans for each student. While in industry, artificial intelligence technologies are able to optimize production processes and increase business efficiency. However, despite the potential benefits associated with learning artificial intelligence technologies, there are serious challenges that require careful analysis. These challenges include ethical dilemmas, such as issues of algorithm transparency and responsibility for making principled decisions. Data security and privacy are also among the key aspects that require innovative approaches to AI technology training. The main purpose of the research is to deeply analyze the prospects and challenges in the field of artificial intelligence technology training, provide a comprehensive understanding of the current state of this field, identify key areas of development and propose practical strategies for effectively overcoming challenges. Taking into account both positive and negative aspects, it is necessary to have a meaningful look at the future of artificial intelligence technology education, taking into account social, ethical and technical aspects. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Educational Perspectives of the Metaverse
The article presents a comprehensive review of the transformational potential of metaverses in the field of modern education. A multidimensional analysis of this technology is carried out, considering it not only as a technical innovation, but also as a fundamentally new educational paradigm. The paper explores in detail the unique capabilities of metaverses for creating immersive learning environments where traditional educational formats are combined with innovative approaches such as gamification and project activities in virtual space. The article pays special attention to the practical aspects of the use of metaverses in the educational process. Methodologically, the article identifies three key areas for the successful realization of the educational potential of the metaverses: technical (providing infrastructure and accessibility), organizational (developing a regulatory framework and standards) and pedagogical (creating effective didactic models). In conclusion, the article emphasizes that the full realization of the educational potential of the metaverse is possible only with a systematic approach that comprehensively takes into account technological, organizational and pedagogical aspects. Such a multi-level approach will make it possible to transform the metaverse from a promising, but still experimental technology into an effective educational tool of a new generation. Only in this case will the metaverses be able to become a catalyst for profound changes in the learning system, ensuring its adaptation to the requirements of the rapidly developing digital reality. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Implementation of AI-Assisted Tools in Foreign Language Training
The article discusses didactic issues of implementing of AI-assisted tools into foreign language training of linguistics students. The purpose of the study evaluate the potential and analyze the case of integrating an AI-assisted tool into a foreign language course. Methodologically the study rests on two didactic approaches, namely a competence-based approach and a personal activity approach. The analysis of the functional scope of AI-assisted tools used for academic linguistic and practical linguistic purposes is carried out. A brief comparative analysis of the available options with an account of essential functional characteristics is given. Based on the needs-analysis the choice of a particular platforms for the training process is reasoned. A set of teaching principles was observed to master the target skills in the experiment. To estimate the efficiency of the training process and development of the target skills, a questionnaire was compiled and offered to students. The analysis of students performance as regards the focus communicative interaction skills was carried out. Challenges in combining traditional and AI-assisted tools in the educational process are analyzed; the ways to overcome them are recommended. The study suggests that the use of AI-assisted tools in foreign language training of linguistics students can be based on a combined approach aimed at improving the quality of individual work and further consolidation of target skills in contact time. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Development of an Adaptive Mathematical Education System for Middle Grades Using Machine Learning
The aim of the study is to improve the study of mathematics topics for middle school children by developing a software implementation of an adaptive educational system using machine learning. During the research, the topic of quadratic equations was chosen as the basis for the research and development of an adaptive system. During the testing of the adaptive system, mistakes were specifically made to simulate the consolidation of knowledge during the educational process and make sure that it works and is able to adapt to the individual level of each student, increasing the level of knowledge gained and contributing to the consolidation of the material. To achieve this goal, Python code was developed in the Jupyter Notebook development environment. Python libraries were also used, in particular the scikit-learn library for implementing machine learning. The presented approach and software implementation can be used both by teachers to check students and track their progress, and by students themselves to assimilate and consolidate the material and knowledge gained in the lessons. The results obtained during the study demonstrate an increase in the effectiveness of adaptive learning methods using machine learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Exploring Social Network Usage and Well-Being Among College Students
