Two-dimensional hexagonal boron nitride nanosheets (h-BNNS) were synthesized on silver (Ag) substrates via a scalable, room-temperature atmospheric pressure plasma (APP) technique, employing borazine as a precursor. This approach overcomes the limitations of conventional chemical vapor deposition (CVD), which requires high temperatures (>800 °C) and low pressures (10⁻² Pa). The h-BNNS were characterized using FT-IR spectroscopy, confirming the presence of BN functional groups (805 cm⁻¹ and 1632 cm⁻¹), while FESEM/EDS revealed uniform nanosheet morphology with reduced particle size (80.66 nm at 20 min plasma exposure) and pore size (28.6 nm). XRD analysis demonstrated high crystallinity, with prominent h-BN (002) and h-BN (100) peaks, and Scherrer calculations indicated a crystallite size of ~15 nm. The coatings exhibited minimal disruption to UV-VIS reflectivity, maintaining Ag’s optical properties. Crucially, Vickers hardness tests showed a 39% improvement (38.3 HV vs. 27.6 HV for pristine Ag) due to plasma-induced cross-linking and interfacial adhesion. This work establishes APP as a cost-effective, eco-friendly alternative for growing h-BNNS on temperature-sensitive substrates, with applications in optical mirrors, corrosion-resistant coatings, energy devices and gas sensing.

Using company size as a moderator, this article examines the MENA region’s gender balance on boards and how it influences capital structure. The study uses the Generalized Method of Moments (GMM) estimate technique to analyze data from a sample of 556 non-financial organizations across 10 MENA countries from 2010 to 2023. The results show that a lower debt ratio is connected with a higher percentage of female board members. Further steps towards debt reduction include increasing the number of independent female board members and decreasing the board’s overall size. The opposite is true for larger enterprises, more profitability, more expansion opportunities, and macroeconomic variables like inflation and GDP growth, which tend to raise the debt ratio. Capital structure decisions in the MENA area are influenced by gender diversity on boards and business characteristics. Therefore, Companies in the MENA area would do well to support initiatives that increase the representation of women on corporate boards. One way to achieve this goal is to establish gender diversity targets or launch programs to increase the number of women serving on boards of directors, particularly in positions of power.

This paper focuses on examining the relationship among organizational factor, work-related factor, psychological factor, personal factor and the commitment of oil palm smallholders toward Malaysian Sustainable Palm Oil (MSPO) certification. The study employed a descriptive research methodology and a structured survey instrument to gather data from oil palm smallholders (n = 441) through simple random sampling technique. Data analysis was conducted using SPSS and partial least square structural equation modeling (PLS-SEM) to test the proposed relationship. The findings reveal that organizational factors significantly impact the affective (β = 0.345, p < 0.05), normative (β = 0.424, p < 0.05), and continuance commitment (β = 0.339, p < 0.05) of oil palm smallholders. Additionally, work-related factors show a substantial effect on these same dimensions of commitment; affective (β = 0.277, p < 0.05), normative (β = 0.263, p < 0.05), and continuance (β = 0.413, p < 0.05). Psychological factors significantly impact the affective (β = 0.216, p < 0.05) and normative commitment (β = 0.146, p < 0.05), with no statistically significant influence on continuance commitment. Conversely, personal factors exhibit limited influence, affecting only continuance commitment (β = 0.104, p < 0.05) to a minor degree, with no statistically significant impact on affective and normative commitment. The present research is among the few empirical findings that have examined the oil palm smallholders’ commitment towards MSPO certification. By emphasizing the role of organizational and work-related factors, the study offers valuable insights for stakeholders within the oil palm sector, highlighting areas to enhance smallholder commitment toward sustainability standards. Consequently, this study contributes a unique perspective to the existing body of literature on sustainable practices in the oil palm industry.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

In the realm of contemporary business, Business Intelligence (BI) offers significant potential for informed decision-making, particularly among executives. However, despite its global popularity, BI adoption in Malaysia’s service sector remains relatively low, even in the face of extensive data generation. This study explores the factors influencing BI adoption in this sector, employing the Technology Acceptance Model (TAM) as its conceptual framework. Drawing on relevant BI literature, the study identifies key TAM factors that impact BI adoption. Using SEM modelling, it analyses quantitative data collected from 45 individuals in managerial roles within Malaysia’s service sector, particularly in the Klang Valley. The findings highlight the crucial role of Perceived Usefulness in influencing the Behavioral Intention to adopt BI, serving as a mediating factor between Computer Self-efficacy and BI adoption. In contrast, Perceived Ease of Use does not have a direct impact on BI adoption and does not mediate the relationship between Computer Self-efficacy and Behavioral Intention. These insights demonstrate the complex nature of BI adoption, emphasizing the importance of Perceived Usefulness in shaping Behavioral Intentions. The outcomes of the study aim to guide executives in Malaysia’s service sector, outlining key considerations for successful BI adoption.