Search Results (5,042)

Green finance can theoretically direct capital toward low-carbon sectors, but systematic city-level empirical evidence is still limited for the Yangtze River Delta region. Using panel data of 41 prefecture-level cities from 2010 to 2024, this paper employs year-fixed-effects, mediation, and moderation models to examine the impact of green finance on carbon emission intensity. The findings are as follows. First, green finance significantly reduces carbon emission intensity. A one-standard-deviation increase in the green finance index lowers carbon intensity by about 23.6% of the sample mean, and this result is robust. Second, green technology innovation contributes about 30% and industrial structure upgrading contributes about 7%, serving as two key mediating pathways. Third, industrial pollution level positively moderates the abatement effect: the more polluted a city, the stronger the marginal emission reduction effect of green finance. Fourth, the emission reduction effect is more pronounced in low-income cities, while the moderating role of urbanization level is not significant. This paper reveals the transmission mechanisms and boundary conditions of the emission reduction effect of green finance, providing empirical evidence for designing regionally adapted green finance policies in the Yangtze River Delta. Full article

Background/Objectives: Anterior direct composite restorations are evaluated through instrumental color matching, clinician appraisal, and patient perception, but these endpoints may diverge by age. This cross-sectional study compared adolescents/young adults (AYA, 15–25 years) with adults/elderly (AE, 50–75 years) for spectrophotometric color difference (ΔE*ab), patient and clinician aesthetic ratings, patient–clinician agreement, and oral-health-related quality of life (OHRQoL). Methods: Consecutive recall patients with at least one anterior direct composite restoration placed ≥6 months earlier were screened; 128 were enrolled, and 126 completed all assessments (AYA n = 64; AE n = 62). Participants completed the OHIP-14 and aesthetic visual analogue scale (VAS) before receiving any USPHS, clinician VAS, or spectrophotometric feedback. A separate clinician, masked to patient scores and spectrophotometric outputs but not to patient age, recorded clinician VAS and modified USPHS criteria. Results: AE restorations showed higher ΔE*ab than AYA restorations (4.8 ± 1.6 vs. 3.2 ± 1.1; p < 0.001), whereas AYA reported lower patient VAS (72.4 ± 12.3 vs. 81.6 ± 10.8; p < 0.001) and higher OHIP-14 psychosocial burden (7.2 ± 2.8 vs. 4.0 ± 2.3; p < 0.001). Clinician VAS was higher in AYA (85.2 ± 7.3 vs. 79.4 ± 8.9; p < 0.001). Patient VAS correlated modestly with ΔE*ab (ρ = −0.38 in AYA; ρ = −0.31 in AE) and more strongly with psychosocial OHIP-14 scores (ρ = −0.54 and −0.47, respectively). Patient-clinician agreement was poor in AYA (ICC = 0.26) and moderate in AE (ICC = 0.58), with larger negative patient-minus-clinician discrepancies in AYA. Exploratory mediation statistically decomposed the age-related patient-satisfaction difference more through patient–clinician discrepancy than through ΔE*ab; causality cannot be inferred. Conclusions: Younger patients may experience dissatisfaction and psychosocial burden despite better instrumental color match. Assessment of anterior composites should combine objective shade measurement with patient-centered expectation clarification, and longitudinal studies should test temporal mechanisms and communication interventions. Full article

With the increasing embeddedness of AI robots and other intelligent technologies in organizational workplaces, leader AI-focused attention has emerged as an important reference point for employees as they use and adapt to AI-related technologies. Drawing on work-related rumination theory, this study develops and tests an integrated mediation model to examine how leader AI-focused attention is related to employee proactive behavior through two parallel pathways: problem-solving pondering and affective rumination. It further investigates the moderating role of AI job role clarity. Based on structural equation modeling of multi-wave survey data from 514 employees, the results show that leader AI-focused attention positively predicts employees’ problem-solving pondering and affective rumination. Problem-solving pondering is positively related to employee proactive behavior, whereas affective rumination is negatively related to employee proactive behavior. In addition, AI job role clarity positively moderates the relationship between leader AI-focused attention and problem-solving pondering; specifically, this positive relationship is stronger when employees report higher AI job role clarity. From the perspective of work-related rumination, this study extends the explanation of the psychological mechanisms linking leader AI-focused attention to employee proactive behavior. It also provides theoretical insights and practical implications for understanding the boundary condition of leaders’ attentional signals in AI-related work contexts and for supporting employee proactive behavior. Full article

