The paper examines the transition of applicant tracking systems (ATS) from record-keeping databases to intelligent decision-support platforms grounded in representation learning and modular architectures. The study synthesizes peer-reviewed findings on semantic resume–job matching, learning-to-rank pipelines, human-in-the-loop re-ranking, and governance practices for fairness and auditability. Particular attention is paid to latency-aware MLOps, API-first interoperability, and explanation surfaces that restore recruiter control while compressing screening cycles. The analysis aligns these capabilities with persistent U.S. hiring frictions—screening workload, time-to-shortlist, and cost-per-hire—showing where embedding-based triage and event-driven integration yield measurable improvements in throughput and shortlist quality. The article proposes a product blueprint: fidelity-preserving parsing, domain-tuned encoders, hybrid re-rankers, continuous bias and drift monitoring, and evented integration with enterprise HR stacks. The discussion outlines risk–control mappings (bias, drift, opacity, load) and operational metrics for evaluation. Findings inform platform designers, HR leaders, and policy stakeholders seeking accountable automation that reduces delay while improving match quality.

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).

This article provides an in-depth analysis of the formation and developmental stages of the historical novel genre in the context of world literature. The historical novel is not only a means of artistically interpreting past events but also serves as a vehicle for preserving cultural memory, fostering national identity, and facilitating philosophical and aesthetic reflection. The article explores the poetic and stylistic features of the genre, the balance between historical fact and fictional imagination, and the evolution of the historical protagonist. Particular attention is given to the literary evolution of historical novels—from classical examples to modern and postmodern approaches. The study also examines the role and significance of historical novel traditions in various regions, with a focus on Turkish literature. Through the works of authors such as Namık Kemal, Tarık Buğra, and İskender Pala, the article illustrates how historical consciousness, national memory, and literary style converge in this genre.

Moreover, the influence of modern technologies and digital culture on the historical novel genre is addressed in detail. The article investigates how electronic formats, audiobooks, and interactive platforms contribute to the genre’s popularization and enhance engagement with readers. It also touches upon the emergence of AI-generated historical texts as a new phenomenon in literary creation. Finally, the article offers scholarly insights into the role of historical novels in education, moral development, and the preservation of cultural heritage, highlighting the genre's relevance and potential in the contemporary literary landscape.

The relevance of the study is determined by the rapid transformation of the global nail service market, where, against the background of growing demand for services, there is an increase in the incidence of iatrogenic onychodystrophies caused by aggressive treatment techniques and errors in the architecture of artificial coatings. The aim of the work is to provide a scientific rationale for and systematize the methodology of training in safe manicure that integrates medical standards of asepsis, the biomechanics of polymer materials, and the ergonomics of the technician’s work. The study employed methods of systematic review of specialized literature, comparative analysis of hardware technologies, and examination of the physicochemical properties of modern gel systems. The results obtained demonstrate that the implementation of wet cuticle processing techniques and protocols for applying thixotropic gels in a thin layer (without leveling) significantly reduces the risks of thermal burns, contact dermatitis, and traumatic onycholysis. Key principles of instructional design for advanced training courses are formulated, based on psychomotor regularities in the acquisition of manual skills. The practical significance of the work lies in the creation of an evidence base for the standardization of next-generation educational programs. The information presented will be of interest to practicing manicure technicians, instructors, technologists of cosmetic brands, and managers of beauty salons oriented toward the premium segment and client safety.

In the end, it provides a collection of design patterns for the building of RTC services that can withstand active network threats and are less susceptible to abuse.

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.

The convergence of persistent Augmented Reality (AR), robotics, and biometric analytics is creating a new technological paradigm where the digital and physical worlds are inextricably intertwined. This integration, while promising, introduces profound ethical risks that traditional governance models fail to address. This paper examines the complex challenges of data governance in this new era. Using a multi-method approach that integrates a systematic literature review, four purposive case studies (algorithmic recruitment, AR law enforcement, consumer wearables, and autonomous delivery robots), and thematic analysis, this research investigates four primary risk domains: (1) deep inferential threats from biometric and behavioural data, (2) cognitive manipulation and pervasive surveillance, (3) the "bystander problem" of non-consensual data capture, and (4) the diffusion of accountability in complex autonomous systems. Findings reveal systemic vulnerabilities, including "ambient biometric surveillance," "bystander invisibility," and "distributed responsibility," demonstrating the inadequacy of existing individualistic consent frameworks. The paper concludes by proposing a dual-pronged governance framework. This framework combines technical safeguards, such as dynamic consent architectures and mandatory AI Impact Assessments (AI-IAs), with policy innovations, including new legal categories for bystander data and multi-stakeholder co-regulatory oversight, to steer technological development toward a human-centric, rights-respecting future.

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

Under conditions of exponential growth of data sets accompanied by a deepening crisis of trust in them, the completeness and reliability of legal and financial reporting, including such forms as the Statement of Financial Affairs (SOFA) and the Schedule of Assets and Liabilities (SOAL), are acquiring critical importance. In this context, traditional data reconciliation procedures prove to be methodologically and technologically insufficient for the analysis of unstructured text fields, within which the most significant manifestations of hidden and hard-to-detect fraud are concentrated. The purpose of the article is to examine how the implementation of natural language processing (NLP) methods in the analysis of unstructured SOFA/SOAL fields modifies and effectively redefines the classical requirements for financial reconciliation. To achieve this aim, a mixed methodological approach is used, including a systematic review of academic literature, content analysis of industry and legal documents, as well as analog modeling based on case studies. The results obtained indicate that traditional manual reconciliation is of low effectiveness for identifying so-called semantic fraud, which is manifested not in formal arithmetic inconsistencies but in substantive and contextual distortions of information. In response to this challenge, the paper proposes a conceptual NLP model that enables the stratification of risks on the basis of metrics of completeness and reliability of disclosed data. The analysis of practical analogues in related domains demonstrates that the use of NLP tools can significantly increase data completeness (for example, from 60% to 73%) and ensure high reliability of results (accuracy at the level of 93%) compared to traditional manual analysis. The introduction of NLP technologies leads to a qualitative transformation of the reconciliation requirement itself: it evolves from comprehensive manual control to an automated, proactive and risk-oriented mode of working with data. The provisions and conclusions set out in the article have practical value for insolvency practitioners, forensic accounting experts, compliance professionals, as well as for regulators involved in the development and updating of new standards for auditing and control of financial reporting.

