Search Results (1,738)

Social media influencers (SMIs) have become effective intermediaries that influence consumer perceptions, attitudes, and behavioral intentions through their online presence and persuasion skills; this has made it imperative to comprehend how buyer-related variables contribute to brand loyalty within contemporary marketing research. This study, therefore, examines the effect of social media influencer capability on brand loyalty in Saudi Arabia, using brand trust as a mediating variable and authentic leadership as a moderating variable. Utilizing Social Exchange Theory and Authentic Leadership Theory, the study applied a quantitative cross-sectional survey design. Data were purposively collected from 476 active social media users in three major commercial hubs in Saudi Arabia (Riyadh, Jeddah, and Dammam). The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that authenticity and communication skills have a positive and significant influence on brand trust and brand loyalty, but expertise and influence only have a significant and positive influence on brand trust, not on brand loyalty directly, which means that the two constructs are indirectly influencing brand loyalty. The study also finds that authentic leadership significantly moderates the relationship between expertise, influence, and communication skills and brand loyalty, while the interaction with authenticity is not significant. Moreover, the mediation analysis shows that brand trust plays a significant mediating role in the relationships between communication skills, expertise and influence and brand loyalty, implying that the antecedents play a leading role in fostering loyalty by first developing trust. The study contributes to theory by offering a process-based perspective on the concept of brand loyalty that positions brand trust as a fundamental mechanism and authentic leadership as a vital enabling context. The findings have practical implications for organizations that want to strengthen brand loyalty through authentic communication, trust-building strategies, and leadership practices in social media-based contexts. Full article

The integration of Geographic Information Systems (GISs) with hydrologic science has evolved over seven decades from manual catchment delineation and output visualization to AI-native spatial water intelligence, reshaping how the water cycle is observed, modeled, and managed. This review explores that evolution, from the progressively tightening coupling between GIS software and hydrologic models to an AI-assisted future in which the line between these two fields blurs and eventually dissolves completely. The evolution of GISs in hydrology is traced through four eras, stratified as: (1) the formalization of governing equations and digital terrain representations (1950–1985); (2) the initial GIS–model coupling era and the rise in watershed simulation (1985–2000); (3) open source and the start of the open data deluge (2000–2015); and (4) machine learning and cloud-native computing (2015–present). A four-level vision for the role of artificial intelligence in the next generation of spatial hydrology is then articulated, from AI-assisted GIS operation to spatially aware AI water intelligence that reasons directly over geospatial data without requiring a traditional GIS or simulation software as an intermediary. Broader limitations and challenges are also discussed. Full article

Academic functioning in university students emerges from the interplay of motivational, self-regulatory, emotional, and contextual processes. The present study examined the network structure linking academic motivation, self-regulated learning, academic engagement, academic burnout, generalized anxiety, self-esteem, and students’ ratings of instruction. Participants were 530 university students from Western Romania (Mage = 28.86, SD = 9.75; 87.5% women). Data were collected through an online cross-sectional survey using validated self-report instruments. A Gaussian Graphical Model was estimated using the EBICglasso procedure to examine the unique associations among the study variables and their relative structural importance within the network. The results indicated a moderately dense psychological network, with academic burnout emerging as the most structurally central node. Intrinsic motivation toward achievement, identified regulation, and performance control were positioned within the adaptive core of the network, whereas burnout, anxiety, amotivation, and low self-esteem clustered within the maladaptive region. Academic engagement occupied an intermediary position linking motivational and self-regulatory processes. Overall, the findings support a systems-oriented interpretation of academic functioning, suggesting that burnout represents a key convergence point in students’ psychological functioning, while self-determined motivation and self-regulated learning may serve as protective processes. These results highlight the value of network analysis for identifying psychologically meaningful intervention targets in higher education. Full article

