Volume 7.
Issue 09.

The study analyzes the main principles and mechanisms of managing time resources and quality characteristics of comprehensive interior design projects — from the concept generation stage to the actual handover of the finished facility. The relevance of the topic is determined by the rapid expansion of the global design services market with a projected volume of 142.41 billion USD by 2025 and the accompanying complication of requirements for execution times and levels of execution accuracy. The aim of the work is to create and substantiate an integrated project management model that integrates advanced methodological approaches and digital technologies to optimize time and quality metrics. The methodological basis of the study relies on a systematic analysis of recent scientific publications, examination of practical cases, and processing of statistical data. Business process modeling was applied as a key tool, enabling the identification of bottlenecks and the development of mechanisms to overcome them. The resulting project management model proposes a combination of adaptive Agile techniques during the creative development stages, Lean manufacturing principles in organizational support and implementation, as well as end-to-end implementation of BIM technologies to ensure the integrity of the information flow and preventive quality control. The scientific novelty of the work lies in the formulation of a hybrid approach finely tuned to the characteristics of the full cycle of design projects. The practical significance of the results is manifested in the applicability of the proposed model by design studio managers, project leaders in the construction industry, and researchers in the fields of architecture and interior design.

Stents and implantable defibrillators are examples of cardiovascular equipment that keep people alive. The safety and dependability of these devices are very important. The Conventional Failure Mode and Effects Analysis (FMEA) methods are well recognized, and they tend to be subjective, reactive, and highly dependent on the past and human knowledge. This paper explores how the Failure Mode and Effects Analysis (FMEA) process of cardiovascular devices can be enhanced with the help of Artificial Intelligence (AI), namely, natural language processing (NLP), machine learning (ML), and predictive analytics. We suggest a modern FMEA framework that uses AI to accurately find possible failure modes and automatically update risk profiles using real-time data from clinical trials, manufacturing, and post-market surveillance. The objective is to demonstrate that AI-enhanced FMEA can transform device design and manufacturing into a more proactive, data-informed safety framework.

Electricity generation and distribution systems are classified as essential infrastructures where system reliability and operability depend on the state of their assets. Conventional maintenance policies such as corrective maintenance and time-based preventive maintenance can be inefficient and fail to detect latent failure symptoms, causing system failures, higher maintenance costs, shorter lifetimes of the equipment, and disruptions in system services. This work suggests a novel maintenance policy based on machine learning for predictive maintenance of electric power systems that will enable maintenance tasks to shift from a reactive approach to proactive decision making in order to avoid failures and plan maintenance tasks efficiently. The new system utilizes information about equipment operating conditions along with advanced features extraction and anomaly detection for accurate diagnosis of faults and health of the assets. The novelty of the new system is in integrating fault diagnosis and maintenance planning under a unified predictive maintenance framework.

The article examines the use of play-based dance methods in inclusive education for children with special educational needs. The relevance of the topic lies in the need for pedagogical tools that simultaneously ensure access to learning, stimulate cognitive and sensorimotor development, and create conditions for full social integration. Unlike traditional models, the proposed system centers on rhythm, movement, play, and visual supports, enabling children to adapt more rapidly to the classroom environment, reduce anxiety, and develop stable skills for collaborative interaction. The novelty of the study lies in the design of a comprehensive session structure comprising “entry — main phase — improvisation window — closure,” as well as the integration of a digital module and family involvement into a single educational process. The study explores cognitive, emotion-regulatory, sensorimotor, and social changes arising from regular practice. Particular attention is paid to mechanisms for transferring acquired skills from the play setting to academic tasks and home micro-rituals. The conclusion formulates the significance of the method for scaling and further adaptation in school and preschool settings. The materials may be useful to teachers, methodologists, and developers of inclusive educational programs oriented toward practice-based learning.

