Search Results (37)

This study examines how Association of Southeast Asian Nations (ASEAN) is framed in Chinese, Japanese, and South Korean English-language mainstream media during four high-salience geopolitical events (2023–2025). Methodologically, it employs a qualitative comparative framing and discourse analysis of 28 systematically selected news articles from leading outlets in each media system, coded using Entman’s four framing functions (problem definition, causal attribution, moral evaluation, and treatment recommendation) and supplemented by representational logics and explicitly stated platform-governance indicators. Drawing on framing theory, representation, platform governance, and critical geopolitics, the analysis finds that ASEAN is portrayed not as an autonomous actor but as a flexible signifier within nationally inflected narratives. Chinese media emphasize regional cooperation and developmental connectivity, Japanese outlets foreground liberal-normative order and security alignment, and South Korean coverage prioritizes technocratic and pragmatic partnership. The study argues that ASEAN’s mediated visibility is shaped by recurring editorial framing patterns and, where explicitly invoked, by infrastructural and platform-related cues, revealing ongoing narrative contestation over regional power and legitimacy in East Asia. Full article

Under the influence of engineering disturbances, the loading rate of surrounding rock is in a state of continuous adjustment. This study conducts experimental investigations on the mechanical response characteristics under different strain rates (10⁻⁵ s⁻¹, 10⁻⁴ s⁻¹, and 10⁻³ s⁻¹). During the uniaxial loading process of coal–rock composite specimens, multi-parameter monitoring was implemented, and a systematic study was carried out on the ring-down count induced by microcracks, the energy values of acoustic emission (AE) events, the stage-dependent strain characteristics on the specimen surface, and the surface temperature variation characteristics. Additionally, the stress–strain curve characteristics under different strain rates were comparatively analyzed in stages. The loading process of the coal–rock composite specimens was reproduced using the Particle Flow Code (PFC3D 6.0) simulation software. The simulation results indicate that the stress–strain results obtained from the simulation are in good agreement with the laboratory test results; based on these simulation results, the energy accumulation and dissipation characteristics of the coal–rock composite specimens under the influence of strain rate were revealed. Furthermore, a microscopic damage model considering strain rate was constructed based on the Weibull probability statistics theory. The results show that strain rate has a significant impact on the strength, elastic modulus, and failure mode of the coal–rock composite specimens. At low strain rates, the specimens exhibit obvious progressive failure characteristics and strain localization phenomena, while at higher strain rates, they show brittle sudden failure characteristics. Meanwhile, the thermal imaging results reveal that at high strain rates, the overall temperature rise in the composite specimens is rapid, whereas at low strain rates, the overall temperature rise is slow—but the temperature rise in the coal portion is faster than that in the rock portion. The peak temperature at high strain rates is approximately 2 °C higher than that at low strain rates. The PFC simulation results demonstrate that the larger the strain rate, the faster the growth rate of plastic energy in the post-peak stage and the faster the release rate of elastic energy. Full article

This mixed-methods case study explores risk management in hotel events using a large resort hotel in Portugal as its empirical setting. Addressing a critical gap between theoretical risk frameworks and their practical application, the research examines which risks are prioritized, how they are perceived, and who owns them across different organizational roles. The study combines a quantitative probability-impact matrix with a qualitative analysis of interviews using a systematic code co-occurrence analysis structured by established risk categories. Quantitatively, operational and safety-related threats, such as accidents during setup, were identified as the most critical. The qualitative findings, however, revealed a stark contrast in siloed risk cultures. The Events Department demonstrated comprehensive, experience-based ownership of risks across all categories. In contrast, other departments exhibited a narrow, operationally focused awareness and showed significant risk blind spots for entire categories, such as Event Planning and Human Resources. This divergence fosters accountability gaps and normalizes recurring issues. Integrating the findings demonstrates that the primary challenge is not the risk register itself but rather the fragmented organizational perception and presence of these blind spots. The study concludes that bridging these perceptual silos is essential for building a resilient, proactive risk culture. The study contributes to theory by empirically mapping divergent risk cultures and blind spots, thereby highlighting the limitations of purely quantitative assessments. It offers a practical diagnostic method and recommendations for using categorical analysis to foster cross-departmental dialogue and shared ownership in hotel event management. Full article

