Natural Protected Areas (NPAs) are critical for biodiversity conservation and ecological balance. These areas are not only refuges for wildlife but also pivotal in promoting sustainable tourism. Geoparks, a unique subset of NPAs, emphasize geological heritage, offering distinctive educational and recreational opportunities. This article explores the significance of Geoparks in Portugal for geotourism and assesses the accessible digital communication strategies of Portuguese Geoparks, emphasizing the analysis of pedagogical concerns. The study highlights the importance of online engagement in enhancing visitor experiences and promoting sustainable tourism practices.

The physical-mechanical characteristics of leather are crucial in the tanning industry since they determine whether the leather satisfies quality standards for various product manufacture. This study's goal was to assess the physical-mechanical characteristics of leather that could be washed and used for garments after the Zetestan-GF polymer was added during the tanning process. The data gathered from the physical-mechanical analysis of two treatments—one a control with white leather (T1) and the other with leather treated with Zetestan-GF polymer (T2)—were compared for the development of this work. Each treatment was performed in triplicate, undergoing three washes, yielding a total of 24 samples for analysis. Following the acquisition of the leather, a control was applied and the various treatments were compared. SAS software version 9.0 was utilized for the data's statistical analysis. The physical-mechanical properties of the control leather and the leather treated with Zetestan-GF polymer were compared using a one-way ANOVA, and any differences in the means (p < 0.05) were assessed using the Tukey test. The findings showed that while the polymer's application during the tanning process affects the parameters of softness, tensile strength, elongation percentage, and dry and wet flexometry, it has no effect on the lastometry parameter. In conclusion, the physical-mechanical characteristics of the product made by tanning cow hides can be greatly impacted by the inclusion of a polymer.

The livelihood of ethnic minority households in Vietnam is mainly in the fields of agriculture and forestry. The percentage of ethnic minorities who have jobs in industry, construction, and services is still limited. Moreover, due to harsh climate conditions, limited resources, poor market access, low education level, lack of investment capital for production, and inadequate policies, job opportunities in the off-farm and non-farm activities are very limited among ethnic minority areas. This paper assessed the contribution of livelihood diversification activities to poverty reduction of ethnic minority households in Son La Province of Vietnam. The analysis was based on the data using three stages sampling procedure of 240 ethnic minority households in Son La Province. The finding showed that the livelihood diversification activities had positively significant contribution to poverty reduction of ethnic minority households in Son La Province. In addition, the factors positively affecting the livelihood choices of ethnic minority households in Son La Province of Vietnam are education level, labor size, access to credit, membership of associations, support policies, vocational training, and district. Thus, improving ethnic minority householder’s knowledge through formal educational and training, expanding availability of accessible infrastructure, and enhancing participation of social/political associations were recommended as possible policy interventions to diversify livelihood activities so as to mitigate the level of poverty in the study area.

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.