This article examines the evolution and current state of Russia-China relations over the past two decades. Despite repeated attempts to accelerate cooperation, both countries have experienced steady, incremental progress in economic, military, and political spheres. Initially underestimated or dismissed by Western analysts, the partnership has grown into a significant strategic alliance, characterized by mutual economic dependence and coordinated international positions. The analysis highlights the asymmetries in their relationship, emphasizing that Russia’s influence over China remains limited, especially in economic and political terms. The article concludes that Russian-Chinese relations are complex, resilient, and cannot be reduced to simplistic narratives of dominance or subordination.

AI-based systems in virtual assistants, conversation agents, and automated platforms rely heavily on intent recognition in Natural Language Processing (NLP). Using TF-IDF with classic classifiers results in reliable baselines, although using major transformer models, such as BERT, offers a deeper analysis and requires many resources. New developments in Quantum Machine Learning (QML) suggest new architectures that might help solve language problems. This study examined different types of intent classification models. A model can be set up using (i) classical TF-IDF + Logistic Regression, (ii) transformer-based BERT, and (iii) a quantum-classical variational quantum circuit (VQC) model from PennyLane. The models were evaluated using a custom JSON dataset with many intent examples. The results indicate that the classical model achieved the best performance among all, generating an accuracy of 68.97% and an F1-score of 70.69%, compared to the performance of BERT (accuracy=65.52% and F1-score = 63.91%) and the VQC model (accuracy=31.03% and F1-score = 25.65%). In addition to explaining the differences in the performance of the models, the visualizations pointed out their sensitive reactions to the structure and level of data in the categories. This paper discusses the advantages and disadvantages of different models, the challenges of using current quantum simulation tools, and the usefulness of QNLP in real-time and privacy-sensitive applications. At this current step in quantum model building, the findings here provide important guidance for future efforts on explainable, scalable, and hardware-accelerated QNLP.

The study is aimed at identifying and analyzing key determinants that influence the propensity of youth to prefer one social network over another. The methodological basis of the work consisted of a comprehensive systems analysis and synthesis of scholarly publications combined with the processing of statistical datasets from recognized industry sources. As a result, a close relationship was established between user choices and three interrelated factors: the specifics of algorithmic content ranking mechanisms, the dominant formats of materials and the sociocultural constitution features of each network’s community. Thus, TikTok emerges as an ecosystem stimulating authentic self-expression, interactive engagement and the consumption of predominantly entertainment media products, whereas Instagram retains the status of a gallery of visually refined idealized content serving as a means of social validation and personal brand maintenance. The scientific novelty of the study lies in the proposal of an adaptive model of PR strategies that accounts for the unique psychographic and behavioral characteristics of each platform’s users. The findings demonstrate the impossibility of effectively applying universal communication solutions and justify the need to design targeted tactics customized to the specifics of TikTok’s and Instagram’s functioning. The presented materials will be useful to both media communications researchers and PR and marketing practitioners seeking to optimize interactions with youth audiences.

In the contemporary automotive service industry, inventory management has emerged as one of the most critical determinants of operational efficiency, service quality, and financial sustainability. Auto service enterprises operate in an environment characterized by high demand uncertainty, extensive spare-parts assortment, rapid technological change, and intense competitive pressure. Consequently, traditional inventory management approaches are often insufficient to meet modern requirements. This article provides a comprehensive and deeply analyzed examination of inventory management mechanisms in auto service enterprises and proposes systematic directions for their improvement. Using analytical, comparative, and systemic research methods, the study explores organizational, technological, economic, and human-factor dimensions of inventory management. Practical examples illustrate the real-world applicability of the proposed mechanisms. The findings demonstrate that integrated inventory management—based on analytical tools, digital technologies, risk management, and customer-oriented strategies—significantly enhances inventory turnover, reduces costs, and improves service reliability.

Rapid developments in industrialization have significantly induced carbon emissions and consequently aggravated climate change. Distinct correlations between increasing levels of carbon emissions and extreme weather events through anthropogenic activities can provide clear evidence of vulnerabilities. The present study provides a detailed global data analysis that quantitatively links historical human-induced carbon emissions to observed trends in extreme weather phenomena. By synthesizing datasets, a solid statistical and physical connection between emissions and their climatic consequences is established. The analysis reveals that since 1960, fossil fuel emissions have quadrupled, peaking at 10.1 ± 0.5 GtC yr⁻¹ in 2023. This surge has pushed atmospheric CO₂ concentrations beyond 429 ppm, 50% more than the pre-industrial era and a peak not seen in at least 800,000 years. This atmospheric forcing has led to unprecedented global temperatures, with 2024 recorded as 1.55 ± 0.13 °C above the 1850–1900 baseline. While planetary sinks absorb about half of all human-caused emissions, with the ocean sink taking in 2.9 ± 0.4 GtC yr⁻¹ at the expense of ocean acidification, the atmospheric growth rate of CO₂ remains alarmingly high. The definitive link to extreme weather is confirmed through attribution science, which shows that human-caused climate change made 74% of over 600 analyzed extreme events more probable or severe, with some events being "virtually impossible" otherwise. These results offer unequivocal, data-supported evidence that rising carbon emissions are a direct driver of the increased frequency and intensity of devastating heatwaves, floods, and droughts, highlighting the urgent need to accelerate the global shift to a net-zero economy.