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.

The article presents a comprehensive analysis of how generative technologies transform product management methods across different stages of the product lifecycle. The study is based on a systematization of modern approaches to applying generative models in ideation, user research, requirements formation, product design, go-to-market preparation, and post-release processes. The paper examines mechanisms that ensure the stability of decisions, including the use of synthetic data, scenario modeling, automation of analytical procedures, and agent-based coordination schemes. Particular attention is given to the asymmetry in method maturity, the impact of data quality on the reliability of conclusions, the requirements for interpretability, and the organizational constraints inherent in corporate environments. The analysis demonstrates that generative technologies reshape not individual stages but the structure of the entire product cycle, creating a continuous decision-making loop and redistributing responsibilities between analytical and engineering functions. The study concludes that the effectiveness of generative models is determined by the coherence of data, architecture, and development processes, which sets new requirements for integrating these technologies into complex products. The article will be useful for product management professionals, AI researchers, digital platform architects, and developers of corporate systems.

This paper surveys analytical and computational techniques for evaluating crack-tip fields in orthotropic composites, emphasizing stress-intensity factors, mode mixity, and T-stress. The review consolidates Stroh-type anisotropic elasticity and interaction-integral formulations for benchmark extraction of K and T, and compares extended finite element and isogeometric enrichments with phase-field and peridynamic schemes that capture fiber-guided path deviation and branching. Particular attention is paid to interface cracks and calibration of anisotropy-aware phase-field surface densities and nonlocal peridynamic kernels.

In addition to synthesizing recent methods, the article highlights the relevance of orthotropic crack modeling to high-performance aerospace composite structures, where accurate crack-tip prediction directly supports durability, certification, and safety of aircraft components. This connection strengthens the applicability of the proposed workflow to national priority areas involving advanced composite structures and reliability-critical industries such as aviation.

The objective is to synthesize a reproducible workflow that links analytical eigensolutions, robust mode-decomposition tools, and mesh-objective crack-evolution solvers. Methods include comparative analysis and structured evidence mapping across ten recent sources. The conclusion outlines when each framework is preferable, validation with Stroh/Jk, discovery with phase-field, CAD-centric accuracy with X-IGA/XFEM, and length-scale-sensitive evolution with peridynamics, together with guidance on uncertainty control and cross-validation.

This scientific article examines the distinctive features of axiological laws’ operational mechanisms during the Renaissance period. The study explores the role of axiological laws within the philosophical and cultural context of the Renaissance, the formation of values, and their impact on social life. Specifically, the article discusses the changing worldviews of the Renaissance era, the recognition of human values, the processes of self-discovery, as well as how philosophical and spiritual values were reflected in scientific approaches. Furthermore, the article provides in-depth analyses of historical and contemporary interpretations of axiology, along with the Renaissance’s influence on the development of science and social thought.

Within the framework of the study, an analysis was conducted of the system of technical and tactical preparation of basketball players aged 12–16, carried out at the intersection of sport pedagogy, cognitive psychology, and applied biomechanics. The study synthesizes up-to-date information on the global youth sport market, the volume of which in 2024 reached 13.9 billion United States dollars. The purpose of the work is the experimental verification of the effectiveness of differential training (DT) and cognitive-coordination training (CCT) when compared with traditional linear approaches to the construction of training programs. Substantively, the emphasis is placed on the regularities of the formation of tactical competence through small-sided game interactions, considered as an instrument for the purposeful complication of the game environment and for improving the quality of rapid situation analysis. The mechanisms of developing stable decision-making algorithms under conditions of time deficit and external pressure are described, which makes it possible to link technical actions with cognitive processes of forecasting, selection, and execution control. As empirical corroboration, statistical indicators of the effectiveness of training interventions for the groups are presented, including parameters of shooting accuracy, tempo characteristics of ball handling, and the developmental dynamics of sport-specific endurance. The obtained results are interpreted as confirmation of the hypothesis that the inclusion of variability in motor tasks and targeted cognitive load in the structure of preparation ensures a statistically significant increase in game skills and an enhancement of young athletes’ adaptability to the changing demands of competitive activity. In conclusion, recommendations are provided for coaching staff, oriented toward the improvement of defensive interactions and the optimization of coordination preparation of the sport reserve.

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.

Landscape science is a key branch of geography that examines the structure, development, and functioning of natural territorial complexes. As interactions between nature and society intensify, landscapes need to be studied not only through individual components but also as integrated systems. This paper systematizes the conceptual foundations of a systems approach in landscape science and outlines the role of landscape ecology in evaluating ecological functions, stability, and responses to anthropogenic pressure. Landscapes are treated as open systems connected to the external environment through exchanges of matter and energy, driven primarily by solar energy, atmospheric precipitation, and biological processes. The synthesis highlights key analytical tasks—assessing landscape stability, identifying ecological risks, regulating anthropogenic loads, and supporting rational use of natural resources—as a scientific basis for sustainable development. The systems approach is presented as practically important for landscape planning, territorial development management, and ecological monitoring.

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.