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 marginalized ecosystem-dependent rural communities, access to ecosystem services plays a crucial role in achieving sustainable livelihoods. This study was conducted to find out the influence of various livelihood capital components on the access mechanism for forest-based Provisioning Services (PS) in some selected villages of the Gosaba Block on the fringes of the Sundarban. The contribution of the livelihood capitals to gain access to Provisioning Services (PS) was identified using factor analysis on 160 households, selected through cluster random sampling. The sustainability levels of livelihood capitals were analyzed using the Prescott-Allen method (2001). The natural, financial, social, and physical capitals were significantly below average, while the human capital was close to average. Enhancement of human, physical, financial, and social capital, ease in issuing Biometric Fisherman cards for entering forests, flexibility in borrowing loans, and ecotourism by involving local villagers must be encouraged to enhance forest-based provisioning services in the near future.

Diabetic retinopathy (DR) is a major cause of blindness globally. Effective screening programs are essential to mitigate this burden. This review outlines key principles and practices in implementing DR screening programs, emphasizing the roles of technology, patient education, and healthcare system integration. Our analysis highlights key principles for establishing successful screening initiatives, including the importance of regular screenings, optimal intervals, recommended technologies, and necessary infrastructure. We emphasize the roles of healthcare providers, patients, and policymakers in ensuring the effectiveness of these programs. Our recommendations aim to support the creation of robust policies that mitigate the impact of DR, ultimately improving public health outcomes and reducing the incidence of blindness due to diabetic retinopathy.

A reservoir of vegetation, wildlife, and medicinal plant abundance is represented by the Haridwar forest divisions. This study deals with the results of ethnobotanical survey of medicinal plants conducted in the Haridwar forest division during the period of December 2016 and March 2019. The information on folk medicinal use of plants were gathered by interviewing with local healers and Vaidya’s who have long been advising the folk medicines for medication of various disorders. The important folk medicinal data of 33 medicinal plants species belonging to 22 families and 33 genera practiced by tribal and local people of the study area has been recorded by the survey team of the Institute. Fabaceae followed by the Lamiacea and Asteraceae were the dominant families. The species diversity showed maximum exploration of Trees, Herbs followed by Shrubs and Climbers. Leaves, seed and root were the most prevalently used part in study followed by the stem bark, fruit, flower, stem and fruit pulp. During the study it was observed that the traditional practices of Gujjars of Uttarakhand have close relation with forests and have strong dependency on the same for food, medicine, timber and fodder etc. The information recorded for the treatment in different ailments has been presented in the paper in the pie charts and tabular form. In the recorded information most of the plants along with Plant name, Family name, Voucher Specimen No., Local Name/Unani name, Part Used, Diseases/Condition and Habitat/ICBN status so as to enrich the existing knowledge on ethnopharmacology. Many of the medications used today have their roots in traditional knowledge of medicinal plants and indigenous uses of plant material, and there are still a plethora of potentially useful pharmaceutical chemicals to be found. In this regard, more in-depth field research could aid in the discovery of novel plant species utilized in indigenous medical systems to improve patient needs. With this aim this study was conducted to explore and trace the ethnobotanical potential of flora of the Haridwar forest division so that it could prove to be immensely advantageous for both the development of new medications to treat dreadful and catastrophic illnesses as well as for the study and preservation of cultural and social variety.

Over the course of many years, the Mekong Delta region has experienced relatively low and inconsistent levels of business attraction and low quality of the enterprise environment compared to other regions in Vietnam. To delve into whether this discrepancy reflects a negative perception of the business environment in the area, this study employs a dataset comprising the aggregate Provincial Competitiveness Index (PCI) and nine of its component scores, alongside other significant control variables, to analyze business attraction trends spanning from 2010 to 2020. It based on the modeling analysis for the panel data that includes Pool-OLS, FEM and REM models. The findings indicate that PCI serves as an important indicator influencing the quality of the business environment and plays a role in determining the location preferences of businesses. It is observed that public investment has exerted an impact on enticing new businesses to the region throughout this period. These research outcomes carry several policy implications, suggesting that public policy interventions can positively shape the business environment, consequently bolstering the appeal of business investments in the region.