Floods are among Pakistan’s most common and devastating natural disasters, and they are becoming increasingly frequent and intense as a result of climate change, glacial melt, accelerated urbanisation, and weak governance. While coverage of climate change in 2025 has improved compared to 2010 and 2022 in terms of attention to climate change, it still silences local voices and long-term resilience narratives. However, much of the literature on disaster reporting in Pakistan has been descriptive, focusing on one-off events rather than situating them within wider framing theories, agenda-setting, and disaster journalism. This study employs qualitative document analysis (QDA) of a sample (n = 300) of media texts from five mainstream Pakistani media outlets (print and broadcast) published between June and August 2025. Drawing on framing theory and using a hybrid coding framework, this study examines causal attribution, impact reporting, actor representation, and narrative patterns. The results show ongoing sensationalism and political blame frames, low inclusion of community voices, and competing discourses of climate change versus nationalist explanations (especially cross-border water politics). This study contributes to global conversations about disaster communication by demonstrating the role of media in fragile governance settings to reveal and obscure the structural causes of vulnerability. Theoretically, it broadens framing and agenda-setting scholarship by showing the simultaneous functioning of dual causal narratives, scientific (climate-induced) and political (nationalistic). It also provides policy recommendations for more inclusive, accurate, and resilient disaster reporting. Full article

Abdominal aortic aneurysms (AAAs) are progressive, life-threatening vascular disorders characterized by focal dilation of the abdominal aorta due to chronic weakening of the arterial wall. The condition often remains asymptomatic until rupture, which carries mortality rates exceeding 70–85%. Among the various etiological theories of AAA development, degradation of the extracellular matrix (ECM) has emerged as the most widely accepted paradigm, with the breakdown of elastin representing a central and irreversible hallmark event. Elastin, a highly cross-linked and durable structural protein, provides elasticity and recoil to the aortic wall. In human AAA specimens, reduced elastin content, impaired cross-linking, and extensive fiber fragmentation are consistently observed, while experimental studies across multiple animal models confirm that elastin degradation directly correlates with aneurysm initiation, expansion, and rupture risk. Elastin loss is driven by a complex interplay of proteolytic enzymes coupled with inflammatory cell infiltration and oxidative stress. Furthermore, elastin-derived peptides perpetuate immune cell recruitment and matrix degradation, creating a vicious cycle of wall injury. Genetic and epigenetic factors, including variants in ECM regulators and dysregulation of non-coding RNAs, further modulate elastin homeostasis in AAA pathobiology. Clinically, biomarkers of elastin turnover and elastin-targeted molecular imaging techniques are emerging as tools for risk stratification. Therapeutically, novel strategies aimed at stabilizing elastin fibers, enhancing cross-linking, or delivering drugs directly to sites of elastin damage have shown promise in preclinical models and early translational studies. In parallel, regenerative approaches employing stem cells, exosomes, and bioengineered elastin scaffolds are under development to restore structural integrity. Collectively, these advances underscore the pivotal roles of elastin not only as a structural determinant of aneurysm development but also as a diagnostic and therapeutic target. This review summarizes and integrates recent discoveries on elastin biology in AAA, with a particular emphasis on molecular mechanisms of elastin degradation and the translational potential of elastin-centered interventions for the prevention and treatment of AAA. Full article

Survival analysis is a fundamental tool for modeling time-to-event data in healthcare, engineering, and finance, where censored observations pose significant challenges. While traditional methods like the Beran estimator offer nonparametric solutions, they often struggle with the complex data structures and heavy censoring. This paper introduces three novel survival models, iSurvM (imprecise Survival model based on Mean likelihood functions), iSurvQ (imprecise Survival model based on Quantiles of likelihood functions), and iSurvJ (imprecise Survival model based on Joint learning), that combine imprecise probability theory with attention mechanisms to handle censored data without parametric assumptions. The first idea behind the models is to represent censored observations by interval-valued probability distributions for each instance over time intervals between event moments. The second idea is to employ the kernel-based Nadaraya–Watson regression with trainable attention weights for computing the imprecise probability distribution over time intervals for the entire dataset. The third idea is to consider three decision strategies for training, which correspond to the proposed three models. Experiments on synthetic and real datasets demonstrate that the proposed models, especially iSurvJ, consistently outperform the Beran estimator from accuracy and computational complexity points of view. Codes implementing the proposed models are publicly available. Full article

Seismic hazards in Thailand are frequently overlooked in disaster management planning, leading to insufficient research and significant economic losses during earthquake events. The 2014 Chiang Rai earthquake exposed critical vulnerabilities in Thailand’s building practices due to widespread non-compliance with building codes and limited preparedness. This exposure prompted the development of empirical vulnerability functions using loss data from 15,031 damaged residences. The study analyzed government compensation records, which were standardized using replacement cost metrics. Three distinct models were developed through probabilistic and possibilistic modeling approaches. Residual analysis demonstrated the superior performance of the possibilistic approach, with the Possibilistic-based Vulnerability Function achieving a 49.84% reduction in residuals for small loss predictions compared to probability-based models. The research findings indicate that possibility theory—capable of addressing multiple uncertainties—provided a more accurate representation of the observed losses. These results offer valuable guidance for enhancing seismic risk assessment and disaster preparedness strategies in local applications. Full article