Chronic obstructive pulmonary disease (COPD) is increasingly recognized as a systemic disorder with clinically significant extrapulmonary manifestations. Among these, renal dysfunction—manifesting as chronic kidney disease (CKD) and acute kidney injury (AKI)—is highly prevalent, frequently underdiagnosed, and strongly associated with adverse clinical outcomes. Meta-analytic data indicate that COPD is associated with more than a twofold increase in CKD prevalence, independent of shared risk factors such as age, smoking, hypertension, and diabetes. CKD in COPD is associated with increased mortality, exacerbation burden, and healthcare utilization. AKI represents a particularly severe expression of renal involvement, occurring most commonly during acute exacerbations of COPD (AECOPD). Although the reported incidence of AKI during AECOPD varies widely by clinical setting—from approximately 2% in population-based studies to over 20% in hospitalized cohorts—its presence is consistently associated with marked increases in mortality, respiratory failure, need for mechanical ventilation, and hospital length of stay. This review synthesizes current evidence supporting a lung–kidney interorgan crosstalk framework in COPD, whereby chronic and acute pulmonary pathophysiology generates systemic disturbances that progressively impair renal structure and function. The heart is incorporated as a physiological intermediary, modulating hemodynamic transmission and venous congestion, without constituting the primary disease axis. Recognizing the role of kidney complications in COPD is crucial, as it influences how we diagnose, predict outcomes, and treat patients—especially when there are sudden flare-ups. Full article

Background: Patients with diabetes mellitus exhibit increased susceptibility to peri-implant inflammation and implant failure due to systemic metabolic dysfunction, impaired immunity, and delayed tissue healing. The oral microbiome is increasingly recognized as a key intermediary in these pathogenic processes. Aims: This review aims to systematically evaluate the available literature examining the relationships among the oral microbiome, biomaterial biocompatibility, and inflammatory changes in peri-prosthetic tissues in insulin-dependent diabetic patients. Methods: A systematic search of PubMed and Scopus databases identified studies published between January 2000 and July 2025. Eligible studies (25 in total) included clinical, histological, microbiological, or immunohistochemical investigations involving diabetic patients rehabilitated with dental implants or prostheses. Study selection and reporting followed PRISMA 2020 guidelines. Results: Diabetic cohorts showed consistent microbial alterations, including a higher relative abundance of periopathogenic species (P. gingivalis, T. forsythia, and F. nucleatum), lower microbial diversity, and greater biofilm-forming potential. Histological analyses frequently described increased inflammatory infiltrates, higher cytokine expression, and reduced soft-tissue integration. Biomaterial surface characteristics were also associated with differences in microbial adhesion, while hyperglycemia was linked to microbial and host-response patterns suggestive of greater pathogenicity and inflammation. Collectively, these findings suggest that diabetes-associated dysbiosis may be associated with increased peri-implant inflammatory changes and altered peri-implant homeostasis. Conclusions: The oral microbiome may be involved in inflammatory activity and biocompatibility at the tissue–implant interface in diabetic patients. A better understanding of host–microbe–material interactions may support risk assessment and help inform future personalized management strategies, such as targeted antimicrobial approaches, probiotic modulation, and biomaterial surface optimization, although these implications should be interpreted cautiously given the predominantly observational and heterogeneous nature of the available evidence. Full article

Background: As China enters the digital era and actively promotes an active aging strategy, smart wearable devices have become increasingly prevalent among older adults; however, their impact on health inequality remains unclear. This study investigates the association between smart wearable devices and health, as well as health inequality, among Chinese older adults, and further examines the mediating roles of joy of living and social participation. Methods: Data were derived from two waves (2018 and 2020) of the China Longitudinal Aging Social Survey (CLASS), with a final sample of 7098 adults aged 60 and above. A two-way fixed-effects model, propensity score matching–difference-in-differences (PSM-DID) approach, and mediation analysis were employed. Results: Smart wearable devices were significantly positively associated with both health and health inequality among older adults in China. Mediation analysis revealed that joy of living and social participation played an intermediary role. Conclusions: This study provides preliminary evidence that smart wearable devices are associated with health and health inequality among Chinese older adults. Policy efforts should focus on developing more user-friendly devices, promoting digital literacy among older adults, and supporting disadvantaged groups. Furthermore, the mediating effects suggest that fostering joy of living and encouraging active social participation may serve as effective pathways to improve health. Full article