This article examines the concept of heart tranquility within the framework of neo-educational psychology and its integration into modern stress-therapy approaches. The study highlights that in contemporary psychological education, emotional and spiritual balance is considered a central component of personal development and pedagogical effectiveness. Heart tranquility is interpreted as a multidimensional state combining emotional regulation, cognitive harmony, and moral awareness, which fosters resilience and creativity in educational contexts. The article analyzes how neo-educational psychology synthesizes humanistic, cognitive-behavioral, and neuropsychological principles to develop methods that prevent emotional burnout and promote psychological well-being among educators and learners. Special attention is given to stress-therapy techniques–mindfulness, reflective dialogue, value-based motivation, and compassion training–which contribute to the restoration of inner peace and professional self-efficacy. The results underline that cultivating heart tranquility is not merely a therapeutic goal but an educational strategy that strengthens emotional intelligence, empathy, and sustainable mental health in the learning environment.

Scrum and Kanban have been the two traditional Agile practices which has been depicted as successful in enhancing software development processes, while each has its own drawbacks when implemented in software IT industry and put into practice individually. Scrum enforces sprint, roles, ceremonies, and load calculation which can occasionally result in overhead and rigidity. Although Kanban is flexible and having constant flow, it might not include task batching or structured prioritization. The research challenge of optimising the productivity of the software development lifecycle by integrating both of these strategies into a hybrid framework called the Scrumban. Methodologically, it synthesizes findings from industry case studies, scholarly literature, and real-world implementation addressing workflow visualization, work-in-progress (WIP) limits, sprint process and team role definitions. The study explores how Scrumban mitigates the drawbacks of its earlier models with increasing productivity, resource use, and deliver quality. Scrumban will serve as a hybrid framework to enhance the entire software development lifecycle, as evidenced by real-world use cases and KPIs such as execution time and defect reduction. Significant findings indicate that Scrum ban enhances adaptation in unpredictable circumstances, helps with continuous delivery, and supports cross-functional collaboration. Scrumban makes use of Kanban's flow productivity and Scrum's structured planning to minimize redundant work, fix bottlenecks, while enhancing transparency. Use cases from enterprise applications, promotional platforms, and incident reporting applications prove Scrumban’s scalability and savings potential. This research determines its findings that Scrum ban is suitable to organizations transitioning from traditional Agile approaches because it is a complete approach that supports both shifting business needs and developments in technology.

This paper discusses a possible path toward adaptive finetuning of large-scale language models over user signal continual learning. In our study, we are trying to organize an approach for explicit and implicit channels. Inside channel heterogeneous feedback filtering, interpreting, and integrating all of them into one regular tuning cycle that would help keep the model updated and qualitative in real-time usage. This paper validates these claims with studies of how fast static model parameterizations get outdated on one hand, and an observation limitation from classic offline process drops in answer accuracy and user trust on the other hand. This unification is novel because it unifies three classes of feedback into a multi-objective loss function with dynamic weights thereof; implemented through a microservice hierarchy architecture that logs, streams filtering, anonymizing, annotating data — then trains in several stages including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) plus contextual bandit plus rolling A/B test with confidence bounds. In fact, after going through several iterations of SFT and RLHF, it is the live model that steadily beats by a good margin all static baselines in terms of human preference. At the same time, the contextual bandit reduces average regret in online mode, and scaling to billions of queries is achieved without loss of metadata integrity or update flexibility. Key challenges are identified: catastrophic forgetting of rare skills, narrow-group preference bias, privacy risks when processing live data, and high manual annotation costs, for which regularization, stratified sampling, differential privacy, and active self-evaluation learning are proposed as solutions. This article should interest and benefit those who investigate and architect systems for natural language, machine learning, and recommendation engines.

Accurate classification of electroencephalogram (EEG) motor imagery tasks is critical for advancing brain–computer interface (BCI) applications. This paper proposes a streamlined phase-based approach to distinguish motor imagery tasks by extracting and leveraging phase information inherent in EEG signals. The method involves decomposing EEG data into relevant frequency bands, computing phase features using analytic signal techniques, and applying feature selection to enhance discriminative power. Experimental evaluation on benchmark motor imagery datasets demonstrates that the phase-based features significantly improve classification accuracy compared to traditional amplitude-based methods. The approach is computationally efficient, robust to noise, and adaptable to real-time BCI systems. These findings underscore the potential of phase information as a valuable modality for refining motor imagery recognition and optimizing user performance in neurotechnology applications.

