Vol. 11, No. 07 [July 2025]

Machine tool manufacturers are working hard to keep their competitive advantage in the current unstable economy and global marketing environment by creating creative designs that meet the demands of modern production, such as high precision, accuracy, and dependability. Therefore, the construction of machine tool structures is crucial since they have a direct impact on the factors that determine a machine's capability, motion accuracy, productivity, and machining quality. By building the structure with the ideal amount of static and dynamic stiffness, this can be accomplished. Nowadays, when developing machine structural components, the goal is to minimize the weight of the machine structure while maintaining the appropriate threshold values for both static and dynamic mechanical qualities. Finite Element Analysis is a technique used to do this.
In the present study structural analysis of the Double Column Vertical Machining Centre is carried out using Cosmo works software. The analysis involves static deformations and strain in structure that is bearing the static forces and design solutions are proposed to optimize the machine structure with maximum deformation in order to have optimum static stiffness, so that balance between productivity and accuracy is achieved. Stiffness of the machine structure for static loads is evaluated by modelling the columns, cross-beam, and cross-slide and ram assembly. Percentage contribution of all the structural members towards the overall deflection at the tool tip is determined. Ram is found to be the important member contributing to the overall deflection at tool tip.
Hence detailed analysis of the ram carried out. Various design alternatives are studied to optimize the ram with the objective of increasing its stiffness with minimum weight addition or maintaining same weight w.r.t existing ram and decreasing the tool point deflection which in turn increases the stiffness of machine.
Keywords: VMC, steel, Ductile cast-iron, Cosmos, software and optimization

The study analyzes the characteristics of selecting data storage infrastructure for individual stages of the AI machine learning pipeline. The necessity of this analysis is driven by the intensive accumulation of data and the increasing complexity of artificial intelligence (AI) models, which impose fundamentally new requirements on storage systems; without proper adaptation, a critical bottleneck emerges: costly compute resources remain idle, time-to-market for ML solutions is extended, and the total cost of ownership of the infrastructure grows. The research aims to analyze the features inherent in the process of selecting infrastructure for data storage at the individual stages of the AI machine learning pipeline. As a methodological basis, a comprehensive analysis of scientific publications in the relevant field was employed. The study's result is a unified decision-making model that maps workload profiles to specific storage classes and configurations, optimizing the choice among object, block, and file systems. Key evaluation criteria include performance, manageability, compatibility with GPU accelerators, fault tolerance, and cost efficiency. The presented materials will be of interest to infrastructure engineers, MLOps specialists, and AI researchers seeking to improve efficiency and reduce costs across the entire model lifecycle.
Keywords: storage infrastructure, machine learning, ML pipeline, AI storage, storage performance, IOPS, AWS EBS, data preparation, model training, inference.

In this article, we will explore the Adomian Decomposition Method, its theoretical foundation, and the computation of Adomian polynomials. Furthermore, we will investigate the application of the ZJ transform in conjunction with MDA for the solution of first- and second-order nonlinear ODEs. Through illustrative examples, we will analyze the effectiveness and potential of this hybrid approach for obtaining approximate analytical solutions to relevant nonlinear problems.
Keywords: ZJ transform, Laplace transform, Adomian decomposition method, Adomianpolynomials, Nonlinear differential equations, Series

Cloud-based AI workloads are increasingly exposed to adversarial threats due to their distributed, large-scale, and shared-resource nature. This paper presents a comprehensive overview of vulnerabilities in AI models deployed in the cloud and surveys the existing defense mechanisms. We classify attacks into evasion, poisoning, and inference threats and explore cloud-specific implications. Furthermore, the paper proposes a hybrid security framework that combines adversarial training, explainable AI, secure model serving, and zero-trust access control to improve robustness against malicious behaviors. Experimental validation is proposed as future work to evaluate the effectiveness of these approaches in real-world cloud environments.
Keywords: Cloud AI, Adversarial Attacks, Security, Explainable AI, Adversarial Training, Model Poisoning, Cloud Security

Artificial Intelligence (AI) has emerged as a transformative force across numerous industries, and the field of education is no exception. This research paper explores the applications, benefits, and challenges of AI in teaching and learning environments. It discusses AI-powered tools like intelligent tutoring systems, automated grading, personalized learning platforms, and AI-driven analytics. The study further evaluates how AI supports both educators and learners in enhancing educational experiences while addressing ethical considerations and potential limitations.