Background: Plant-rich dietary patterns are widely associated with metabolic and gastrointestinal health benefits. However, individuals consuming predominantly plant-based foods may also experience chronic low-dose exposure to dietary pesticide residues. At the same time, physical activity is recognized as an important lifestyle factor influencing metabolic health and gut microbiome composition. How microbiome features and microbiome-related metabolic profiles vary according to physical activity level in adults consuming plant-rich diets and reporting gastrointestinal symptoms remains insufficiently characterized. Objective: To explore associations between physical activity level, gut microbiome characteristics, and urinary organic acid patterns in adults consuming predominantly plant-rich diets and experiencing gastrointestinal symptoms, within a cohort characterized by comparable estimated dietary pesticide exposure used as a contextual dietary background variable. Methods: This cross-sectional observational study included 93 adults consuming ≥50% plant-based foods for at least six months and reporting persistent gastrointestinal symptoms. Participants were stratified according to physical activity level using WHO-based thresholds (<150 vs. ≥150 min/week of moderate-intensity activity). Stool microbiota were assessed using a targeted quantitative PCR panel, and microbial diversity was summarized using a laboratory-derived Shannon index. A voluntary subgroup (n = 50) underwent targeted urinary organic acid analysis (LC–MS/MS). Dietary pesticide exposure was indirectly estimated using national surveillance data combined with individual dietary records and was applied uniformly across groups. Analyses were primarily descriptive and exploratory; results are presented as associations. Results: Estimated dietary pesticide exposure did not differ between physical activity groups. Participants with lower physical activity were older and exhibited lower microbial diversity and a higher prevalence of reduced abundance in selected commensal taxa. Differences were observed in selected intermediary organic acid markers, while no statistically significant difference was found for the bile acid-related indicator. Several cross-domain correlations were identified between microbial features and metabolite patterns. However, given the cross-sectional design, age imbalance between groups, and subgroup-based metabolomic analyses, the findings should be interpreted as hypothesis-generating rather than indicative of independent effects of physical activity. Conclusions: In adults consuming plant-rich diets and reporting gastrointestinal symptoms, physical activity level was associated with distinct microbiome and microbiome-related metabolic patterns under comparable estimated dietary pesticide exposure. These findings highlight the potential contribution of lifestyle factors to interindividual variability in gut microbial and metabolic profiles, while underscoring the need for age-adjusted, longitudinal, and biomarker-based studies to clarify directionality and mechanisms. Full article

Objective: Emphysema is an important chronic obstructive pulmonary disease (COPD) phenotype characterized by the destruction of air spaces distal to the terminal bronchiole. Aiming to detect potential emphysema biomarkers and to assess the systemic effects of emphysema in blood plasma, we conducted a small cross-sectional shotgun proteomics study. Methods: This study included N = 40 participants divided into four subgroups (N = 10 per group): patients with emphysema and COPD (CE), patients with COPD but without emphysema (CN), healthy smokers (HS) and healthy never-smokers (HN). The participants were sampled non-probabilistically to be similar in terms of age, sex and comorbidities. Participants’ blood plasma was analyzed using liquid chromatography–mass spectrometry. Bioinformatic analysis included detection of differentially expressed proteins (DEPs) and overrepresentation analysis (ORA). Results: Across all groups, a total of 994 proteins were identified, with NADP-dependent malic enzyme (NADP-ME; encoded by ME1) being the only DEP in the CE vs. CN contrast. Proteins such as BMP1, ADAMTSL-2, -4 and IGFBP4, -5, 6 were identified to be upregulated in CE vs. HN. Fibulin-1, -3 and several immunoglobulin components were identified to be downregulated in the CE vs. HN contrast. ORA revealed several enriched processes, including serine-type endopeptidase activity, insulin-like growth factor I and II binding, and signaling receptor binding. Conclusion: We propose NADP-ME, an important enzyme of intermediary metabolism and redox homeostasis, as a potential biomarker candidate of emphysema. Notably, NADP-ME is also implicated in anoikis resistance. Additionally, changes in the expression levels of BMP1, ADAMTSL-2 and -4, and fibulin suggest potential major systemic effects of extracellular matrix perturbation. As all data was derived from LC-MS analysis, these findings need to be further evaluated with complementary methods. Full article

