Search Results (2,466)

As social media influencers increasingly shape sustainable consumption, understanding the psychological mechanisms underlying consumer responses is essential. Drawing on social influence theory and reactance theory, this study examines how influencer characteristics affect sustainable behavioral intentions through perceived pressure and consumer reactance, while considering the moderating role of green self-identity. Using survey data from 382 respondents, the proposed model was tested using partial least squares structural equation modeling (PLS-SEM). Given the cross-sectional research design and the reliance on self-reported data, the findings should be interpreted as associational rather than strictly causal. The results show that influencer expertise, homophily, and social influence significantly increase perceived pressure. Perceived pressure, in turn, positively influences consumer reactance, which negatively affects sustainable purchase intention and positively affects avoidance intention. In addition, green self-identity significantly moderates the relationship between perceived pressure and reactance, such that consumers with a stronger green self-identity exhibit heightened sensitivity to perceived pressure and experience stronger reactance responses, indicating heightened sensitivity among environmentally self-identified consumers. These findings extend existing sustainability and influencer marketing research by revealing the dual and potentially counterproductive effects of persuasive communication. The study highlights the importance of autonomy-supportive and identity-consistent messaging for promoting sustainable consumption and provides practical guidance for designing effective influencer-based sustainability strategies. Full article

Social security systems constitute a structurally significant component of public finance in developing economies and often generate persistent fiscal pressures through budgetary transfers. Demographic transformation, widespread informality in labor markets, and weaknesses in contribution-based financing increase the dependence of social security systems on public resources. The objective of this study is to examine whether budget transfers to the social security system affect fiscal sustainability in Turkey by analyzing their relationship with the budget deficit and the public sector borrowing requirement. The analysis employs annual data for Turkey covering the period of 1984–2024. A comprehensive time-series econometric framework is adopted, incorporating conventional and structural-break unit root tests, the ARDL bounds testing approach with error correction modeling, and the Toda–Yamamoto causality method. The empirical findings provide evidence of a stable long-run relationship among the variables. The results indicate that social security budget transfers exert a statistically significant and persistent effect on the public sector borrowing requirement, while no direct long-run effect on the headline budget deficit is detected. Causality results further confirm that fiscal pressures associated with social security financing materialize primarily through borrowing dynamics rather than short-term budgetary imbalances. By explicitly modelling social security budget transfers as an independent fiscal channel over a long historical horizon, this study contributes to the literature by offering new empirical insights into the fiscal sustainability implications of social security financing in Turkey. The findings also provide policy-relevant evidence for developing economies facing similar institutional, demographic, and fiscal challenges. Full article

The revision and implementation of the Environmental Protection Law signaled a major transformation in China’s environmental regulatory paradigm—from a traditional command-and-control model to a more diversified and market-oriented approach. This shift has raised critical questions regarding the actual impact of regulation on green technological innovation. Using panel data from A-share listed firms in China between 2011 and 2022, this study employs a propensity score matching–difference-in-differences (PSM-DID) model to identify the causal effect of environmental regulation on green innovation. Results reveal that the enactment of the law significantly enhances firms’ green innovation capacity. Robustness tests confirm the stability of these findings. Further analysis identifies several potential transmission mechanisms. Specifically, we find robust empirical evidence that environmental regulation exerts its effects through elevated R&D investment levels and strengthened executives’ environmental awareness, while the financing constraint and environmental information disclosure channels yield suggestive yet less statistically robust results in indirect effect tests. Moreover, heterogeneous effects are more evident among non-state-owned enterprises, firms in the eastern region, and those in highly market-oriented provinces. This study contributes empirical evidence to the literature on environmental regulation and green innovation, and offers policy insights for improving environmental governance in emerging economies. Full article