Social media plays a significant role in bringing the world together with just a click. However, social media addiction is rapidly increasing, especially among the youth, which affects their mental health and well-being. Instagram is one of the social media sites that is used mainly by students. Due to increasing exposure to Instagram, including the short videos, called Reels, today's students consume the content at an alarming rate, thus becoming addicted to it. College students tend to relentlessly use Instagram, without considering the time and energy wastage or addressing its impact on their mental health and well-being. The present study aimed to (1) explore the relationship between Instagram usage and mental well-being among college students in India, and (2) examine whether Instagram usage serves as a meaningful predictor of students mental well-being. The findings reveal a significant negative correlation (r=?0.332, p<.001) between Instagram usage and students well-being, indicating that higher Instagram use is associated with lower levels of mental well-being among college students. Further, a simple linear regression analysis confirmed that Instagram usage is a significant predictor of well-being (F(1, 154)=19.099, p<.001), explaining a meaningful portion of the variance. The results highlight the need for educators, parents and other stakeholders to recognise the influence of social media, especially Instagram, on students mental health and well-being and develop strategies to promote healthier usage patterns. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Rising from Covid-19: Hybrid Teaching Experiences of University Teachers
COVID-19 pandemic has affected higher education all over the globe. The unprecedented situation has brought changes in teaching at universities. Universities across the world decided to teach online. By the time, teachers hone online teaching Skills Universities resorted to hybrid teaching. Teachers are oblivious to hybrid teaching and had situational anxiety. In spite of all teachers started their hybrid classes. The present study assumes transtheoretical model for hybrid learning derived from ecosystem theory. Recent research is yet to capture the hybrid teaching experiences of university teachers amid COVID-19. The present study employed qualitative research method to capture in-depth understanding of hybrid teaching experiences. Study interviewed eight University teachers to understand the hybrid teaching experiences and applied interpretative phenomenological analysis (IPA) to interpret the interview data. The findings of the study emerged with themes and sub-themes describing hybrid-teaching experiences and listed major challenges faced by them. Few of the challenges are handling online and offline students group simultaneously, technology integration to classes, technological barriers, and COVID-19 anxiety. Study recommends more research in the area for broader understanding of nuances of hybrid teaching. Nevertheless, to find solution to the challenges faced by the teachers while conducting hybrid classes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Review of Methods for Using Virtual Museums in Literature Lessons
The article is devoted to the study of the possibilities of using virtual museums in school teaching of literature as an innovative tool capable of enriching traditional approaches to the study of literary texts. The article considers methodological aspects of integrating digital museum resources into the educational process, including various forms of work: virtual excursions, thematic quests, project assignments and comparative analysis of museum exhibits. Particular attention is paid to the didactic potential of virtual museums in forming students holistic understanding of the cultural and historical context of literary works. Both the advantages of this approach and the existing limitations are analyzed. The article presents a classification of methods for working with virtual museums in chronological perspective, reflecting the evolution from simple excursion forms to complex interactive models. A conclusion is made about the need for a balanced combination of the technological capabilities of virtual museums with the depth of literary analysis. The material of the article is of practical interest to literature teachers, methodologists and developers of educational digital resources, offering specific recommendations for the effective use of virtual museums in school practice of teaching literature. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Machine Learning Approaches for Detection of Cyberbullying in Virtual Space
Cyberbullying, hostile behavior of a group or an individual to defame or harass the victim mentally with the help of social media and other e-communication platforms, has the potential to create a lifelong negative impact on mental health with the power of inducing suicidal thoughts. It is on the rise among the early adolescents of the age group from 8 to 16. Hence it is vital to detect Cyberbullying at an early stage to safeguard the victims at the high risk of developing depression, anxiety, and suicidal ideas. It also helps to mitigate psychological, academic, and social consequences. Existing cyberbullying detection approaches primarily depend on static monolingual questionnaires and are not personalised. With the developments in Artificial Intelligence, many neural network-based approaches are explored to detect cyberbullying. This study discusses and provides comparative analysis of various machine learning approaches for detecting cyberbullying victimization among school students highlighting their effectiveness and limitations. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Robust Model for Quantum-Resistant Cryptography to Tackle Quantum Risks