Voice-of-Customer (VoC) analysis plays a crucial role in understanding customer expectations, preferences, and pain points. Traditional manual methods for analyzing VoC data are time-consuming, error-prone, and limited in scalability. This research explores the use of Artificial Intelligence (AI), specifically large language models and natural language processing techniques, to automate the VoC analysis process. We present an AI-driven pipeline capable of extracting, clustering, and interpreting customer feedback from various sources. The paper demonstrates how automation enhances accuracy, reduces manual workload, and enables real-time decision-making. This approach enables organizations to systematically scale their customer experience strategies using AI insights.
Keywords: Voice-of-Customer, AI, NLP, Automation, Customer Feedback, Sentiment Analysis, Machine Learning

Artificial Intelligence (AI) and Data Science are two synergistic fields reshaping the landscape of modern computation and analytics. This paper explores the convergence of AI and Data Science, highlighting their integrated applications across industries. It discusses key methodologies, frameworks, and real-world use cases where AI augments the predictive power of data science workflows. The study also identifies current challenges and future directions in developing intelligent, data-driven systems

Machine Learning (ML) has emerged as a transformative force in modern computing, enabling systems to learn from data and make predictions or decisions without being explicitly programmed. This paper explores the fundamental techniques of machine learning, including supervised, unsupervised, and reinforcement learning, and their real-world applications across various domains such as healthcare, finance, transportation, and cybersecurity. We also examine the challenges associated with ML, including data privacy, model interpretability, and algorithmic bias. Finally, we discuss future directions and research opportunities that aim to make ML more robust, ethical, and accessible.
Keywords: Machine Learning, Supervised Learning, Unsupervised Learning, Deep Learning, Artificial Intelligence, Data Science

The hybrid workplace model has turned into a disruptive factor in the IT field that shifts the cultural peculiarities and organizational patterns. The study explores the impact of cultural changes on the organizational growth in IT businesses and especially the cultural changes related to autonomy, collaboration, engagement, driven leadership, and organization alignment. The underlying cultural factors affecting the hybrid work environment were determined by the exploration factor analysis (EFA) based on survey-derived data of IT workers with a structured survey. Various Linear Regression (MLR) was then done on the components retrieved and the extent to which they were predictive of organizational growth was established. Results indicate that engagement and driven leadership were considered as the most relevant factors in developing hybrid culture by high performance and second to them is organizational alignment and autonomy. Collaboration should not be overlooked too, though its influence appears to be moderate. The report claims that empowering the staff members together with the arrangement of the leadership styles and the reaffirmation of the company values might all lead to growth in the rapidly developing digital workplace. Such observations can give HR directors and those who develop legislation in the IT sector an effective strategy of transitioning into establishing hybrid models that can lead to the long-term development of the organization, as well as job satisfaction in employees.
Keywords: Autonomy, Collaboration, Engagement Driven-based, Leadership and alignment of the Organization.

The study focuses on conducting an analysis of the regulatory mandates of the National Institute of Standards and Technology of the United States (NIST) as an element in constructing a comprehensive national cybersecurity system. The objective of the article is to systematize and evaluate the effectiveness of implementing the NIST Cybersecurity Framework (CSF) for the protection of strategically significant sectors: healthcare, energy, and transport complex.
The methodological basis consisted of a retrospective system-synthetic analysis of current scientific publications for 2021–2025, relevant regulatory acts, and statistical reports reflecting practices of NIST standards implementation.
As a result of the study, the main advantages and bottlenecks in adapting NIST CSF in various sectors were identified, and recommendations for further improvement of the framework model were formulated.
In particular, with regard to the protection of electronic medical records (EHR), it is proposed to implement a multi-level approach combining AES-256 encryption, role-based access control (RBAC), and continuous monitoring for strict compliance with HIPAA requirements.
The scientific novelty lies in reconceiving NIST CSF not as a static set of prescriptions, but as a dynamic, adaptive system capable of integrating sectoral specificity and timely responding to new technological challenges.
The practical significance of the obtained conclusions is determined by their value for cybersecurity specialists, representatives of regulatory authorities, and leaders of critical infrastructure organizations responsible for developing strategic measures for the protection of information assets.
Keywords: cybersecurity, NIST, cybersecurity framework, critical infrastructure, national security, data protection, healthcare, energy, transport, risk management