Sound plays a fundamental role in human perception, conveying information about events, objects, and spatial dynamics that may not be visually accessible. However, current technologies such as Acoustic Event Detection typically reduce complex soundscapes to textual labels, often failing to preserve their semantic richness. This limitation motivates the exploration of sound-to-image (S2I) translation as an alternative connection between audio and visual modalities. Unlike multimodal approaches guided by intermediary constraints during the learning process, we investigate S2I translation without class supervision, cluster-based alignment, or textual mediation, a paradigm we refer to as direct S2I translation. To the best of our knowledge, apart from our previous work, no prior study addresses S2I translation under this fully direct setting. We propose a convolutional–attention generative framework composed of an audio encoder and a densely connected GAN integrating self-attention and cross-attention mechanisms. The attention-based model is systematically compared with a purely convolutional baseline. Results show that introducing attention at early stages of the generator significantly improves translation performance, increasing the likelihood of producing interpretable and semantically coherent visual representations of sound. These findings indicate that attention strengthens semantic correspondence between audio and vision while preserving the fully direct nature of the translation process. Full article

Rural homestead reform and rural revitalization policies support and influence each other, forming a coupled relationship. Based on the research data of 120 villages in four provinces of China in the pilot area of rural homestead reform, this paper empirically analyzes the effect and mechanism of the coupling and coordination of rural homestead reform and rural revitalization on the development of the rural collective economy by applying the coupling coordination model and the multiple chain intermediary effect model. The results show that the coupling and coordination of rural homestead reform and rural revitalization can significantly improve the level of rural collective economic development, in which the improvement of infrastructure and the optimization of industrial structure play an intermediary role. The intermediary effect of the optimization of industrial structure is higher than the intermediary effect of the improvement of infrastructure. In addition, the coupling and coordination of rural homestead reform and rural revitalization can also enhance the development level of the rural collective economy through the chain intermediary effect of improving rural infrastructure and then optimizing rural industrial structure. From the heterogeneity analysis, the mediation effect of infrastructure improvement and industrial structure optimization is stronger in the western region than in the central region. However, the central region can promote the development of the rural collective economy through the chain mediation effect. Full article

The acceleration of climate change poses a growing threat to human health, particularly by exacerbating non-communicable diseases (NCDs) such as cardiovascular and respiratory conditions. Rising global temperatures amplify air pollution and environmental toxins, disproportionately affecting vulnerable populations. This narrative review explores the complex pathways linking climate-related environmental stressors to adverse health outcomes, focusing on the intermediary roles of epigenetic modifications and alterations in the microbiota. Epigenetic processes, including DNA methylation and histone modifications, may mediate how environmental exposures influence gene expression and disease susceptibility. Concurrently, changes in microbiota composition induced by pollutants and temperature fluctuations can promote inflammatory responses and immune dysfunction. Elucidating these molecular mechanisms is essential for developing targeted interventions and adaptive strategies to mitigate the health impacts of climate change. This review underscores the importance of identifying epigenetic and microbiota-based biomarkers for early risk stratification and for informing public health prevention and adaptation policies. A transdisciplinary approach, grounded in the One Health framework, is critical to addressing the growing burden of climate-sensitive diseases and reducing health inequalities. Full article

This paper argues that digital identity in AI-mediated environments has become a central mechanism through which contemporary societies organise recognition, participation, and belonging. Digital identity is no longer simply a technical representation of the individual. It is produced through infrastructural processes of classification, ranking, and credibility signalling that determine who becomes visible, who is treated as legitimate, and who is able to participate meaningfully in social and civic life. The paper develops a conceptual framework that treats AI-driven platforms as social infrastructures rather than neutral intermediaries. It shows how identity is inferred through data-driven systems rather than negotiated through social interaction, how recognition is operationalised through visibility and credibility metrics rather than ethical judgement, and how participation becomes conditional on algorithmic allocation of attention rather than guaranteed by access alone. Visibility is identified as the key conversion point through which inferred identity becomes social consequence. Drawing on interdisciplinary literature, the analysis demonstrates that misrecognition, exclusion, and inequality in platform environments are not primarily the result of isolated error or intentional bias. They are patterned outcomes of ordinary optimisation processes that distribute legitimacy and opportunity unevenly across social groups. These dynamics reshape group formation, harden social boundaries, and concentrate risk among populations that are already more vulnerable to misrecognition and reduced contestability. The paper concludes that governing digital identity is a societal challenge rather than a purely technical one. As platforms increasingly perform institutional functions without equivalent accountability, digital identity governance becomes a critical site of social ordering. Addressing this challenge requires public standards for how visibility, recognition, and participation are allocated, meaningful avenues for contestation, and protections against the normalisation of stratified belonging in AI-mediated societies. Full article

