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.

The purpose of this research is to present a bibliometric analysis of the literature on the ways in which the motivations of individual sports consumers impact the creation of sports infrastructure and the creation of sports-related policy. Design/methodology/approach: Based on the PRISMA approach and information gleaned from the Scopus database, 2605 publications were found to be pertinent to the subject. We conducted a literature analysis of trends and patterns using VOSviewer-based knowledge mapping. Findings: Recent years have seen a proliferation of scholarly publications on the topic of individual sports consumption motivation and its influence on policy formulation and infrastructure development. This suggests that interest in this field is expanding. The list of eminent journals, decision-makers, and organizations involved in this issue demonstrates its global influence. The interdisciplinary nature of the subject is reflected in the study’s emphasis on the most widely published authors and key research terminology. Originality/value: This study closes significant knowledge gaps regarding the complex interactions between societal, environmental, and individual factors that affect the motivation to consume sports and how these motivations influence decisions about sports infrastructure and policies. It does this by using bibliometric techniques and the most recent data. The project aims to create a more thorough picture of how public health policy, sports governance, and urban planning are impacted by the motivations behind sports consumption. Policy implications: Policymakers, planners, and sports organizations can use the results to generate more targeted and effective strategies for the development of sports infrastructure and policy formulation. The study highlights how important it is to make well-informed policy decisions and participate in customized involvement in order to improve public welfare and the overall sports consumer experience.

Studies to evaluate the response of passion fruit seedlings in terms of emergence, nursery, and early field growth to growing media and mulching were carried out at the Teaching and Research Farm of Joseph Sarwuan Tarka University Makurdi between July and December 2018. Treatments consisted of five media, composted from readily available substrates. The five nursery media were; medium 1:1:2:3 (SB) composed of top soil + poultry manure + river sand; medium 2:1:2:3 (RHB) – rice hull + poultry manure + river sand; medium 3:2:3:1 (RHB) – rice hull + poultry manure + river sand; medium 4:1:4:3 (SDB) – sawdust + poultry manure + river sand and medium 5:1:2:3 (SDB) – sawdust + poultry manure + river sand. For the nursery experiment, treatments were the five potting media, while the field trial was a 5 × 2 factorial arrangement consisting of the five growing media and mulching status (mulch and no mulch). In both cases, treatments were laid out in randomized designs that were replicated three times. Results showed that there were no significant differences in all the emergence traits evaluated. However, medium M5 (sawdust based) showed superior performance in most of the seedling characters evaluated. Under field conditions, the sawdust based media (M4 and M5) gave the best growth of passion fruit seedlings compared to the other potting media. Application of mulch, however, did not elicit any significant response in plant growth. It is therefore conclusive that sawdust based growing media could be used to produce high quality passion fruit seedlings with the prospect of excellent performance under field conditions.

This article explores the application of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework in the context of integrating self-driving tractors into agricultural practices. With a focus on understanding the factors influencing the acceptance and adoption of this transformative technology, we delve into the implications for farmers, industry stakeholders, and the future of sustainable agriculture and rural tourism.

Facing the digital economy era, considerable attention is paid to the importance of understanding the fundamental impact on the information and development of blended teaching methods regarding the higher education. For this reason, the purpose of this study is to answer the challenges brought by the digital economy era, identify the effective teaching methods which would be used in English Correspondence course in the era of digital economy, aiming to form the patterns of learning, provide high motivation, strength and knowledge, and most importantly contribute to the complex competences of future working. For further research, it is expected to be able to prove that using the blended teaching methods will effectively improve students’ communication skills and learning efficiency, enhance students’ learning experience and critical thinking skills.