How does artificial intelligence (AI) adoption shape administrative efficiency and e-citizen integration in local governments, and what role does professional development play in mediating these relationships? Drawing on a survey of 500 municipal employees across Spanish municipalities, this study employs partial least squares structural equation modelling (PLS-SEM), with formal non-linearity testing via Warp3 algorithms, to test a theoretically grounded model. The conceptual framework integrates Digital Transformation Theory and Public Value Theory as primary explanatory lenses, while drawing on the Technology Acceptance Model (TAM) and Total Factor Productivity (TFP) logic as complementary background perspectives that contextualise rather than directly operationalise the micro-level findings. Structural results reveal that AI adoption exerts a strong direct (and statistically linear) effect on perceived administrative efficiency (β = 1.04, p < 0.001; the standardised coefficient exceeding 1.0 and R² > 1 are a legitimate WarpPLS warp-model fit index rather than evidence of model misspecification: the Warp3 warp functions inflate the variance of predicted efficiency and break the additive identity SST = SSM + SSE, with the high AI–PD collinearity (r ≈ 0.84) as the contributing mechanism (RSCR = 1.000, SSR = 1.000); a comparative re-estimation without the moderation term yields β = 0.87 and R² = 0.76; we adopt this parsimonious specification (β ≈ 0.87, R² = 0.76) as the substantively interpretable estimate, with predictive relevance confirmed by a high Stone–Geisser Q² = 0.685, indicating that the model fits and predicts well rather than overfitting, while simultaneously stimulating professional development (β = 0.84, p < 0.001, R² = 0.70). Professional development positively predicted both efficiency (β = 0.27, p < 0.001) and e-citizen integration (β = 0.26, p < 0.01). Efficiency is the primary driver of e-citizen integration (β = 0.54, p < 0.001, R² = 0.53). The proposed moderation of AI adoption by professional development on efficiency was not supported (β = −0.01, p = 0.44), suggesting additive rather than synergistic effects. Model fit was robust (GoF = 0.701; ARS = 0.749; APC = 0.495); convergent and discriminant validity were confirmed by composite reliability, average variance extracted, Fornell–Larcker, and HTMT criteria; and common method bias diagnostics (Harman’s single-factor test, full-collinearity AFVIF, and marker-variable analysis) indicated that systematic method variance was not a material threat. These findings offer micro-empirical evidence of the mechanisms linking AI adoption to citizen service outcomes via a professional development pathway and provide actionable recommendations for Spanish and European municipalities navigating AI-driven governance reform. Full article

Drawing on dynamic capability theory and upper echelons theory, this study develops a moderated mediation framework that treats green dynamic capabilities as a mediator and top management team (TMT) behavioral integration as a moderator. Empirical tests based on sample data reveal that the application of generative artificial intelligence is positively associated with corporate green innovation. This relationship is partially mediated by green dynamic capabilities. Moreover, TMT behavioral integration not only positively moderates the direct effect of generative AI on green innovation but also strengthens both stages of the indirect path, thereby reinforcing the overall mediated mechanism. The study uncovers a “capability transformation” process and “governance boundary” conditions through which generative AI may facilitate green innovation in agricultural enterprises, extends theoretical research at the nexus of digital technology and green innovation, and offers practical guidance for agri-businesses seeking coordinated digital and green development. Full article