The COVID-19 pandemic presented an unprecedented challenge for the tourism industry, disrupting operations, reducing customer demand, and forcing businesses to adapt quickly to a new reality. This study explored the impact of COVID-19 on the tourism and hospitality industry and addresses a specific research gap: the limited application of structured theoretical frameworks—particularly the three-R stakeholder collaboration model—in examining recovery strategies. Prior studies have largely focused on individual tactics or short-term impacts, rather than a structured, stakeholder-driven, multi-phase recovery process. Employing a qualitative research design, this study used grounded theory to investigate how tourism service providers and key stakeholders navigated the challenges of the pandemic. Data were collected through semi-structured interviews with 20 participants, including tourism business owners, hotel and event managers, public sector employees, and academics. These insights offer a comprehensive, multi-perspective view of how the industry navigated the pandemic. Grounded theory was used to inductively develop a model of recovery behaviors, with data coded through open, axial, and selective stages using MAXQDA 2020 software. Findings revealed several key strategies—such as cost-cutting, digital transformation, and cultural adaptation—that enabled businesses to maintain operations during the pandemic and recover in its aftermath. Finally, refashioning business models through digital transformation, the diversification of services, and a shift to local markets helped tourism providers adapt to the changing environment and ensure long-term resilience. Full article

This manuscript introduces a comprehensive framework for augmenting classical statistical methodologies through the targeted integration of core quantum mechanical principles—specifically superposition, entanglement, measurement, wavefunctions, and density matrices. By concentrating on these foundational concepts instead of the whole expanse of quantum theory, we propose “quantum-inspired” models that address persistent shortcomings in conventional statistical approaches. In particular, five pivotal distributions (normal, binomial, Poisson, Student’s t, and chi-square) are reformulated to incorporate interference terms, phase factors, and operator-based transformations, thereby facilitating the representation of multimodal data, phase-sensitive dependencies, and correlated event patterns—characteristics that are frequently underrepresented in purely real-valued, classical frameworks. Furthermore, ten quantum-inspired statistical principles are delineated to guide practitioners in systematically adapting quantum mechanics for traditional inferential tasks. These principles are illustrated through domain-specific applications in finance, cryptography (distinct from direct quantum cryptography applications), healthcare, and climate modeling, demonstrating how amplitude-based confidence measures, density matrices, and measurement analogies can enrich standard statistical models by capturing more nuanced correlation structures and enhancing predictive performance. By unifying quantum constructs with established statistical theory, this work underscores the potential for interdisciplinary collaboration and paves the way for advanced data analysis tools capable of addressing high-dimensional, complex, and dynamically evolving datasets. Complete R code ensures reproducibility and further exploration. Full article

This work investigates the impact of C-class solar flare events (XRA) on ionospheric and low-frequency signal propagation by analyzing the maximum correlation lag time, correlation, and Granger causality between low-frequency time-code signal strength and XRA. The results show that within the lag interval of (−5, 5) minutes, XRA exhibits the highest correlation and causality with signal strength, supporting the theory of a rapid ionospheric response and early warning to XRA. The correlation coefficient increases significantly with flare intensity, indicating that XRA has both linear and nonlinear dual effects on the disturbance of low-frequency signal propagation paths. Granger causality tests further confirm that XRA events have an immediate and sustained direct impact on signal strength. These findings provide a basis for understanding the disturbance mechanism of solar activity on the Earth’s ionosphere and support the use of low-frequency signals in space weather forecasting. Full article

The shift of European countries to more environmentally friendly sources of energy is leading to an increase in the share of natural gas in the energy mix. At the same time, the flexibility and cost-effectiveness of maritime transport are making liquefied natural gas (LNG) more competitive compared to traditional forms of natural gas, despite recent geopolitical, health and economic events threatening its supply chain. The aim of this study is to analyze the European LNG import network using network theory indicators to identify trading communities in the network and to investigate the security of supply through network failure simulation. The network model was developed using a programming code in MATLAB R2022B software. The results of the analysis can be summarized as follows: (a) a shift in the center of gravity of LNG trade from the Mediterranean to the Atlantic shores was identified; (b) a gradual consolidation of Europe’s import network was noted; (c) an increasing dependence of Europe on a limited number of countries was observed; and (d) the most critical nodes for network resilience were identified through modeling. Based on these findings, recommendations are proposed to strengthen Europe’s energy security and independence. Full article

