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 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.