Marburg virus (MARV), a highly pathogenic member of the Filoviridae family, causes severe hemorrhagic fever with a high case fatality rate and currently lacks effective therapeutics. The viral entry process, mediated by the interaction between the MARV glycoprotein (GP) and host receptor C-type lectin domain family 4 member M (CLEC4M) (L-SIGN), represents a critical target for early-stage intervention. The active compounds from BindingDB and the decoy from DUDE were used. The RDKit was used for feature engineering. Machine learning models were trained on an initial dataset consisting of 56 active chemicals and 1232 decoys. Among the tested algorithms, the Random Forest model demonstrated superior performance, achieving the highest discriminative ability (AUC = 0.93, MCC = 0.88) on the test set. Virtual screening of 11,032 phytochemicals resulted in 120 predicted actives, of which 42 compounds satisfied drug-likeness criteria. Subsequent molecular docking identified three lead compounds (PubChem IDs: 42608095, 5281601, and 11243993) with moderate-to-promising binding affinities (−6.3 to −6.5 kcal/mol) toward the CLEC4M binding site. ADMET analysis revealed favorable pharmacokinetic and toxicity profiles for the selected lead compounds. DFT calculations of the three compounds highlighted their electronic stability and reactive nature, indicating that PubChem IDs 42608095 and 5281601 possess particularly stable electronic properties conducive to favorable target interactions. Combining machine learning models with molecular docking and Molecular Dynamics (MD) simulations worked well in finding promising phytochemical inhibitors. The MM/GBSA binding free energy calculations further confirmed binding affinities, with values of −10.83 and −11.08 kcal/mol, respectively, suggesting favorable complex stability. These findings provide a pathway for developing new antiviral agents against MARV, pending further experimental validation and optimization. Full article

Drawing on moral cleansing theory, this study adopts an actor-centered perspective to examine how ingratiation relates to employees’ moral rumination and subsequent coworker-directed cooperative behavior, thereby offering insights to help organizations to understand and guide such behaviors. Using a multi-wave survey design, this study collected data from 272 employees to examine a theoretical model investigating how employee ingratiation influences coworker-directed cooperative behavior through moral rumination, while also examining the moderating role of employee moral identity. The results indicate that employee ingratiation positively influences moral rumination, which in turn enhances coworker-directed cooperative behavior. Furthermore, the indirect effect of ingratiation on coworker-directed cooperative behavior via moral rumination is strengthened among employees with high moral identity. This study advances the literature by shifting the focus from targets and observers to actors themselves, examining how ingratiation shapes actors’ own moral perception and subsequent behavior. It further contributes by introducing moral rumination as a mediating mechanism and exploring the moderating effect of moral identity, as well as offering new insights into ingratiation in organizational contexts. Full article

This paper develops an optimal control framework for a mechanical–structural model of bone remodeling that couples osteocytes, osteoblasts, and osteoclasts with bone density, incorporating porosity-dependent feedback mechanisms. To represent clinically relevant interventions, three bounded control functions are introduced: anabolic stimulation of osteoblast activity, anti-resorptive suppression of osteoclast-mediated resorption, and structural modulation of porosity feedback. The controlled system is shown to be mathematically well-posed, and the necessary optimality conditions are derived via Pontryagin’s Maximum Principle, leading to explicit characterizations of the optimal controls. The resulting state–adjoint system is solved numerically using a forward–backward sweep method. Numerical results demonstrate that the optimal intervention effectively suppresses osteoclast activity and drives the system toward higher, more stable bone density levels than the uncontrolled dynamics. In particular, the anti-resorptive control consistently plays the dominant role in shaping the optimal strategy. A cost-effectiveness analysis based on ACER, ICER, and the efficient frontier shows that strategies involving anti-resorptive inhibition achieve the greatest therapeutic gains at moderate cost, while additional controls yield only marginal improvements. Sensitivity analysis further indicates that parameters associated with osteoclast dynamics and bone formation have the strongest influence on density-related outcomes. Full article

Anxiety disorders are characterized by dysregulation of monoaminergic signaling and remain a significant therapeutic challenge due to limitations associated with current pharmacological treatments. In this context, the essential oil of Morisonia flexuosa (Capparaceae) seeds was chemically characterized and evaluated for anxiolytic-like activity in adult zebrafish. Chemical profiling by GC–MS and GC–FID revealed a predominance of isothiocyanates, particularly butyl isothiocyanate (42.60%) and isobutyl isothiocyanate (42.28%). Acute toxicity assessment demonstrated no lethality at the tested doses. Behavioral analyses showed a significant increase in light preference in the light/dark paradigm, with moderate locomotor reduction insufficient to account for the behavioral shift solely by sedation. Pharmacological antagonism assays indicated that the anxiolytic-like effect was predominantly mediated by 5-HT1 and 5-HT2A/2C receptors. Chemometric analyses (PCA, HCA, and heatmap) revealed statistical association between compound abundance and behavioral endpoints, supporting the contribution of major isothiocyanates within the tested model. Notably, the strongest behavioral response was observed at the lowest concentration, suggesting an ideal effective concentration range. Collectively, these findings provide the first evidence that an isothiocyanate-rich essential oil from M. flexuosa exerts serotonergic-involved anxiolytic-like effects in zebrafish and supports further mechanistic investigation of its neuropharmacological potential. Full article

