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.

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.

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 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.