The evolution of wireless communication is currently at a critical juncture, transitioning from the established paradigms of 5G to the ambitious, ultra-high-frequency landscapes of 6G and beyond. This research article explores the convergence of three pivotal technologies: sub-terahertz (THz) communications, Digital Twin (DT) networks, and Edge Intelligence. By synthesizing existing literature and theoretical frameworks, this paper investigates how the integration of real-time physical-to-digital synchronization can mitigate the inherent propagation challenges of frequencies above 100 GHz. We provide an exhaustive analysis of the architectural requirements for DT-enabled 6G systems, focusing on the role of multi-sensor data fusion and ray-tracing propagation modeling in creating high-fidelity virtual replicas of the radio environment. Furthermore, the study delves into the necessity of Edge and Fog computing to handle the massive computational overhead required for low-latency synchronization. A significant portion of this work is dedicated to the standardization of cross-domain interfaces and secure edge intelligence, ensuring that DT deployments are resilient against adversarial interference. The findings suggest that while the hardware requirements for 6G are daunting, the systemic integration of digital twins and edge-based analytics provides a viable pathway for achieving the terabit-per-second speeds and microsecond latencies envisioned for the next decade.

The rapid expansion of social media platforms has fundamentally transformed how brands communicate with consumers, shifting traditional one-way advertising into interactive, data-driven, and socially embedded marketing ecosystems. This transformation has been accompanied by rising concerns over perceived risk, trust, privacy, manipulation, and authenticity, particularly among younger digital-native audiences. Drawing exclusively from established scholarship on perceived risk, consumer socialization, social media advertising, influencer marketing, emotional appeal, personalization, and platform-specific engagement, this study develops an integrated and theoretically grounded framework to explain how contemporary consumers—especially Generation Z—interpret, evaluate, and respond to social media advertising.

Grounded in the perceived risk model of Dowling and Staelin, this article argues that digital advertising is no longer evaluated only on economic or performance uncertainty, but also on psychological, social, and identity-related risks, especially when mediated by influencers, algorithmic personalization, and peer-generated content. The increasing visibility of influencer marketing and short-form video platforms has introduced new mechanisms of trust and persuasion, in which users rely more heavily on symbolic cues such as relatability, authenticity, follower metrics, and emotional resonance than on formal brand information.

By synthesizing insights from studies on influencer credibility, emotional appeal, informativeness, personalization, and platform-specific content, the article demonstrates that consumer responses to social media advertising are shaped by the interaction between perceived risk and perceived value. While influencer endorsements and emotionally rich content can reduce uncertainty and increase message acceptance, they can also trigger skepticism when users perceive commercial manipulation or identity misalignment. This tension is especially salient among Generation Z, whose high social media engagement and content exposure make them simultaneously more informed and more vulnerable to persuasive messaging.

Using a qualitative analytical framework inspired by Miles and Huberman’s approach to interpretive data synthesis, this research integrates prior empirical findings into a coherent model of consumer decision-making in digital advertising environments. The results show that positive consumer responses are most likely when advertising messages balance emotional engagement, informational transparency, and credible social proof. Conversely, when influencer content or targeted advertising is perceived as intrusive, misleading, or overly commercialized, users engage in defensive risk-handling strategies such as ad avoidance, skepticism, or negative brand evaluation.

This article contributes to digital marketing theory by unifying perceived risk, social influence, and platform-mediated engagement into a single explanatory structure. It also offers strategic implications for brands seeking to communicate effectively with socially connected and psychologically complex digital consumers. By focusing on trust, authenticity, and emotional relevance rather than mere exposure or algorithmic reach, marketers can build more sustainable and ethically grounded relationships with contemporary audiences.

The study examines the chemical-mineralogical composition and phase transformations of serpentinites from the Arvaten and Sultan-Uvays deposits to assess their suitability as local raw materials for forsterite refractories. XRD, SEM and chemical analysis were employed. The main stages of thermal transformations and their relationship to the formation of forsterite-bearing phases were identified. The magnesia-silicate and magnesia-iron modules were considered as suitability criteria. The results show that serpentinites from the Arvaten deposit are a promising feedstock for producing forsterite refractory materials.