Now a days, a wide range of solutions is available for monitoring and tracking human within a defined area, employing diverse technologies. Among these, solutions that utilize cameras or image sensors are predominant, owing to their straight forward integration, scalability, and ease of development, particularly with the remarkable advancements in Artificial Intelligence (AI) algorithms for visual data processing. However, these vision-based approaches pose inherent risks related to the theft or unauthorized disclosure of sensitive data. In this study, the design of a novel solution for effective intrusion detection and monitoring system using 24 GHz mm Wave radar technology is presented. The proposed system incorporates advanced edge-based signal processing and machine learning algorithms to achieve high accuracy while facilitating easy integration, scalability, and future development. This approach also ensures the information security and confidentiality of sensitive user data.
Keywords: mmWave, radar, HLK-LD2410, IoT, individual monitoring, edge processing.

Since the increased drug efficacy and lower toxicity in nano-mediateddrug delivery model have led to various applications in current science, the ecologically friendly synthesis regarding nanoparticles (NPs) by the green route from plant extracts has gained favor. In the presented work, we looked into the biosynthesis regarding goldoxide nanoparticles (NPs) from Au2O NPs green tea that are both stable and reasonably priced. The approach used for preparing Au NPs using green synthesis is demonstrated in this work to be straightforward and affordable. This was accomplished by combining an aqueous solution of goldnitrate with an olive extract, and visual spectroscopy as well as color change were employed to demonstrate the preparation ofAu2O NPs. Through using UV spectroscopy, the nature regarding the synthesized AgNPs was determined to be 2.5 mM. According to the research,aqueous extract is a good material for makingAu2O NPs, and by modifying the factors influencing this study, moreAu2ONPs in smaller sizes could be produced. After that, it was established that the extracted gold oxide had antibacterial characteristics, allowing it to be employed in a variety of pharmaceutical preparations for treating infections brought on by both negative and positive bacteria. As it wasdemonstrated to have a bactericidal effect on bacteria like the ones utilized in this research-namely, Staphylococcus aureus Staphylococcus pneumoniae, and Escherichi
Keywords: Au2O NPs, nanostructure, green tea, bacteria, XRD examination

The rapid advancement of digital technologies has transformed the landscape of education, with the metaverse emerging as a new and immersive learning platform. At the same time, gamification has proven to be a powerful pedagogical strategy for enhancing student engagement and motivation. The convergence of these two fields - educational virtual worlds and game-based learning - opens new avenues for experiential and interactive education. This study explores the integration of gamification into educational virtual environments in the metaverse, focusing on how game design elements, such as points, badges, levels, and collaborative missions, can be incorporated to support pedagogical goals. Building on the existing literature, this research proposes a methodology for designing immersive, game-enriched educational experiences. The study examines both the benefits and limitations of such applications, highlighting the potential of collaborative design as a means to enhance creativity, motivation, and deeper engagement.
Keywords: Education, Gamification, Metaverse, Minecraft, Virtual Worlds, Roblox

Posyandu (Integrated Health Post) serves as a crucial frontline service for maternal and child health in Indonesia, especially in rural and low-resource communities. However, the effectiveness of Posyandu is often hindered by its continued reliance on paper-based data collection and reporting systems. These manual methods lead to common challenges such as data inaccuracies, inefficiencies in service delivery, and delays in reporting that undermine timely health interventions for toddlers. To address these issues, this study adopts a Human-Centered Design (HCD) approach to develop a digital application specifically tailored to the workflows and needs of Posyandu cadres. The research engaged 15 active Posyandu cadres in a participatory design process, which included interviews, co-design workshops, and iterative prototype testing. The final application includes key features such as digital KartuMenujuSehat (KMS), automated growth chart visualization, offline data entry capability, and a user interface adapted to local culture and digital literacy levels. Usability testing using the System Usability Scale (SUS) revealed a mean score of 81.4, indicating a high level of user satisfaction and ease of use. The study highlights that involving end-users throughout the design cycle can overcome major barriers such as digital illiteracy and limited internet infrastructure. Moreover, it demonstrates that community-driven co-creation can result in practical, acceptable, and scalable digital health solutions. The findings underscore the potential of HCD-based approaches in supporting digital transformation of maternal and child health programs, particularly in underserved areas. This approach not only enhances the quality and accuracy of health data but also improves operational efficiency and sustainability in primary health services.
Keywords: Human-Centered Design. Digital Health Innovation, Child Nutrition Monitoring, Community Health System, Posyandu Application