With the increasingly evolving agile product development culture of today, the provision of compliance controls usually occurs in the form of an invitation to pace and imagination. This research paper examines a model of bridging cooperation between product teams and compliance officers in agile cultures. The major aim of this study is to identify the major success factors and major hurdles to seam-free integrated compliance and recommend a model embracing compliance culture with shared responsibility and sustained compliance. The study employs the mixed-methodology approach that entails quantitative data analysis of sprint-level data and qualitative analysis of team communication patterns. A six-month mixed-methods study across four agile teams in a financial technology company was conducted using sprint data, communication logs, and 24 interviews. Quantitative analysis revealed a 70% reduction in compliance issues (from 45 to 5 per quarter) and a 60% increase in sprint velocity (from 25 to 40 story points). Qualitative analysis showed improved trust, shared understanding, and proactive engagement when compliance was embedded early.  It is discovered in this study that early and regular engagement of compliance officers in the agile development cycle, and the use of automated compliance tools with a shared knowledge base, can preclude significant compliance lag and product quality at scale. The paper illustrates a new compliance integration model and best practices for enabling productive collaboration between product and compliance teams.

Many people have been helped by orthopedic implants in recovering their ability to walk and reducing their discomfort. Still, many times, revision surgeries are required when there are minor inconsistencies in the design of the parts. The main purpose of this paper is to discuss how SPC, when used properly in machining orthopedic implants, may greatly decrease issues with consistency and, as a result, the number of basic surgeries that need revision. The study makes use of practical information from a high-precision medical device manufacturing environment to explain how key control charts, Cp/Cpk figures, and similar tools improve product accuracy and make it simpler to predict outcomes. It appears from the findings that using SPC improves the results for patients, saves costs in the long run, and boosts the quality of treatment in orthopedics. This paper bridges the gap between statistical manufacturing theory and its practical application to human health by proposing a scalable model for quality improvement in the medical device industry.

This article examines the influence of depth of field on the perception of detail in product photography of jewelry. The relevance of the study stems from the fact that in macro photography of jewelry, microscopic elements of facet cutting and polishing determine the value of the piece, while a shallow depth of field simultaneously represents a risk and a creative tool, since even a minimal focusing error can conceal important details; at the same time, the choice of an optimal focus zone directly affects viewer trust and the commercial success of the images. This work aims to establish a quantitative relationship between the width of the focus zone and the subjectively perceived level of detail, to determine the threshold depth of field at which an observer ceases to notice micro‑surface defects, and to develop recommendations for optimal shooting settings for catalog, advertising, and artistic photography of jewelry. The novelty of the research lies in the synthesis of optical depth‑of‑field calculations with data from macro calculators and diffraction theory, in the integration of psychophysiological results from eye‑tracking and the flicker paradigm with behavioral analyses of consumer perception, and in the formulation of practical recommendations based on consensus among focus‑stacking, tilt‑shift, and lighting experts in jewellery photography. The main findings indicate that stopping down from f/4 to f/8 doubles depth of field, although further aperture reduction is limited by diffraction at around f/11; at a 1:1 macro ratio with an f/8 aperture and a 100 mm focal length, the physical width of the focus zone is only 0.6 mm, necessitating a 15‑frame focus‑stacking sequence with 0.1 mm steps; a shallow depth of field significantly reduces likes and purchase intent (coefficients −0.23 and −0.94), a moderate setting constructs a clear visual hierarchy without losing context, while an extremely shallow depth of field intensifies emotional response but prolongs change detection time; optimal shooting regimes range from f/8 with focus stacking for catalogs to f/4 for advertising macro scenes and moderate apertures for social media. This article will be valuable to professional jewelry photographers, e-commerce specialists, marketers, and researchers in the field of visual perception.