Excitotoxicity caused by excessive activation of glutamate receptors, particularly N-methyl-D-aspartate receptors (NMDARs), significantly contributes to neuronal damage in neurodegenerative diseases (NDDs), such as Alzheimer’s, Parkinson’s, and Huntington’s diseases. This systematic review aimed to evaluate the effects of plant extracts and phytochemicals on NMDAR-mediated excitotoxicity and to summarize their proposed neuroprotective mechanisms. The review protocol was registered in PROSPERO (CRD42024528160). A systematic search of Medline, Embase, Web of Science Core Collection, and PubMed identified 323 records, with an additional 7 records identified through manual searching that specifically considered in vitro and in vivo inhibitors of NMDAR excitotoxicity using plant extracts and isolated phytochemicals. Twenty-seven studies demonstrated that plant extracts and phytochemicals attenuate excitotoxicity through multiple mechanisms, including inhibition of NMDAR-induced currents, reduction of intracellular calcium influx, modulation of NMDAR expression, attenuation of oxidative stress, and mitochondrial dysfunction. However, the evidence base was largely dominated by in vitro and ex vivo studies, with limited in vivo validation, restricting translational relevance. Risk-of-bias assessment using an adapted version of the Office of Health Assessment and Translation (OHAT) Risk of Bias Tool indicated that 4 studies had a low overall risk of bias, 12 had low to moderate risk, and 11 were at moderate risk, with key limitations related to inadequate reporting of blinding, randomization, and allocation concealment. In contrast, exposure characterization, outcome assessment, and confounding control were generally strong across studies. Although the findings support the mechanistic neuroprotective potential of certain plant extracts and phytochemicals against NMDAR-mediated excitotoxicity, further well-designed in vivo and clinical studies are required to establish their therapeutic relevance for the treatment of neurodegenerative diseases. Full article

Digital transformation has become a strategic pillar of modernization in public administration; however, evidence from emerging economies shows that technology implementation alone does not guarantee institutional transformation. This study examines the structural relationships among digital competencies, change management, technology adoption, and digital transformation in public administration institutions in Lima, Peru, incorporating Digital Inclusion Practices (DIP) as a moderating variable. A quantitative, non-experimental, cross-sectional design was applied to a sample of 358 public servants working in ministries, national agencies, regional administrative units, and municipal governments located in Lima. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that digital competencies and change management are positively associated with technology adoption, and that change management and technology adoption show significant positive relationships with digital transformation. Technology adoption partially mediates the relationships between digital competencies and digital transformation, and between change management and digital transformation. Additionally, digital competencies show a direct and statistically significant, although weak, relationship with digital transformation. The moderating relationship involving DIP was not statistically significant. These findings suggest that digital governance in emerging public administrations may depend less on individual skills alone and more on structured institutional processes that support effective technology adoption, strategic change management, and institutional modernization. Full article

Artificial intelligence has been effectively integrated into every stage of undergraduate research, significantly improving students’ learning efficiency. Against this backdrop, artificial intelligence is no longer merely an optional external tool, but has become an extension of college students’ personal capabilities—that is, “AI identity.” The study constructs a moderated dual-mediation model from the perspective of ambidextrous learning. Moving beyond prior work on AI usage frequency or literacy, this study centers on AI identity and reveals the double-edged effect of AI identity on research creativity, with a positive indirect effect via exploratory learning and a negative indirect effect via exploitative learning, along with the asymmetric moderating role of ethical dilemmas on these two pathways. Using questionnaire surveys analyzing 451 college student responses, the results demonstrate that AI identity positively correlates with research creativity, where exploratory learning serves as a positive mediator while exploitative learning acts as a negative mediator. Ethical dilemmas moderate the relationship between AI identity and ambidextrous learning. These findings provide actionable insights for higher education institutions to foster students’ exploratory AI use, mitigate overreliance, and establish ethical governance frameworks for AI-assisted research, thereby assisting universities in guiding students toward developing a healthy understanding of AI identity and refining ethical guidelines for AI applications. Full article