High Mobility Group ^{Box 1} (HMGB1) and S100 proteins are major ligands of Receptor for Advanced Glycation End-products (RAGE) and have causal roles in endometriosis lesions. Yet the AGE–RAGE pathway that unifies Advanced Glycation End-products (AGEs) with these ligands has not been assessed in endometriosis. In diabetes, atherosclerosis, and chronic kidney disease, AGE–RAGE links insulin resistance and oxidative stress to inflammation, fibrosis, and organ harm. Endometriosis shares key drivers of AGE accumulation, including insulin resistance, oxidative stress, and chronic inflammation. Endometriosis is also linked to higher vascular risk and arterial stiffness. We asked whether AGE–RAGE could bridge metabolic stress to pelvic lesions and systemic risk. We did a focused review of mechanisms and an evidence map of studies on AGEs, RAGE, or known RAGE ligands in endometriosis. We grouped findings as most consistent with a driver, amplifier, consequence, or parallel role. We included 29 studies across human samples, cell systems, and animal models. Few studies measured AGE adducts directly. Most work tracked RAGE ligands (mainly HMGB1 and S100 proteins) and downstream immune and angiogenic programs. Across models, this pattern fits best with a self-reinforcing loop after lesions form. RAGE expression often aligned with lesion remodeling, especially fibrosis. Blood and skin readouts of AGE burden were mixed and varied by cohort and sample type. A central gap is receptor proof. Many models point to shared Toll-like receptor 4 (TLR4)/ nuclear factor kappa B (NF-κB) signaling, but few test RAGE dependence. Overall, current evidence supports AGE–RAGE as a disease-amplifying loop involved in chronic inflammation and fibrosis rather than an initiating trigger. Its effects likely vary by stage and site. Priorities now include direct lesion AGE measurement, paired systemic–pelvic sampling over time, receptor-level studies, and trials testing diet or drug interventions against clear endpoints. Outcomes could include fibrosis, angiogenesis, immune state, pain, and oocyte and follicle function. Full article

The human microbiome is often presented as “the next genetics,” with the expectation that microbial profiles will explain complex diseases and yield new therapies. Yet for most conditions, it remains unclear whether microbiome changes act as causal drivers or primarily mirror underlying host biology and pathology. In this narrative review, we argue that microbiome causality is frequently overstated relative to the roles of host genetics and the environment, and we explore the implications for molecular pathology. We outline a simple framework in which the microbiome can act as (i) a primary driver, (ii) a conditional mediator or effect modifier or (iii) an association biomarker that mainly reflects upstream processes. We then use marital and household studies as natural experiments to test whether chronic diseases track more strongly with a shared microbiome or with a shared lifestyle and host susceptibility. Across metabolic, inflammatory, neurodegenerative and ageing-related outcomes, spouses show only low to modest disease concordance, which is difficult to reconcile with a universally strong, transmissible microbiome causality. Adult microbiomes instead appear mostly host-constrained and context-dependent, acting more as destabilisers of homeostasis and amplifiers of allostatic load than as independent disease-causing factors. For molecular pathology, this suggests that microbiome features are often most informative as biomarkers integrated alongside host genomics, immune context and histopathology, rather than as standalone targets. Study designs and diagnostic workflows should therefore jointly model the host genome, environment, behaviour and microbiome within broader systems medicine frameworks. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) exhibits poor clinical response to gemcitabine, largely due to intrinsic and acquired mechanisms of chemoresistance. Identifying agents capable of enhancing gemcitabine efficacy without increasing cytotoxicity remains an unmet therapeutic need. Here, we characterise a small drug sensitiser molecule, B12, and evaluate its potential to sensitise PDAC cells to gemcitabine. Methods: Gemcitabine’s dose–response was assessed by MTT assay to determine IC₅₀ values and dose-modifying factor (DMF). Phenotypic consequences of co-treatment were examined using colony formation and wound scratch assays. Mitochondrial membrane potential (JC-1) and apoptosis (Annexin V/PI) were measured using flow cytometry. Transcriptomic profiling was performed using mRNA-seq with differential expression analysis and pathway enrichment (KEGG/GSEA). NF-κB activity was assessed by nuclear and cytoplasmic fractionation of p65, and RT-qPCR validation of NF-κB associated target genes. Results: B12 alone displayed minimal cytotoxicity in the PANC-1 cell line and normal pancreatic ductal HPDE cells, yet shifted the gemcitabine dose–response curve in PANC-1 cells, reducing the IC₅₀ and yielding a dose-modifying factor of 1.39. Functionally, B12 enhanced gemcitabine-induced suppression of colony formation and reduced wound closure relative to gemcitabine alone. The co-treatment also increased both mitochondrial depolarisation and apoptotic cell populations, with increased cell proliferation inhibition over time. Transcriptomic profiling identified a set of B12-associated genes downregulated both in B12-treated and B12 + gemcitabine conditions, including factors linked to growth, survival, inflammation, metabolism, and drug inactivation. Gene set enrichment analysis revealed negative enrichment of NF-κB associated pathways during B12 co-treatment. Consistently, nuclear-cytoplasmic fractionation showed that B12 reduced gemcitabine-induced nuclear accumulation of p65, accompanied by decreased expression of NF-κB associated targets such as BCL2L1, CCL20, SLC2A1, and MAP3K14. Conclusions: In PDAC cell models, B12 enhances gemcitabine cytotoxic response while displaying minimal intrinsic toxicity under the conditions tested. The sensitising phenotype is accompanied by increased apoptotic susceptibility and is associated with reduced NF-κB signalling at the pathway, transcript, and p65 nuclear localisation levels. However, to establish causality, the lack of sensitisation in HPDE cells will require further validation. Full article