As quantum computing advances, conventional cryptographic algorithms face developing threats, necessitating the improvement of quantum-resistant security mechanisms. Winternitz One-Time Signature (WOTS) is a promising cryptographic scheme that offers robust resistance in competition to quantum attacks. This paper explores the software of WOTS in enhancing the protection of digital communications and information integrity in a quantum computing generation. By manner of analysing the fundamental standards, sensible implementations, and ability demanding situations of WOTS, this research dreams to provide insights into its effectiveness as a quantum-resistant protection solution. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Workplace Stress Prediction Using Explainable AI: A Non-intrusive Approach with Psychosocial and Demographic Features
This study investigates non-intrusive approaches to workplace stress detection using machine learning/deep learning techniques. Current methods rely heavily on physiological measurements (ECG, EDA, PPG) or behavioral monitoring which face implementation challenges in corporate environments. We propose an alternative approach utilizing psychosocial stressors and demographic variables to develop an explainable AI model for occupational stress detection. This approach addresses the practical limitations of current methods, which fail to discover underlying contributors to employee stress in the workplace. Two datasets were evaluated: The corporate stress dataset (CSDIW) with 50,000 records across 30 features and the HR Analytics Job Prediction dataset (HAJP) with 15,000 records across 10 features. Machine learning models including Logistic Regression, Nae Bayes, Neural Networks, Autoencoders?+?XGBoost and ensemble methods (SVM, RF and XGBoost) were implemented in different feature spaces. Results indicate improved performance on HAJP dataset with and without feature engineering whereas models consistently underperformed on the CSDIW dataset. The soft voting-based ensemble classifier turned out the best performer achieving an accuracy of 0.86 and an f1 score of 0.84. Our findings suggest that while psychosocial features hold promise for non-intrusive stress detection in workplace, data quality and appropriate labelling remain critical challenges. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
FortGen IDS: The Fusion of SOAR and Hybrid IDS for Enterprise
In this data era, enterprise are encountering rise of challenges in detecting and responding to cyberattacks. There is a need for a sophisticated cyber approach that leverages cutting-edge technologies to fortify against the unexpected attacks. This paper presents FortGen IDS, a novel cybersecurity solution combining Security Orchestration, Automation, and Response (SOAR) with Hybrid Intrusion Detection Systems (IDS). The primary contribution of FortGen IDS is its innovative algorithm inspired by Genghis Khans military tactics, enhancing threat detection and response, particularly against Distributed Denial-of-Service (DDoS) attacks. The proposed model leverages advanced automation and orchestration capabilities to provide a more holistic approach to enterprise cybersecurity. Empirical validation studies have been carried out to determine the best algorithm for anomaly detection, also explicitly comparing the performance of FortGen and Hybrid IDS. It helps make businesses digital defenses stronger against evolving cyber threats. This approach has greater scope in improving cyber-defense in the context of enterprise security, ensuring that firms are well-fortified against potential cyber threats. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
HashPress: Building a Green Data Center for Tomorrow
Green data centers are a game-changer for the environment, offering substantial benefits over traditional data centers. They prioritize reducing carbon emissions, optimizing energy use, and enhancing overall energy efficiency. One of the key strategies involves using deduplication and compression technologies, which can significantly reduce the environmental footprint of data centers. By decoupling data-set sizes for compression and deduplication, these centers can optimize each process individually, ensuring that neither technique is compromised and both are fully leveraged for maximum efficiency. In this paper, the HashPress algorithm is introduced, which aims to further enhance the green credentials of data centers. This algorithm proposes innovative measures to optimize data handling processes, making them environment friendly. The empirical validation is conducted to support its efficacy, by improving storage efficiency and reducing energy consumption. The discussion highlights its potential in supporting the broader goal of establishing a green technology infrastructure. Through these advancements, green data centers not only decrease their environmental impact but also pave the way for a resource-efficient future in the technology industry. This paper also delves into the explosive growth of the green data center, importance of decarbonization and sustainability in reducing the environmental footprint and the current strategies and solutions being deployed by leading companies to address the challenge. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Machine Learning Based Parking Space Classification Using R-CNN and Faster R-CNN FPN Architecture