In this study, a novel protein glutaminase derived from Chryseobacterium lactis CGMCC 33780 (CLPG) was successfully purified via a one-step cell surface display approach, yielding its mature form. Subsequently, the enzymatic properties of CLPG were characterized. It exhibited optimal activity at a pH of 5 and a reaction temperature of 50 °C, and retained over 70% of its activity after a 12 h incubation at 50 °C. The study further investigated the impact of CLPG-mediated deamidation on the structural and functional attributes of krill protein isolate (KPI). A comprehensive analysis was conducted on the deamidation extent, conformational alterations, and microstructural morphology of KPI, employing techniques such as FTIR, CD, DSC, and SEM. After deamidation treatment with CLPG, the foaming and emulsifying properties of KPI were moderately shifted. When the CLPG dosage was 1.0 U/g with a corresponding deamidation degree of 15.18%, the emulsifying property of KPI reached the maximum value of 23%. These property enhancements were possibly primarily attributed to the increased electrostatic repulsion and hydrophobicity induced during the deamidation process. This work not only pioneers a novel method for the expression and purification of protein glutaminase but also applies it to the modification of krill protein, offering fresh insights for the development and application of protein glutaminases. Full article

Professionals working in family and social services are frequently exposed to emotionally demanding interpersonal experiences, which may influence both their occupational well-being and their perceptions of close relationships. This study was conducted to examine the mediating role of compassion fatigue in the relationship between emotional intelligence and marital anxiety among unmarried mental health professionals in Türkiye. The sample consisted of 311 unmarried mental health workers, including psychologists, social workers, and psychological counselors employed in provincial directorates of the Ministry of Family and Social Services. Data were collected using the Trait Emotional Intelligence Questionnaire—Short Form (TEQue-SF), the Compassion Fatigue—Short Scale, and the Marital Anxiety Scale. Mediation analysis was conducted using PROCESS Macro Model 4. The findings revealed that emotional intelligence negatively predicted compassion fatigue. Emotional intelligence also negatively predicted marital anxiety, while compassion fatigue did not directly predict marital anxiety. Mediation analysis revealed that compassion fatigue played a significant moderate mediating role in the relationship between emotional intelligence and marital anxiety. These findings suggest that occupational emotional experiences may be indirectly associated with relationship-related concerns among unmarried mental health professionals. The results highlight the importance of considering both emotional intelligence and compassion fatigue in understanding marital anxiety and supporting the development of training, supervision, and psychoeducational interventions aimed at strengthening emotional regulation and professional well-being. Future research including both unmarried and married professionals, as well as longitudinal and mixed-method designs incorporating qualitative interviews, may further clarify these relationships and the mechanisms underlying them. Full article

Exposure to electromagnetic fields (EMFs) at different frequencies has been reported to induce apoptotic changes in brain tissue. Apoptosis is commonly evaluated using the TUNEL (terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling) method; however, conventional semi-quantitative scoring is subjective and may vary between observers. Therefore, this study aimed to evaluate the feasibility of transfer learning-based convolutional neural network (CNN) models for the objective and automated classification of apoptotic damage in TUNEL-stained brain sections. A total of 92 light microscopy images of TUNEL-stained rat brain tissue, obtained from experimental groups, were analyzed. Apoptotic damage was categorized into three classes (0: no, +1: slight, +2: moderate) based on semi-quantitative scoring. Pre-trained convolutional neural network models, including AlexNet, SqueezeNet, GoogLeNet, Inception-v3, and ResNet-101, were applied for image classification. All models were able to classify apoptotic damage levels, defined by the extent of TUNEL staining, from images with varying performance. The best-performing model achieved high classification accuracy and demonstrated strong agreement with manual scoring, as determined by visual assessments by experts. The models successfully distinguished between different levels of apoptotic damage observed across experimental groups. The findings suggest that transfer learning-based CNN models may provide an objective and reproducible approach for the classification of apoptotic damage in TUNEL-stained histopathological images, thereby reducing observer-dependent variability. This approach may also support histopathological evaluation in experimental models, including studies investigating EMF-induced brain injury. Full article