The nature of nonlinear dynamics of financial markets results in fractal geometry and chaotic behavior that can be viewed on a variety of scales in time. This paper conducts research on the fractal characteristics of the stock network and its contribution to the price of assets based on the Fractal Market Hypothesis (FMH). A multiscale network centrality measure is built based on high-frequency return dependencies to measure the self-similar, scale-invariant nature of inter-stock dependencies. The network factor and portfolio returns are then broken down with the wavelet packet decomposition (WPD) to obtain frequency-domain profiles, which characterize the variability of risk transmission in relation to investment horizons. The profiles are consistent with scaling properties of fractal, but the decomposition does not identify causal pathways on its own. Estimation of fractal dimension by use of the box-counting technique aided by the Hurst exponent analysis reveals that the A-share of China market exhibited long-range dependence and multifractal scaling. Network factor has the largest explanatory power in mid-frequency between the D5 and D6 bands of 32 to 128 days. This intermediary frequency concentration is consistent with the hypothesis of heterogeneous markets, in which the groups of investors with varying time horizons generate scale-related price dynamics. The addition of the network factor to a 6-factor specification lowers the GRS under the 5-factor specification by 31.45 to 17.82 on the same test-asset universe, indicating better cross-sectional coverage in the sample. The estimates of the Lyapunov exponents (0.039) as well as the correlation dimension ($D_2=4.7$) confirm the presence of low-dimensional chaotic processes of the network factor series, but these values are specific to the Chinese A-share market over the 2005–2023 sample period. These results provide a frequency-disaggregated use of network-based factor modeling and suggest that it can be applicable in multiscale portfolio risk management where the investor horizon is not uniform. Full article

The decarbonisation of the transport sector is a cornerstone of the European Green Deal, necessitating a transition toward integrated, digital, and sustainable mobility solutions such as Mobility-as-a-Service (MaaS). While early MaaS implementations were characterised by local bottom-up experiments, recent state-level actions mark a shift toward large-scale systemic deployment. This paper investigates the evolving role of universities within this transition, using MaaS4Italy initiative as a primary case study. Through a qualitative analysis of 11 pilot projects, conducted between January and July 2025, the research examines how academic institutions have been integrated into the national governance framework, transitioning from traditional living labs for technical testing to pivotal institutional anchors and governance buffers. The findings reveal a dual role for universities: as scientific partners and as neutral mediators. However, a relevant paradox is highlighted as well: while the institutionalisation of universities de-risks public investment and fosters data-sharing trust, it may simultaneously limit their potential as high-density operational testbeds for innovative Corporate MaaS (CMaaS) solutions. Present research supports a broader understanding for policymakers, thus underscoring the importance of formalising the role of intermediary institutions to ensure the long-term sustainability and scalability of smart mobility ecosystems. These insights prove to be pivotal towards the implementation of multi-level environmental governance mechanisms and the strategic use of recovery funds to catalyse the transition toward climate neutrality. Full article

Generative AI has surfaced as a key driving force for corporate sustainable development and strategic transformation, offering new perspectives for effectively enhancing corporate ESG performance practices. Utilizing panel data sourced from Chinese A-share listed firms spanning the years 2012 to 2024, this research establishes and substantiates a model elucidating the mechanism by which generative AI impacts corporate ESG performance. The findings reveal the subsequent points: First, generative AI can effectively drive improvements in corporate ESG performance. Second, the caliber of information disclosure acts, in part, as an intermediary factor influencing the correlation between generative AI and corporate ESG performance enhancement. Third, sustainable innovation partially mediates the relationship between generative AI and corporate ESG performance enhancement. Fourth, environmental regulations weaken the beneficial influence exerted by generative AI on a company’s ESG achievements. Fifth, compared to non-manufacturing firms, companies situated in the central and western parts of China, and non-technology-intensive firms, the application of generative AI exerts a more pronounced enhancing impact on ESG achievements in manufacturing firms, firms in eastern regions, and technology-intensive firms. The research findings provide new insights for improving corporate ESG performance and provide strategic guidance for businesses aiming to attain long-term sustainable growth through reliance on generative AI. Full article