This research work aims to create an accurate and economical model for classifying parking space using deep learning techniques. Using current advances in deep learning and computer vision, the proposed model solves urban mobility difficulties, particularly parking management. To address parking space occupancy classification, the research work suggests using two proven deep learning architectures, R-CNN (Region-based Convolutional Neural Networks) and Faster R-CNN FPN (Feature Pyramid Network), as well as insights from previous research. The proposed models take advantage of the R-CNN and Faster R-CNN FPN architectures. This solution uses binary classifiers, such as ResNet50, to assess image patches representing individual parking spaces and offer precise occupancy values. Furthermore, this research investigates the Faster R-CNN FPN architecture, which uses a feature pyramid network to record hierarchical information and reason about complex spatial configurations in parking lots. The proposed models stand out for their ability to use high-resolution photos from real-world parking lots, allowing them to learn discriminative features automatically from raw image data. This differs from traditional methods that rely on handcrafted features, allowing the models to manage a wide range of parking lot circumstances, including changes in weather, illumination, and occlusions caused by surrounding vehicles or barriers. This research intends demonstrate the improved performance and scalability of deep learning models for parking space occupancy classification by conducting extensive testing. Here the implementation method focuses on systematic data collection, annotation, preprocessing, and model training to create machine learning models that can reliably categorize parking spot occupancy, allowing for successful parking management solutions in real-world scenarios. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Fibonacci Based Security Algorithm for ECG Signal
The rising dependence on telemedicine and wearable well-being devices has made the safe transmission of sensitive biomedical information, like Electrocardiogram (ECG) signals, a critical concern. In this paper, we propose a Fibonacci-based security calculation to guarantee the secrecy and integrity of ECG signals during transmission. The proposed algorithm leverages the unique properties of the Fibonacci succession to make a lightweight and effective encryption instrument custom-made to the continuous prerequisites of ECG information. By coordinating Fibonacci changes with conventional cryptographic strategies, the proposed strategy accomplishes upgraded security with insignificant computational above, making it reasonable for asset-obliged devices. The algorithm is evaluated regarding encryption strength, computational proficiency, and capacity to endure cryptographic attacks. Trial results exhibit that the Fibonacci-based approach gets ECG signals and preserves the nature of the first clinical information, guaranteeing both security and exactness in remote health monitoring systems with the Signal to Noise greater than 50dB. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
More Than Skills: How Digital Competence and Partner Attitudes Shape Financial Resilience in Couples
In the rapidly digitizing financial landscape, the ability to effectively use digital tools has become essential for financial well-being. This study examines the impact of digital competence on financial resilience within dual-income married couples, adopting a dyadic perspective. Drawing on the Actor-Partner Interdependence Moderation Model(APIMoM), it studies both actor and partner effects of digital competence. As well as the moderating role of each partners attitude toward FinTech. Data was collected from 107 (214 individuals) working couples in Gurgaon, India. Covariance-Based Structural Equation Modeling using SmartPLS revealed that digital competence significantly influences both individuals and their partners financial resilience. Moreover, attitudes toward FinTech were found to moderate these relationships, strengthening the positive effects of digital competence. Notably, the husbands attitude had a stronger moderating impact on the wifes resilience than vice versa, indicating potential gender-based dynamics. The study marks the importance of addressing both digital skills and relational attributes in aiding household financial resilience. Practical implications suggest that digital literacy programs should consider couple-based interventions that target both digital competence and attitude change. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Sensor-Integrated Smart Pump forDeep Vein Thrombosis Prevention
Deep Vein Thrombosis (DVT) is the formation of blood clots in the lower limb because of prolonged immobility. Such critical medical conditions can be avoided by regularly using compression cuffs on the limbs; however, traditional compression devices lack adaptability, cause patient discomfort, and have inconsistent pressure application. This study presents a smart wearable and portable compression system integrated with sensors to receive real-time feedback. A DC motor, controlled by an H-bridge converter, inflates the systems inflatable sleeves. The DC motor speed controls the pumping pressure, and a solenoid valve controls the inflation rate. Pressure, temperature, and moisture sensors are embedded in the inner part of the cuff to monitor the physiological parameters. An Arduino-based control system was used to control the inflation rate, air pressure, and duration of compression, optimally ensuring patient comfort. The designed pump was tested and shown adaptability when the sensor data changes. An AI-based control framework is also proposed in this work to enhance the performance and to make the pump autonomous and user-friendly. The response of the proposed AI-based control was validated through simulations of the model developed from fundamentals. The simulation results suggest that the AI-based DVT pump is more adaptable to the physiological parameter variations, even when the parameters change rapidly. The AI-driven model provides faster and more precise control of inflation and deflation patterns, preventing overheating, over-compression, and sweating. This study highlights the feasibility of a smart, wearable DVT pump that can adapt to the compression requirements while ensuring safety and comfort. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.