Background: Gastric and gastro‑oesophageal junction cancers remain major contributors to global cancer mortality. In Nigeria, late diagnosis is common, and this pattern continues to drive high cancer‑related deaths.

Materials and Methods: Clinico‑pathological data were retrieved from four major histopathology laboratories in Anambra State, southeast Nigeria. Statistical analysis was conducted using the Chi‑square test in SPSS version 25 to assess differences in proportions.

Results: Gastric and gastro‑oesophageal junction cancers were most frequently diagnosed in individuals in their sixth and seventh decades of life, with a median age of 56.7 years. There was no clear sex predilection in the study population. The pylorus and antrum were the most common tumour locations, and intestinal‑type adenocarcinoma was the predominant histological subtype. Most cases presented at advanced stages, with the majority staged as T3 or T4.

Conclusion: Gastric and gastro‑oesophageal junction cancers in this setting are characterised by late presentation and are predominantly antral adenocarcinomas. These findings highlight the need for a regional screening initiative aimed at earlier detection and improved outcomes.

This study examines the feasibility of machine failure detection using deep learning approaches in a bid to improve predictive maintenance approaches. A deep-learning model has been created using the AI4I 2020 Predictive Maintenance dataset in order to effectively predict equipment failures. The model is built using two deep learning algorithms Long – Short Term Memory (LSTM), and Convolutional Neural Networks (CNNs). The preprocessing of the data that encompasses data cleaning, feature engineering, and normalization is applied to guarantee data quality. The metrics used to evaluate model performance are accuracy, ROC and AUC. Empirical findings show that the proposed LSTM-CNN model has a high predictive accuracy and significantly better results compared to the other traditional Support Vector Machine (SVM) models, especially when it comes to predicting complex patterns and dependence of operational data. In spite of the benefits, there are still issues of data quality, architecture, hyperparameter choice, and model interpretability. In general, the research validates the high potential of deep learning in reliable machine failure detection and specifies the main directions of future studies.

The contemporary environment of small and medium-sized enterprises is characterized by accelerating technological change, intensifying competitive pressures, fragmented markets, and the increasing importance of intangible strategic resources. Within this complex context, business consulting is no longer a peripheral support function but has become a central mechanism through which SMEs can adapt, innovate, and remain viable. Despite the growing importance of consulting, the theoretical and methodological foundations that explain how consulting interventions systematically influence business model innovation, organizational legitimacy, and network-based competitiveness remain fragmented and underdeveloped. This research article develops and elaborates a comprehensive theoretical and methodological framework for business consulting for SMEs by integrating the complex consulting model proposed by Kovalchuk (2025) with established perspectives from business model theory, innovation management, network theory, and corporate social responsibility research.

The study argues that consulting for SMEs cannot be reduced to technical advice or episodic problem solving but should be understood as a multi-layered socio-economic process that reconfigures organizational structures, strategic orientation, and inter-organizational relationships. Drawing on business model literature that emphasizes value creation, value capture, and value delivery as dynamic and interdependent processes, the article demonstrates that consulting interventions can function as catalysts for business model innovation when they are embedded within broader organizational and network contexts (Johnson et al., 2008; Magretta, 2002; Foss and Saebi, 2017). The complex consulting model elaborated by Kovalchuk (2025) is used as a central analytical lens to explain how consulting processes integrate diagnostic, design, implementation, and feedback loops in ways that align strategic intent with operational capability.

Methodologically, this research adopts a qualitative integrative design that synthesizes insights from multiple streams of literature. Rather than presenting statistical testing, the study develops a conceptually rich interpretive analysis that maps how consulting activities interact with business model patterns, network structures, and legitimacy-building mechanisms. This approach allows for a nuanced understanding of how SMEs can use consulting not only to solve immediate problems but also to develop long-term strategic resilience.

The results indicate that effective consulting contributes to SME performance by enabling systematic business model adaptation, strengthening network embeddedness, and enhancing organizational legitimacy in the eyes of stakeholders, including customers, governments, and partners (Saebi et al., 2017; Hu et al., 2020; Wenqi et al., 2022). The discussion further situates these findings within broader debates on innovation, corporate responsibility, and strategic change, highlighting both the opportunities and the structural constraints that shape consulting outcomes.

Overall, this article contributes to theory by integrating the complex consulting model of Kovalchuk (2025) into the mainstream discourse on business model innovation and SME strategy. It also offers a robust conceptual foundation for practitioners and policymakers seeking to design consulting systems that foster sustainable competitiveness in the SME sector.