Background: Violent TV series and streaming content are increasingly accessible to children, raising concerns about behavioral imitation and psychological effects. This study examined copycat behaviors and associated emotional and somatic symptoms among children who reported watching the age-restricted series Squid Game. Methods: In this observational study of 228 Italian primary school children (aged 8–11), 128 who had watched Squid Game formed the analytic sample. They were categorized into a Copycat Behavior (CB) group or a Non-Copycat Behavior (NCB) group based on self-reported imitation of scenes or games from the series. Parents completed the Child Behavior Checklist (CBCL). Group differences were assessed using Mann–Whitney U tests, and gender distribution was compared with Chi-square tests (α = 0.05). Results: Among viewers, 42 children (32%) engaged in imitation behaviors, typically reenacting game-based violent scenes with friends (52%), siblings (28%), or classmates (20%). Age and gender distributions did not differ between groups. The CB group scored slightly higher on the CBCL Somatic Complaints scale compared with the NCB group (M = 54.12 vs. 52.92; U = 1414.5, p = 0.033), although this difference was small. No significant differences emerged on other CBCL syndrome or broadband scales. Conclusions: Among children engaging in copycat behaviors exhibited a small, subclinical increase in somatic complaints. While causality cannot be inferred, the findings highlight the need to protect vulnerable children—particularly those prone to somatic distress—from unsupervised access to violent, age-inappropriate content. Media literacy for parents and educators, and longitudinal studies including non-viewers are recommended. Full article

Despite ongoing improvements in safety regulations and management practices, the construction industry continues to experience high rates of occupational accidents, highlighting the need for more effective preventive safety management. Although risk assessment systems have been introduced to address limitations of conventional document-oriented practices, empirical evidence on differences in workers’ safety-related outcomes in real construction settings remains limited. This study examined whether differences exist in safety consciousness, safety attitude, and safety behavior between construction workers who use a risk assessment system and those who do not. Group differences were analyzed using Welch’s t-tests, with effect sizes reported as Hedges’ g, and supplementary item-level analyses were conducted to explore underlying patterns. The results indicate that system users reported higher levels of safety consciousness, safety attitude, and safety behavior than non-users, with consistently large between-group differences across all three constructs. Item-level analyses showed more pronounced differences in proactive safety engagement, safety-first value orientation, and participatory safety practices. Rather than emphasizing causal effects, these findings are interpreted as reflecting differences in safety-related cognition, evaluative orientations, and practices across contrasting safety management and information contexts, providing context-specific insight into proactive safety management in construction settings. Full article

Supervisor ostracism represents a pervasive and detrimental workplace stressor, yet existing research has predominantly focused on reactive coping mechanisms, leaving a critical gap regarding how employees can proactively prevent such mistreatment. To address this problem, this study draws on signaling theory as an overarching framework—integrated with social exchange theory as a downstream mechanism—to propose that employees can actively construct a “moral shield” by engaging in green advocacy, a high-cost, self-transcendent behavior that signals intrinsic moral character. We tested our theoretical model using a multi-method design. Study 1, a scenario-based experiment with 146 supervisors, provided causal evidence that green advocacy leads supervisors to objectively grant interpersonal moral credits, which subsequently reduces their behavioral intentions to ostracize. Study 2, a three-wave time-lagged survey of 434 employees, complemented these findings by confirming that green advocacy is associated with employees’ perceived moral credits and reduced perceived ostracism in a field setting. Furthermore, we found that this signaling process is contingent upon the receiver’s interpretation: the protective effect of green advocacy is amplified when Supervisory Support for the Environment (SSE) is high. This research contributes to the literature by identifying a novel, behavior-based signaling strategy for averting social exclusion and validating the dual nature (granted vs. perceived) of moral credits in hierarchical interactions. Full article

