The aim of this study was to elucidate the expected moderating effect exerted by institutional owners on the intricate correlation between the characteristics of boards of directors and the issue of earnings management, as gauged by the loan loss provisions.The sample encompassed all the banks listed on the Amman Stock Exchange (ASE) over the period between 2010 and 2022, representing a total of 151 observations. The results derived from the examination clearly demonstrate that the institutional owners have a key impact on augmenting the monitoring tasks and responsibilities of the boards of directors across the study sample. The results revealed the fundamental role of such owners in strengthening the supervisory tasks carried out by boards of directors in Jordan. A panel data model has been used in the analysis. The results of this study show that the presence of the owner of an institution has a discernible moderating role in the banks' monitoring landscape. Indeed, their presence strengthens the monitoring tasks of the banks’ boards by underscoring the quest to restrict the EM decisions. Interestingly, the results support the monitoring proposition outlined by agency theory, which introduced CG recommendations as a deterrent tool to reduce the expectation gap between banks' owners and their representatives.

Mangrove forests are vital to coastal protection, biodiversity support, and climate regulation. In the Niger Delta, these ecosystems are increasingly threatened by oil spill incidents linked to intensive petroleum activities. This study investigates the extent of mangrove degradation between 1986 and 2022 in the lower Niger Delta, specifically the region between the San Bartolomeo and Imo Rivers, using remote sensing and machine learning. Landsat 5 TM (1986) and Landsat 8 OLI (2022) imagery were classified using the Support Vector Machine (SVM) algorithm. Classification accuracy was high, with overall accuracies of 98% (1986) and 99% (2022) and Kappa coefficients of 0.97 and 0.98. Healthy mangrove cover declined from 2804.37 km2 (58%) to 2509.18 km2 (52%), while degraded mangroves increased from 72.03 km2 (1%) to 327.35 km2 (7%), reflecting a 354.46% rise. Water bodies expanded by 101.17 km2 (5.61%), potentially due to dredging, erosion, and sea-level rise. Built-up areas declined from 131.85 km2 to 61.14 km2, possibly reflecting socio-environmental displacement. Statistical analyses, including Chi-square (χ2 = 1091.33, p < 0.001) and Kendall's Tau (τ = 1, p < 0.001), showed strong correlations between oil spills and mangrove degradation. From 2012 to 2022, over 21,914 barrels of oil were spilled, with only 38% recovered. Although paired t-tests and ANOVA results indicated no statistically significant changes at broad scales, localized ecological shifts remain severe. These findings highlight the urgent need for integrated environmental policies and restoration efforts to mitigate mangrove loss and enhance sustainability in the Niger Delta.

This study examines the determinants of audit quality and their impact on detecting financial statement fraud at public accounting firms member of OAI Solusi Manajemen Nusantara in Indonesia. Using a quantitative approach, data was collected through a structured questionnaire distributed to auditors and staff. Key findings highlight the significant influence of auditor independence, professional proficiency, and supervision actions on conducting effective audits, thereby enhancing fraud detection capabilities. The research identifies challenges such as the focus on Indonesian firms and potentially limiting broader applicability. Recommendations include enhancing auditor training, adopting stringent audit procedures and technology, and ensuring adherence to auditing standards to improve audit quality and uphold financial reporting integrity. This study underscores the critical role of audit quality in preventing and detecting financial statement fraud, suggesting avenues for future research to explore additional influencing factors.

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.