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.

This study aims to investigate what influences local workers over the age of 40 to work and stay employed in oil palm plantations. 414 individuals participated in a face-to-face interview that provided the study’s primary source of data. Exploratory Factor Analysis was used to analyse the given data. The study revealed that factors influencing local workers over the age of 40 years to leave or continue working in oil palm plantations can be classified as income factors, internal factors and external factors. The income factor was the most significant factor as the percentage variance explained by the factor was 26.792% and Cronbach Alpha was high at 0.870. Therefore, the study suggested that the oil palm plantation managements pay more attention to income elements such as basic salary, wage rate paid to the workers and allowance given to the workers since these elements contribute to the monthly total income received by the workers and in turn be able to attract more local workers to work and remain in the plantations.

This study investigates the relationship between hydrological processes, watershed management, and road infrastructure resilience, focusing on the impact of flooding on roads intersecting with streams in River Nile State, Sudan. Situated between 16.5° N to 18.5° N latitude and 33° E to 34° E longitude, this region faces significant flooding challenges that threaten its ecological and economic stability. Using precise Digital Elevation Models (DEMs) and advanced hydrological modeling, the research aims to identify optimal flood mitigation solutions, such as overpass bridges. The study quantifies the total road length in the area at 3572.279 km, with stream orders distributed as follows: First Order at 2276.79 km (50.7%), Second Order at 521.48 km (11.6%), Third Order at 331.26 km (7.4%), and Fourth Order at 1359.92 km (30.3%). Approximately 27% (12 out of 45) of the identified road flooding points were situated within third- and fourth-order streams, mainly along the Atbara-Shendi Road and near Al-Abidiya and Merowe. Blockages varied in distance, with the longest at 256 m in Al-Abidiya, and included additional measurements of 88, 49, 112, 106, 66, 500, and 142 m. Some locations experienced partial flood damage despite having water culverts at 7 of these points, indicating possible design flaws or insufficient hydrological analysis during construction. The findings suggest that enhanced scrutiny, potentially using high-resolution DEMs, is essential for better vulnerability assessment and management. The study proposes tailored solutions to protect infrastructure, promoting sustainability and environmental stewardship.

The proportion of elderly people is growing steadily in many countries, and this trend is expected to continue. As a result, ageism—negative discrimination often tied to perceptions of the elderly—becomes especially harmful. Ageism prevents older generations from being fully accepted by society and, in turn, hinders their ability to adapt to today's technological changes. In this article, we present the results of our survey mapping the extent of ageism among youth in Uzbekistan, known for its cultural tolerance in Central Asia, and in Hungary, a more individualistic society in Central Europe. To interpret the survey results accurately, we included specific questions to measure social desirability bias, enabling a realistic comparison of ageism levels between the two countries. Data was collected through a survey translated into multiple languages, with a final sample of nearly 400 respondents, each either currently pursuing or already holding a college-level diploma. Our methodological approach was twofold. First, we conducted simple chi-square tests to compare levels of negative and positive ageism between the two countries under study. Upon finding significant differences, we used multivariable OLS regression to explain the variance in types of ageism in Uzbekistan and Hungary, accounting for the possible effects of social desirability bias. Uzbek youth demonstrated higher levels of positive ageism and lower levels of negative ageism compared to Hungarian youth. This finding confirms that the cultural tolerance in Uzbek society remains strong and, in many ways, could serve as a model for Hungary. Additionally, our literature review highlights that adequate infrastructure is essential for a society to treat older adults equitably alongside other citizens.

In the current era of globalization, the need arises to train individuals who are spiritually enriched, creatively developed, and culturally grounded through the advancement of education and science, as well as through art and culture. These individuals must be capable of integrating artistic creativity into their professional activities. In this context, the issue of fostering values of historical and cultural significance through virtual reality technologies emerges as a novel area of research. The study aims to reveal the essence of the concept "virtual museum" and test the level of perspective art teachers' readiness for utilizing the virtual museum in their professional activity to foster their cultural values of artic creativity. Employing quantitative and qualitative methods, the study encompassed questionnaires, tests, and assignments administered to 135 university students divided into control and experimental groups. To diagnose students' readiness to utilize virtual museum technology in their professional activities, three components (motivational, cognitive, and operational), criteria, indicators and levels of readiness were identified. Findings indicate that there is a noticeable difference between the experimental group's results before and after completing the authors' elective course titled "Methodology of using the virtual museum". This demonstrates the effectiveness of this course conducted with the experimental group. The study highlights the importance of perspective art teachers' acquisition of knowledge, skills and competences necessary to implement the virtual museum method in their teaching activity through the proposed elective course incorporated into the university educational process in order to foster students' cultural values of artic creativity.

This study investigates the performance assessment of methanol and water as working fluid in a solar-powered vapour absorption refrigeration system. This research clarifies the system’s performance across a spectrum of operating conditions. Furthermore, the HAP software was utilized to determine and scrutinize the cooling load, facilitating a comparative analysis between software-based results and theoretical calculations. To empirically substantiate the findings, this research investigates methanol-water as a superior refrigerant compared to traditional ammonia- water and LiBr-water systems. Through experimental analysis and its comparison with previous research, the methanol-water refrigeration system demonstrated higher cooling efficiency and better environmental compatibility. The system’s performance was evaluated under varying conditions, showing that methanol-water has a 1% higher coefficient of performance (COP) compared to ammonia-water systems, proving its superior effectiveness in solar-powered applications. This empirical model acts as a pivotal tool for understanding the dynamic relationship between methanol concentration (40%, 50%, 60%) and system performance. The results show that temperature of the evaporator (5–15 ℃), condenser (30 ℃–50 ℃), and absorber (25 ℃–50 ℃) are constant, the coefficient of performance (COP) increases with increase in generator temperature. Furthermore, increasing the evaporator temperature while keeping constant temperatures for the generator (70 ℃–100 ℃), condenser, and absorber improves the COP. The resulting data provides profound insights into optimizing refrigerant concentrations for improved efficiency.