This study draws on socio-technical transition theory to examine how multi-actor dynamics among producers, consumers, and the media within an experimental niche—Korea’s regulatory sandbox—shape policy responsiveness and the regulatory speed of governmental responses to emerging technologies, thereby influencing socio-technical transitions. We construct a longitudinal dataset of 2136 sandbox approvals between 2019 and 2025 and 1374 cases in which related legal or administrative adjustments have been completed. Changes in actor couplings before and after sandbox approval are first assessed using Pearson correlation analysis, while temporal lead–lag relationships are identified via vector autoregression (VAR) and Granger causality tests. Building on these dynamic analyses, the study subsequently investigates the determinants of regulatory response speed using ordered logistic regression, incorporating government policy orientation (progressive vs. conservative) as a moderating variable. The results show, first, that the strong producer–consumer coupling observed prior to sandbox approval weakens afterwards, whereas the consumer–media linkage becomes substantially stronger. Second, the time-series analysis of technologies within the regulatory sandbox reveals a typical technology-push pattern and a self-reinforcing feedback loop. Specifically, producer activity initiates the signal sequence, preceding consumer reactions; subsequently, media coverage significantly drives consumer engagement, and the resulting increase in consumer attention, in turn, stimulates further media coverage. Third, in the ordered logit model, media activity accelerates legal and regulatory reform, whereas consumer activity acts as a delaying factor, with producer activity showing no significant direct effect. Finally, government policy orientation systematically moderates the magnitude and direction of these effects. Overall, the study proposes an actor-centered mechanism in which learning generated in the sandbox is externalized through consumer–media channels and translated into regulatory pacing. Based on these findings, we derive practical implications for firms and regulators regarding proactive media engagement, transparent use of evidence, institutionalized channels for consumer input, and robust feedback standards that support sustainable commercialization of emerging technologies. Full article

India is one of the fastest growing economies with significant potential for the use of smart farming operations. Although agriculture is a major sector, implementation of smart technologies in the agriculture sector has not progressed in India. We use a mixed-methods approach to develop knowledge on the factors determining this slow adoption of smart technology and develop strategies for large-scale adoption in the Indian agriculture sector. First, qualitative interviews are used to understand the factors behind the slow diffusion of smart technology in the agriculture sector. Based on the responses, we link the results of the qualitative study from the agri-sector to the well-known Diffusion of Innovations (DoI) theory. We then develop a framework for applying Fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the impact of multiple causal factors. We apply our research findings to help achieve SDG 12 in the agriculture sector. Our findings indicate individual factors on their own may influence adoption, but some reasonable combinations of factors (e.g., a combination of technology, knowhow, experience, benefits-operation, and finance and reliability) could also result in the large-scale adoption of smart technologies in improving Indian agricultural operations. By doing so, we provide a contextual empirical configurational test of the DoI theory in the Indian smart agricultural context. Full article

Resilience to stress integrates cognitive, physiological, and behavioral adaptations to sustain performance under adversity. Genetic variation in catechol-O-methyltransferase (COMT, rs4680) and angiotensin-converting enzyme (ACE, rs1799752) modulates dopaminergic and renin–angiotensin signaling, influencing tissue oxygenation and fatigue resistance. We examined COMT- and ACE-promoter methylation and genotypes in relation to resilience traits in Swiss tactical athletes (24.6 years) with a maximal power output of 534 W and 21,656 W, respectively, during cardiopulmonary exercise and elbow strike testing. At a 5% false-discovery rate, COMT genotype/methylation explained ~12% of the variance in cognitive performance and metabolic resilience, while ACE explained ~6–7% in strength-endurance and muscle resistance. Antidromic linear associations between COMT genotype and methylation with visual reaction time under reactive stress indicate opposing regulatory influences, best captured by regression models incorporating (epi)genetic covariates. The strongest methylation effects involved COMT promoter associations with muscle hemoglobin content across cardiopulmonary exercise zones (r = 0.43–0.58) and sport-specific strain (r = −0.46). COMT- and ACE-promoter methylation, correlated with time spent in the first aerobic training zone (r = 0.55 and 0.32), indicating environmentally responsive epigenetic modulation. These findings highlight neurovascular–metabolic coupling via dopaminergic and renin–angiotensin pathways as a key mechanism in stress adaptation. System-level adaptations in these pathways align with COMT and ACE (epi)genetic blood profiles, positioning them as candidate resilience biomarkers. Larger, preregistered studies with site-specific CpG analyses and mechanistic assays are needed to establish causal relevance and translational utility for resilience-informed performance optimization in high-stakes professionals. Full article