The rising complexity of modern socio‑economic systems demands sophisticated risk assessment frameworks capable of anticipating adverse outcomes across heterogeneous domains. This paper proposes a novel, cross‑domain conceptual framework for AI-driven risk assessment that synthesizes insights from two apparently disparate fields: financial market risk forecasting and hazardous-material (hazmat) transportation risk analysis. Drawing on the latest developments in augmented analytics and predictive modeling within finance (Hassan, 2025; Pala, 2023; Oko‑Odion, 2025; Kesarpu & Dasari, 2025) and on extensive literature on risk modeling for hazardous-material transport (Liu et al., 2021; Dong et al., 2020; Li et al., 2020; Ditta et al., 2019; Guo & Luo, 2022; Huang et al., 2018; Liu et al., 2018; Erkut et al., 2007; Ma et al., 2020), the framework aims to bridge methodological gaps and deliver a unified paradigm. The proposed framework foregrounds privacy-preserving federated learning techniques (Kalejaiye, 2025) and real-time event sourcing architectures (Kesarpu & Dasari, 2025) as foundational components. We argue that many of the structural and methodological challenges in financial risk forecasting—such as non-stationarity, data sparsity, and regulatory constraints—mirror those in hazmat transportation risk modeling, such as accident unpredictability, environmental variability, and inter-jurisdictional data silos. Through this convergence, the paper outlines theoretical implications, exposes limitations, and sets a research agenda for future empirical validation. The integration of cross-domain methods promises enhanced predictive accuracy, improved resilience, and holistic risk oversight in industries where both financial and physical risks are significant.

Financial inclusion has become a fundamental factor in socio-economic growth, empowering individuals and communities, especially those that do not have the facilities of traditional banking to obtain fundamental financial needs: credit, savings, and insurance. Due to the efforts at the global level to increase access, high disparities still exist among the underserved communities whereby a lack of financial literacy, institutional discrimination, and infrastructural barriers impact the righteous access to the credit market. This study examines how the various patterns of loan distribution, credit demand, and financial inclusion occur within underserved communities using data-driven methodologies and superior visualization tools. By studying the demand of loaning in different areas such as business, personal, educational, housing, and car loans; the study is able to conclude that the demand of credit is well spread amongst all these areas thus underlining the diversity of financial needs. Employing the interactive dashboard, filtering, and statistical methods, and the study equips stakeholders with practical information about the financial habits and preferences of borrowers and the need for inclusive lending patterns that will consider various credit needs. With data visualization and predictive analysis, this study illustrates how complicated financial data can be converted to results that are clear, comprehensible, and applicable to policy making and decision making by financial institutions as well as policymakers. Machine learning and AI-based techniques are also included in the methodology, which reinforces the reliability of loan risk evaluation, anticipates possible default patterns, and allows creating more effective models of credit distribution. The results support the significance of socially fair lending policies which reduce unbalanced socio-economical demands and financial feasibility and make the deprived community benefit credit access and develop empowerment, resiliency. Further, the study brings out the ethical and regulatory aspects of technological solutions implementation with respect to financial systems as they require fairness, transparency, and accountability. This study optimizes on the field of knowledge about financial inclusion by providing an effective framework of incorporating the aspects of technological innovation, data-driven analysis, and inclusive policy-making in the envisaged goal of a more transparent, sustainable, and equitable financial ecosystem. The findings of this study can be useful not only in research but also in everyday life at the financial and policymaking levels and thus can be used as a resource to interested stakeholders to reduce the disparity between financial availability and socio-economic advances.

The purpose of this article is to determine the characteristic features and the practical application of the rustic style in wedding floristry design as an expression of eco-trends and to demonstrate its contribution to the formation of the modern culture of wedding rituals.

The research methods used in the article are: system analysis; observation and generalization; analysis and synthesis; expert assessment methods and the graphic method.

