Achieving both high power output and energy efficiency poses a significant challenge in optimizing membraneless microfluidic fuel cells (MMFCs) due to the inevitable trade-off between fuel utilization and power density. This study aimed to introduce an effective method to enhance power density (P), fuel utilization (Fu), and exergy efficiency (Ee) in an MMFC with flow-through porous electrodes through deep learning-based multi-target and criteria surrogate optimization (MTCO). We developed an experimentally validated 3D numerical model to train a deep-learning artificial neuron network (DNN), achieving high prediction accuracy (R2 = 0.999). An MTCO model was constructed with the latest non-dominated sorting genetic algorithm (NSGA-III) and a comprehensive multiple-criteria decision-making model (PROMETHEE-II). This model identified an optimal combination of input parameters that could achieve high P (121.266 mW/cm2), Fu (98.445
Psychological capital (PsyCap) plays an important role in increasing the learning capacity, professional skills, problem-solving, and innovation in the work of employees (Luthans et al., 2013). In developing countries, for example, Vietnam, research on PsyCap has not been mentioned much or fully. It extends beyond social capital and human capital to create profitability. The objective of this paper is to study the impact of PsyCap on the work engagement of sales staff in real estate enterprises in Vietnam. PsyCap includes the factors: 1) confidence, 2) optimism, 3) hope, and 4) resiliency. The research method we used is quantitative, with a sample size of 453 real estate sales staff in Vietnam. The research model is based on the theory of PsyCap and work engagement. The results of the study showed that resiliency had the strongest impact on dedication and passion at work, while confidence and optimism also had a positive effect, but to a lesser extent. However, factors such as hope did not have a significant impact. The study provides practical suggestions for real estate firms in developing training programs to enhance employees’ PsyCap to increase engagement and work performance.
In this work, four coumarin-based Zn(II) complexes, Zn-CT6, Zn-CT7, Zn-CT8, and Zn-NCT, were synthesized. The complexes exhibit ligand-dependent solid-state emission ranging from cyan to red, whereas appreciable solution fluorescence is mainly observed for Zn-NCT. TD-DFT calculations support the assignment of the dominant absorption bands and provide qualitative insight into the locally excited and charge-transfer characters of the electronic transitions. Biological evaluation revealed that Zn-CT7 and Zn-CT8 display moderate cytotoxicity toward A549, MCF-7 and HepG2 cancer cells (IC50 similar to 10 & micro;M). DNA-binding studies indicate that the complexes interact with calf thymus DNA through a predominantly non-intercalative binding mode, most plausibly groove-associated binding. Additional cellular assays suggest that Zn-CT8 induces intracellular ROS generation, mitochondrial membrane potential (MMP) depletion, and apoptosis-associated morphological changes. Exploratory docking and 300 ns molecular dynamics simulations against the GLP-1 receptor complex provide qualitative structure-based hypotheses for future target-validation studies. To improve aqueous dispersibility, a Tween-80 micellar formulation of Zn-CT8 was prepared and physicochemically characterized. Finally, Zn-NCT showed efficient cellular uptake and preferential endoplasmic-reticulum/mitochondrial localization in HeLa cells, supporting its utility as an intracellular fluorescent probe. Overall, these results establish ligand-dependent relationships among structure, photophysics, DNA interaction, cytotoxicity, and cellular imaging behavior in coumarin-based Zn(II) complexes.
Artificial intelligence (AI) and machine learning (ML) have been increasingly adopted in the banking sector due to their ability to analyze large-scale datasets, process complex variables, and uncover hidden patterns, especially in the context of liquidity risk, posing a significant challenge for commercial banks. This study contributes to the field by conducting a comprehensive evaluation of several widely used early warning models, such as least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and extreme gradient boosting (XGBoost), to identify the most suitable approach for forecasting liquidity risk in Vietnamese commercial banks (VCBs) based on VCBs data over the period of 2014–2023. By pinpointing key indicators associated with liquidity crises, these models can assist banks and regulatory authorities in implementing timely preventive measures and enhancing risk management strategies. As a result, the RF model outperforms other methods in identifying possible liquidity crises, according to the empirical results, with an accuracy rate of 99.8 percent. These findings provide bank managers and policymakers with a powerful tool for timely preventive measures, thereby enhancing the resilience and stability of the financial system.
Human capital theory suggests that investments in education and training improve workers’ productivity, employability, and economic value (Fadila et al., 2025; Al-Hmesat et al., 2025). This study investigates the impact of overall training quality outcomes on job readiness and employment market fit for the Japanese labor market in the context of digital transformation and artificial intelligence (AI). Drawing on human capital theory, signaling theory, and employability theory, the study examines how training quality enhances workforce preparedness and alignment with employer expectations. Data were collected from 360 respondents, including trainees and stakeholders involved in Japanese labor-market training programs in Hanoi, Vietnam. The proposed relationships were analyzed using Cronbach’s alpha, exploratory factor analysis (EFA), correlation analysis, and multiple regression analysis. The findings indicate that overall training quality outcomes have significant positive effects on both job readiness and employment market fit. High-quality training improves occupational competence, workplace discipline, communication skills, adaptability, and workforce integration, thereby enhancing trainees’ employability in Japanese enterprises. The study extends previous research by simultaneously examining training quality, job readiness, and employment market fit within an AI-driven labor-market context. The findings provide practical implications for vocational training institutions, labor-export enterprises, and policymakers seeking to strengthen workforce quality, improve labor-market competitiveness, and promote sustainable international labor mobility.