Background/Objectives: Depressive disorder (DD) is a prevalent psychiatric condition often diagnosed through subjective self-reports, which can be time-consuming and lead to inaccurate assessments. To enhance diagnostic precision, integrating Electroencephalography (EEG) with machine learning (ML) has gained attention as an objective approach for DD diagnosis and severity assessment. Methods: We propose an interpretable EEG-based ML framework that integrates optimized functional connectivity features, including Coherence, Phase Lag Index (PLI), and Granger causality, to explore EEG-based functional connectivity patterns in individuals clinically diagnosed with depressive DD and to model symptom severity and cognitive vulnerability. The identified biomarkers provide a promising foundation for developing objective, clinically actionable decision-support tools in psychiatric care. Feature selection was performed using the Neighborhood Component Analysis (NCA) method, and biomarkers were identified through statistical tests. Results: The highest classification performance (97.66% ± 2.05%accuracy, 99.20% ± 1.10% sensitivity, 95.91% ± 4.66% specificity, 98.00% ± 1.02% f1-score, and 0.95 ± 0.48 MCC) was achieved using 21 NCA-selected features with a KNN (K = 9) classifier. The best severity assessment (r² = 0.89 ± 0.10, MSE = 3.96 ± 17.05) and cognitive impairment prediction (r² = 0.89 ± 0.06, MSE = 0.23 ± 0.45) were obtained using an ANN regressor with 20 and 17 NCA-selected features, respectively. Conclusions: Our approach outperforms previous EEG-based ML models in DD classification and severity prediction using fewer features. Notably, this is the first study to use EEG connectivity features to predict patients’ severity and cognitive impairment in DD. Coherence and PLI values from frontal and temporal pathways across the alpha, beta, and gamma sub-bands may serve as critical biomarkers for DD diagnosis, severity assessment, and prediction of cognitive impairment. Full article

We present a unified description of dense matter and neutron star structure based on simple but physically motivated models. Starting from the thermodynamics of degenerate Fermi gases, we construct an equation of state for cold, catalyzed matter by combining relativistic fermion statistics with the liquid drop model of nuclear binding. The internal stratification of matter in the outer crust is described by the $\beta$-equilibrium, neutron drip and a gradual transition to supranuclear matter. Short-range repulsive interactions inspired by Quantum Hadrodynamics are incorporated at high densities in order to ensure stability and causality. The resulting equation of state is used as input in the Tolman–Oppenheimer–Volkoff equations, yielding self-consistent neutron star models. We compute macroscopic stellar properties including the mass–radius relation, compactness and surface redshift that can be compared with recent observational data. Despite the simplicity of the underlying microphysics, the model produces neutron star masses and radii compatible with current observational constraints from X-ray timing and gravitational-wave measurements. This work demonstrates that physically transparent models can capture the essential features of neutron star structure and provide valuable insight into the connection between dense-matter physics and astrophysical observables; they can also be used as easy-to-handle models to test the impact of more complicated phenomena and variations in neutron stars. Full article

This article proposes a redefinition of scientific authorship under conditions of algorithmic mediation. We shift the discussion from the ontological dichotomy of “tool versus author” to an operationalizable epistemology of contribution. Building on the philosophical triad of instrumentality—intervention, representation, and hermeneutics—we argue that contemporary AI systems (notably large language models, LLMs) exceed the role of a merely “mute” accelerator of procedures. They now participate in the generation of explanatory structures, the reframing of research problems, and the semantic reconfiguration of the knowledge corpus. In response, we formulate the AI-AUTHorship framework, which remains compatible with an anthropocentric legal order while recognizing and measuring AI’s cognitive participation. We introduce TraceAuth, a protocol for tracing cognitive chains of reasoning, and AIEIS (AI epistemic impact score), a metric that stratifies contributions along the axes of procedural (P), semantic (S), and generative (G) participation. The threshold between “support” and “creation” is refined through a battery of operational tests (alteration of the problem space; causal/counterfactual load; independent reproducibility without AI; interpretability and traceability). We describe authorship as distributed epistemic authorship (DEA): a network of people, artifacts, algorithms, and institutions in which AI functions as a nonsubjective node whose contribution is nonetheless auditable. The framework closes the gap between the de facto involvement of AI and de jure norms by institutionalizing a regime of “recognized participation,” wherein transparency, interpretability, and reproducibility of cognitive trajectories become conditions for acknowledging contribution, whereas human responsibility remains nonnegotiable. Full article