In this study it is determined that, considering the rustic style in wedding floristry, its key features and advantages should be noted, which are directly connected with popular eco-trends and with the contribution to the modern culture of celebration. The rustic style is distinguished by its naturalness, closeness to the environment, and the use of natural materials as the basis for décor. This includes the use of wood, field flowers, moss, branches, and other eco‑materials that create an atmosphere of coziness and harmony.

The advantages of such an approach consist in its environmental friendliness — it contributes to minimizing the impact on nature, and also allows the support of local floristic traditions. The rustic style does not require great luxury, therefore it is a more economically accessible option for many couples. In addition to aesthetic attractiveness, it creates an organic and warm atmosphere, helping to emphasize the sincerity and cordiality of the event.

In the modern culture of wedding ceremonies, the rustic style actively forms the tendency toward more conscious and simple celebrations. It reflects the striving for harmony with nature and the return to traditional values. Thus, the rustic style is not simply a trend — it becomes part of a general cultural rethinking of the way important life events are held.

This paper addresses the migration of PeopleSoft application modules to Oracle Cloud Software as a Service (SaaS) using a “Lift-and-Shift” approach. While infrastructure migration is well-documented, this research focuses on the application-centric aspects, providing a detailed playbook for organizations. The paper outlines a structured methodology encompassing Boot Camp sessions on Oracle Cloud offerings, organization requirement reviews to determine the fit, fit-gap analysis on the business requirements, review of key gaps when adopting Oracle Cloud, implementation best practices, rigorous testing strategies, and culminating in user training. The proposed methodology streamlines the migration process, leading to a 30% increase in project efficiency and an estimated 25% saving on infrastructure management. It emphasizes minimizing disruption to business operations and maximizing the benefits of cloud adoption, such as scalability and cost-efficiency [3][4][11]. The research draws upon existing literature, case studies [5], and industry best practices to present a practical guide for successful PeopleSoft application migration to Oracle Cloud, and its utility is conceptually validated through a simulated case study that illustrates its application.

This article examines the specifics of using artificial intelligence tools to optimize wellness programs, focusing on the integration of diet, exercise regimens, and the formation of healthy habits. The digital transformation of this field faces a paradox of information overload coupled with a lack of personalized solutions. The fragmented nature of existing approaches, in which nutrition, physical activity, and psychological aspects of health are treated in isolation, reduces the effectiveness of wellness programs. This study aims to systematize modern concepts regarding the integration of AI for creating holistic, adaptive systems that support a healthy lifestyle. The article highlights contradictions between the growing interest in algorithmic solutions in the wellness sector and the insufficient development of the methodological foundations for their implementation, as well as between the technological capabilities of AI systems and the ethical constraints on their use. The analysis demonstrates the potential of AI to overcome the limitations of traditional programs through multifactor analysis of biometric data, predictive modeling of individual regimens, and affective-cognitive regulation. A system architecture is proposed that integrates physiological parameter monitoring, adaptive planning of diet and exercise regimens, and a module for psychological support. The materials presented in the article are of value to researchers in digital health, developers of wellness applications, and specialists in preventive medicine.

The study provides a comprehensive assessment of strategies and empirical results of implementing artificial intelligence-based solutions aimed at improving the efficiency of business processes in small and medium-sized enterprises (SMEs). The purpose of the work is to systematize and analyze the effectiveness of two key AI directions: conversational systems (conversational AI) and generative digital avatars in the context of lead generation, marketing communications, and customer service. The scientific novelty of the work lies in the proposal of a unified model for the synergistic integration of conversational and generative AI technologies, aimed at creating a seamless customer journey and maximizing marketing ROI while simultaneously increasing operational efficiency. Methodologically, the study is based on a review of relevant scientific literature from recent years. The conclusion confirms the initial hypothesis regarding a significant increase in operational efficiency and return on marketing investment when AI is strategically implemented. The work is of substantial practical and theoretical interest for SME executives, marketers, digital transformation experts, and researchers of applied aspects of artificial intelligence.