Background/Objectives: With the rapid expansion of the global sports market, the significance of sports sponsorship has attracted growing attention. However, during the golden age of the sports industry’s development in China, international sports brand giants such as Nike, Adidas, and Under Armour have rapidly captured a substantial share of the Chinese sports consumer market through their distinctive product designs and varied marketing strategies. This has resulted in a highly competitive environment for China’s sports goods industry. Therefore, fostering the improved development of domestic sports brands has become a crucial issue deserving of thorough scholarly investigation. This study examines how consumers’ differing levels of sports involvement and the degree of fit between the sponsoring brand and the sponsored event affect their cognitive and emotional responses to sports sponsorships. Methods: By employing Predictive Coding Theory and ERP (event-related potential) brainwave technology, this study delves into the psychological and neurobiological levels to analyze the impact of consumer sports involvement on the processing of sponsorship information. Results: The results indicate significant differences in cognitive and emotional responses between high-involvement and low-involvement consumers. Additionally, the fit between the sponsoring brand and the sponsored event also significantly affects consumers’ cognitive and emotional responses. These differences stem from consumers’ complex and sophisticated predictive coding models. Conclusions: This study not only provides scientific evidence for sports brands in selecting and executing sponsorship activities, but also offers new perspectives for evaluating and optimizing sponsorship effectiveness. Full article

Current construction codes require detailed analyses for structural retrofitting, which must consider performance during seismic events. Therefore, the computational models used to evaluate existing infrastructure require nonlinear structural analysis and damage estimates. For structural retrofitting, nonlinear computational modeling must represent the connectivity between existing and new elements. This study proposes recommendations on structural modeling based on fiber elements to represent reinforced concrete (RC) moment frames retrofitted with mortar walls reinforced with steel wire mesh. For this purpose, capacity curves of moment frames retrofitted with mortar walls were calculated by hand with the Bernoulli–Euler beam theory, moment–curvature analyses, and a plastic hinge model. Then, these capacity curves were used to calibrate the connectivity and constraint conditions in fiber models between the existing frame and the new wall required to capture the performance of the retrofitted structure. The study found that, for a single wall connected with top and bottom frame border elements, the capacity curves from fiber models underestimate stiffness, maximum strength, and residual strength. These estimation issues are reduced by including intermediate connectivity nodes between the top and bottom frame where rigid link constraints connect the existing frame with the new wall. Full article

Symbolic encoding of information is the foundation of Shannon’s mathematical theory of communication. The concept of the informational efficiency of capital markets is closely related to the issue of information processing by equity market participants. Therefore, the aim of this comprehensive research is to examine and compare a battery of methods based on symbolic coding with thresholds and the modified Shannon entropy in the context of stock market efficiency. As these methods are especially useful in assessing the market efficiency in terms of sequential regularity in financial time series during extreme events, two turbulent periods are analyzed: (1) the COVID-19 pandemic outbreak and (2) the period of war in Ukraine. Selected European equity markets are investigated. The findings of empirical experiments document that the encoding method with two 5% and 95% quantile thresholds seems to be the most effective and precise procedure in recognizing the dynamic patterns in time series of stock market indices. Moreover, the Shannon entropy results obtained with the use of this symbolic encoding method are homogenous for all investigated markets and unambiguously confirm that the market informational efficiency measured by the entropy of index returns decreases during extreme event periods. Therefore, we can recommend the use of this STSA method for financial time series analyses. Full article

European clinical guidelines recommend the use of Exposure and Response Prevention (ERP) and Comprehensive Behavioral Intervention for Tics (CBIT) as first-line treatments for tic disorders. Although ongoing efforts in research are being made to understand the mechanisms underlying these behavioral approaches, as of yet, the neurophysiological mechanisms behind behavioral interventions are poorly understood. However, this is essential to tailor interventions to individual patients in order to increase compliance and efficacy. The Theory of Event Coding (TEC) and its derivative BRAC (Binding and Retrieval in Action Control) provide a theoretical framework to investigate cognitive and neural processes in the context of tic disorders. In this context, tics are conceptualized as a phenomenon of enhanced perception–action binding, with premonitory urges constituting the perceptual and the motor or vocal expression constituting the action part of an event file. Based on this, CBIT is assumed to strongly affect stimulus–response binding in the context of response selection, whereas the effects of ERP presumably unfold during stimulus–response binding in the response inhibition context. Further studies are needed to clarify the neurophysiological processes underlying behavioral interventions to enable the individualization and further development of therapeutic approaches for tic disorders. Full article