University of Da Nang

This study explores how AI-Driven disclosure strategies on climate finance influence investors' perceptions and decisions, focusing on perceived sustainability value and authenticity. Based on 449 responses from Vietnam, using partial least squares structural equation modeling (PLS-SEM), the findings show five dimensions of AI-Driven climate disclosures positively affect perceived sustainability value and perceived authenticity. Sustainability value enhances financial sustainability and climate finance investment, while authenticity only predicts financial sustainability. Sustainable finance dynamics showed limited but surprising moderating effects. By integrating signaling theory and the SOR framework, the study contributes to emerging literature by demonstrating how AI-driven disclosure strategies can enhance investor understanding of climate information, support more credible sustainability signaling, and improve the effectiveness of climate-related financial decision-making. The findings offer implications for organizations and policymakers aiming to advance transparency, reduce information risk, and promote sustainable finance.

This work aims to design BODIPY-(Zn-porphyrin)2 conjugates that channel all harvested light energy into one conjugate moiety, providing a specific photoresponse with an extended excitation range. Three BODIPY-porphyrin conjugates have been prepared, and their excitation energy deactivation channels have been established. In two of them, the BODIPY pi-conjugation was extended at either the 2,6-positions or the 3,5-positions via Sonogashira coupling. Then the click reaction was applied to conjugate it to two Zn-porphyrins. The pi-conjugation extension shifts the BODIPY absorption spectra bathochromically, with the 3,5-substituted BODIPY spectra being more shifted compared to those of the 2,6-substituted ones. These BODIPY-(Zn-porphyrin)2 conjugates exhibit fluorescence spectra that are almost identical to those of the parent BODIPYs, independently of the excitation wavelength. The Zn-porphyrin transfers its own excitation energy to the BODIPY moiety and simultaneously acts as a BODIPY fluorescence quencher by enhancing the BODIPY radiationless transition rate in the conjugate. The third BODIPY-(Zn-porphyrin)2 conjugate was prepared by attaching two Zn-porphyrins via click reaction to azide groups in the meta-positions of the phenyl ring at the 8-position of BODIPY. Here, the BODIPY moiety harvests the excitation energy, which ultimately goes to the Zn-porphyrins with no traces of BODIPY emission. Notably, the intramolecular transition rates in the Zn-porphyrins remain unperturbed in the conjugate.

Patient safety remains a key issue in developing countries, where limitations in resources and safety systems may compromise the quality of care. Limited evidence exists on nurses' safety culture and competence in Vietnam. This study examined these aspects, their associated factors, and their interrelationship. A cross-sectional survey was conducted from September to December 2024 in three provincial hospitals in central Vietnam. Five hundred ninety-four registered nurses working in inpatient departments of three Level I provincial hospitals in central Vietnam completed two instruments: the Hospital Survey on Patient Safety Culture and the Health Professional Education in Patient Safety Survey. Statistical analyses involved descriptive methods, non-parametric procedures, Spearman's rank correlation, and generalized linear models. Findings revealed moderately favorable assessments of safety culture and high competence levels. Strong domains included teamwork, continuous learning, feedback on errors, and communication, whereas the lowest-scoring elements related to blame-oriented responses to mistakes, staffing adequacy, and handoff processes. Education level, department, night-shift frequency, and incident-reporting behavior all showed significant associations with both patient safety culture and competence. These results indicate the importance of developing strategies that concurrently enhance organizational culture and individual competence. Supportive leadership, adequate staffing, and continuous professional development may contribute to improvements in patient safety in hospitals. Future research should explore causal pathways and evaluate interventions that strengthen both safety culture and competence.

Transferring data in the mobile ad hoc network can be enabled to analyze data transferring and the network that manages the data and route them into the VPN-based routing. Here is the process of maintaining the gateway for the analysis. The main problem here is the routing of the data packets, and the analysis of the nodes in the form of packages is the main issue in this study. To fix this, troubleshooting problems can be enabled for the packets which reach the destinations and the echo response. The primary technique used in this study is energy efficient geographic routing protocol and reward-based intelligent Ad hoc routing is used for the analysis. The energy-efficient geographic routing protocol enables the EGRPM method to reduce the sensor nodes and the WSN. This allows gathering the data and the nodes to maintain the geographic way. Reward-based intelligent Ad hoc routing is used in automatic decision-making, and the analysis of the system to produce the selection action for the research is reinforcement learning. This results from the study of the configuration and the analysis of the data in the ad hoc network. This enables the formation of learning about the routing protocol and facilitates the current data transfer to the research done in the ad hoc networks. This data analysis in the mobile network helps analyze the system and the entire data management.

Interpretability is essential in Whole Slide Image (WSI) analysis for computational pathology, where understanding model predictions helps build trust in AI-assisted diagnostics. While Integrated Gradients (IG) and related attribution methods have shown promise, applying them directly to WSIs introduces challenges due to their high-resolution nature. These methods capture model decision patterns but may overlook class-discriminative signals that are crucial for distinguishing between tumor subtypes. In this work, we introduce Contrastive Integrated Gradients (CIG), a novel attribution method that enhances interpretability by computing contrastive gradients in logit space. First, CIG highlights class-discriminative regions by comparing feature importance relative to a reference class, offering sharper differentiation between tumor and non-tumor areas. Second, CIG satisfies the axioms of integrated attribution, ensuring consistency and theoretical soundness. Third, we propose two attribution quality metrics, MIL-AIC and MIL-SIC, which measure how predictive information and model confidence evolve with access to salient regions, particularly under weak supervision. We validate CIG across three datasets spanning distinct cancer types: CAMELYON16 (breast cancer metastasis in lymph nodes), TCGA-RCC (renal cell carcinoma), and TCGA-Lung (lung cancer). Experimental results demonstrate that CIG yields more informative attributions both quantitatively, using MIL-AIC and MIL-SIC, and qualitatively, through visualizations that align closely with ground truth tumor regions, underscoring its potential for interpretable and trustworthy WSI-based diagnostics