The intensification of competition, the proliferation of brand choices, and the increasing symbolic saturation of consumer markets have elevated the strategic importance of understanding how durable consumer–brand relationships are formed, sustained, and transformed over time (Aaker 2009). Within this context, brand attachment, brand trust, and brand loyalty have emerged as interrelated yet theoretically distinct constructs that together shape the long-term value of brands from a consumer-based perspective (Belaid and Behi 2011; Atulkar 2020). Despite the extensive literature addressing these concepts individually, there remains a fragmented understanding of how they interact dynamically across utilitarian and symbolic consumption contexts, particularly in environments characterized by low involvement, service-dominant logics, and digitally mediated brand encounters (Dhikari 2019; Algharabat et al. 2020). This study develops an integrative theoretical and interpretive framework that synthesizes classical brand equity theory with contemporary relational and affective branding perspectives to examine how brand attachment and brand trust jointly contribute to the formation and stabilization of brand loyalty.

Grounded exclusively in established scholarly literature, this research adopts a qualitative-descriptive methodological orientation, emphasizing analytical reasoning, conceptual triangulation, and interpretive synthesis rather than statistical modeling (Baron and Kenny 1986; Aldás-Manzano 2013). The methodological approach is designed to bridge structural brand equity models with relational paradigms that foreground emotions, narratives, and symbolic meaning-making (Aaker and Aaker 2016; Bagozzi et al. 2017). By systematically analyzing patterns reported across prior empirical studies in retailing, hospitality, services, and consumer goods, the study identifies recurring relational mechanisms through which brand experiences and perceived quality evolve into trust-based attachments and, subsequently, behavioral and attitudinal loyalty (Caruana 2002; Hemsley-Brown and Alnawas 2016).

The findings suggest that brand attachment operates as a pivotal mediating construct that translates functional performance and symbolic resonance into emotionally grounded loyalty, while brand trust functions as a stabilizing force that mitigates perceived risk and reinforces relational continuity over time (Frasquet et al. 2017; Ahmadian et al. 2023). Furthermore, the analysis reveals that contextual factors such as service intensity, experiential richness, and brand authenticity significantly moderate these relationships, underscoring the need for context-sensitive branding strategies (Assiouras et al. 2015; Bahri-Ammari et al. 2016). The study contributes theoretically by offering a holistic relational branding model that integrates cognitive, affective, and behavioral dimensions, and managerially by articulating nuanced implications for building resilient brand relationships in contemporary markets characterized by volatility and consumer skepticism (Berthon et al. 2009; Green et al. 2015).

The rapid advancement of Artificial Intelligence (AI) is fundamentally transforming the educational landscape, requiring future computer science teachers to possess high levels of digital literacy and technological competence. This paper explores the technology and methodology of utilizing AI-powered digital platforms in the professional preparation of informatics educators. The research focuses on how these platforms—ranging from adaptive learning systems and automated grading tools to AI-driven content generators—can be effectively integrated into the teaching process. The study analyzes the current state of digital platform implementation in higher pedagogical education and identifies the specific skills future teachers need to master to facilitate AI-driven instruction. The proposed technology emphasizes a transition from traditional teaching methods to a more personalized, data-driven approach where AI acts as a co-facilitator. Key components of this technology include the selection of appropriate AI tools, the development of pedagogical scenarios for their use, and the ethical considerations surrounding AI in the classroom. The findings suggest that the structured use of digital platforms not only enhances the technical proficiency of future computer science teachers but also prepares them to create innovative, interactive, and efficient learning environments for the next generation of students.

Optimal modes of heat treatment of plant raw materials were selected based on the application of a high-frequency electromagnetic field.

The accelerating convergence of hyperautomation, generative artificial intelligence, process mining, and intelligent automation frameworks represents a profound structural transformation in how contemporary organizations conceptualize, execute, and govern complex workflows. Across financial services, cyber-physical systems, and digitally mediated customer environments, automation has evolved beyond rule-based scripting into adaptive, self-optimizing, and cognitively augmented systems. This research article develops an original, integrative theoretical framework that situates hyperautomation not merely as a technological assemblage but as an epistemic shift in organizational intelligence and operational rationality. Drawing strictly on the provided scholarly and practitioner-oriented references, the study synthesizes perspectives from intelligent process automation, robotic process automation, conversational artificial intelligence, Industry 4.0 sensor ecosystems, Internet of Behaviors paradigms, and metrological foundations of cyber-physical infrastructures. Particular emphasis is placed on the role of generative artificial intelligence and process mining as catalytic mechanisms that transform static workflows into dynamically learning systems, enabling continuous discovery, prediction, and optimization of organizational processes (Krishnan & Bhat, 2025).

The article advances three central arguments. First, hyperautomation must be understood as a multilayered socio-technical system in which data generation, behavioral interpretation, and decision orchestration are inseparably intertwined. Second, the integration of generative AI into automation architectures fundamentally reconfigures decision-making authority, shifting organizations from deterministic control toward probabilistic and adaptive governance models. Third, the operational success of hyperautomation is contingent upon infrastructural foundations—such as sensor reliability, dimensional metrology, and cyber-physical security—that are often under-theorized in automation discourse. Methodologically, the study adopts a qualitative, theory-building research design grounded in critical synthesis and interpretive analysis of existing literature. The results reveal that hyperautomation produces not only efficiency gains but also new organizational vulnerabilities, ethical tensions, and epistemological uncertainties. The discussion situates these findings within broader debates on Industry 4.0, intelligent automation, and digital transformation, offering a nuanced assessment of limitations and future research trajectories. By articulating a comprehensive conceptual model, this article contributes to academic scholarship and provides a theoretically grounded lens for practitioners navigating the complexities of next-generation automation ecosystems.

In the present study a comprehensive investigation of the potential for integrating artificial intelligence methods into the predictive maintenance system of marine machinery is conducted. The application of AI in this context emerges as one of the key factors contributing to the enhancement of reliability and safety in shipping. The aim of the study is to compare the performance of various machine learning and deep learning algorithms based on an analysis of contemporary scientific publications. As a result of the comparative analysis a multifactorial methodology for selecting the most suitable model has been developed taking into account not only prediction accuracy but also the volume and quality of the input data computational costs and the transparency of the obtained conclusions. It is shown that hybrid approaches — in particular when convolutional neural networks are used for feature extraction from vibration and acoustic signals and LSTM networks are used for time series analysis — demonstrate the highest accuracy in predicting the remaining useful life of critically important equipment (main engines generators etc.). The scientific novelty of the work lies in the proposal of an integrated framework for the selection of AI solutions that ensures a balanced consideration of accuracy computational complexity data requirements and interpretability of results. In conclusion it is substantiated that the transition to object-oriented technical maintenance based on AI enables a substantial reduction in operating costs and failure risks compared to reactive and planned preventive strategies. The obtained conclusions are of practical interest to researcher’s shipbuilding engineers and data analysis specialists engaged in the development of intelligent monitoring systems.

This article examines the effect of the amount of husk (husk) in cottonseed shrot on the duration of the extraction process and the amount of oil yield in obtaining high-protein cottonseed shrot.

The study also examines the relationship between the degree of cottonseed shrot grinding, the concentration of miscella formed, and the duration of the extraction process during direct solvent extraction of cottonseed shrot.