The engineering disciplines, or careers rooted in the field of STEM, struggle with the gender gap because they are embedded in a sociocultural and historical context in which hierarchically organized masculinities are derived and reproduced within the structures of an unequal society. This paper aims to critically discuss why male engineering students often fail to recognize the dissimilar gender relationships in society and engineering. Based on qualitative research regarding the future time perspective and an intersectional gender analysis, preliminary results are presented, derived from a survey conducted with a sample of 40 undergraduate students from the Engineering Faculty of the National University of Jujuy (UNJu) in Argentina. The analysis reveals that male students are not widely aware of the history of gender relations, are unfamiliar with female scientists, and do not recognize the importance of gender equity. In conclusion, I suggest rethinking educational strategies and redefining masculinity, thereby allowing new models to emerge for a more inclusive environment and culture in engineering.
Keywords: Engineering students, future, gender gap, intersectionality, masculinities.

To ensure cybersecurity in all its complexity, the role of a legal framework setting out rights and obligations of responsible entities and providing for appropriate procedures and practices cannot be underestimated. It is increasingly frequent to refer to this area of legislation as the Cybersecurity Law. The paper opens with an overview of existing definition of cybersecurity and the role of its legal regulation. Then we focus to the role of the EU law in providing the legal framework of the cybersecurity and legal acts in this area represented by the EU secondary legislation. Attention is drawn on the two main acts in this area - the Cyber Security Act II (EU Regulation of 2019) and the Directive on measures for a high common level of cybersecurity across the Union (known as NIS2, 2022). Cybersecurity provides a perfect example of an area where corresponding legal rules need to be adopted bythe EU law and harmonized on the Member State state level.
Keywords: cybersecurity law; EU cybersecurity legislation; cybersecurity certification scheme; EU Cybersecurity Act

This study aims to analyze the adoption of Integrated Farming Systems (IFS) using the Technology–Organization–Environment (TOE) framework. IFS integrates agricultural, livestock, and aquaculture practices to support sustainability and improve household livelihoods. Using a quantitative approach, data were collected from farming households in Sumberagung Village, Sleman, Yogyakarta, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that the TOE model effectively explains 91.7% of the variance in IFS adoption. All three components—technology, organization, and environment—significantly influence the implementation of IFS, with technology having the strongest effect. These findings suggest that advancing agricultural technology, strengthening organizational capacity, and improving environmental support are essential for promoting sustainable farming practices.
Keywords: Integrated Farming System, Technology–Organization–Environment (TOE), sustainable agriculture, adoption model, PLS-SEM, rural development

This chapter looks at how combining quantum cryptography and artificial intelligence (AI) can help solve new cyber security problems caused by powerful quantum computers. It first explains the basics of quantum cryptography, focusing on Quantum Key Distribution (QKD), which uses the rules of quantum physics to create secure communication channels. The chapter also shows how AI can make these systems even stronger by handling large amounts of data, finding patterns, and helping security systems adapt to new threats. The research is based on two methods: direct data from organizations like NIST and a detailed review of existing studies. It also explains how quantum computing, using ideas like superposition and entanglement, can lead to new ways to protect data and detect cyber threats quickly. In the end, the chapter shows that using AI and quantum technologies together is a smart and necessary step to keep our digital world safe.
Keywords: Quantum Cryptography, Artificial Intelligence, Quantum Computing, Cybersecurity, QKD, Data Protection.

The implementation of CI/CD in the banking sector enables faster development, improves software quality, and enhances security. However, this process comes with challenges such as regulatory compliance requirements, integration with legacy systems, and ensuring data protection. This article examines the theoretical aspects of CI/CD, successful case studies of its application in the banking sector, and key risks associated with its adoption. Additionally, it proposes methods for overcoming challenges in the implementation of automated development and deployment processes in financial organizations.
Keywords: CI/CD, banking, automation, security, regulatory compliance, software.