This study delves into the complex flow dynamics of magnetized bioconvective Ellis nanofluids, highlighting the critical roles of viscous dissipation and activation energy. By employing a MATLAB solver to tackle the boundary value problem, the research offers a thorough exploration of how these factors, along with oxytactic microorganism’s mobility, shape fluid behavior in magnetized systems. Our findings demonstrate that an increase in the magnetization factor  leads to a decrease in both velocity and temperature due to enhanced interparticle resistance from the Lorentz force. Additionally, streamline analysis reveals that higher mixed convection parameters  intensify flow concentration near surfaces, while increased slip parameters reduce shear stress and boundary layer thickness. Although isotherm analysis shows that higher Ellis fluid parameters enhance heat conduction, with greater porosity values promoting efficient thermal dissipation. These insights significantly advance our understanding of nanofluid dynamics, with promising implications for bioengineering and materials science, setting the stage for future research in this field.

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 efficiencies and performance of gas turbine cycles are highly dependent on parameters such as the turbine inlet temperature (TIT), compressor inlet temperature (T1), and pressure ratio (Rc). This study analyzed the effects of these parameters on the energy efficiency, exergy efficiency, and specific fuel consumption (SFC) of a simple gas turbine cycle. The analysis found that increasing the TIT leads to higher efficiencies and lower SFC, while increasing the To or Rc results in lower efficiencies and higher SFC. For a TIT of 1400 ℃, T1 of 20 ℃, and Rc of 8, the energy and exergy efficiencies were 32.75% and 30.9%, respectively, with an SFC of 187.9 g/kWh. However, for a TIT of 900 ℃, T1 of 30 ℃, and Rc of 30, the energy and exergy efficiencies dropped to 13.18% and 12.44%, respectively, while the SFC increased to 570.3 g/kWh. The results show that there are optimal combinations of TIT, To, and Rc that maximize performance for a given application. Designers must consider trade-offs between efficiency, emissions, cost, and other factors to optimize gas turbine cycles. Overall, this study provides data and insights to improve the design and operation of simple gas turbine cycles.

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.

Phytochemical and antioxidant analysis of some varieties of Capsicum was evaluated. Mature Capsicum varieties were collected across the State. The seeds were removed, sun-dried for 3 days, stored for 2 weeks at 15 ºC–25 ºC in polythene bags before planting. Saponins, tannins, flavonoids, alkaloids and cardiac glycosides were present in abundant, moderate and trace amounts. Combined anthraquinones were absent in all varieties. Yellow (0.810 ± 0.0006 µg/mL), red long dry (0.211 ± 0.0006 µg/mL) and round peppers (2.527 ± 0.0003 µg/mL) had the largest values for total phenol, flavonoids and tannins. Shombo and yellow peppers had the largest (0.270 ± 0.002 µg/mL) and least (0.102 ± 0.001 µg/mL) capsaicin content. The antioxidant activities varied across the varieties. At 100 µg/mL of methanol, yellow (45%) and round peppers (45%) had largest mean absorbances for 2,2-Diphenyl-1-Picrylhydrazyl (DPPH) Radical Scavenging Activity while sub-shombo pepper (23%) had the least. For Ferric Reducing Antioxidant Power (FRAP), yellow (0.63 ± 0.001 µg/mL) and sub-shombo peppers (0.55 ± 0.001µg/mL) had the largest and least values at 100 µg/mL of methanol. At 100 µg/mL of methanol, red long dry (0.112 ± 0.001) and shombo peppers (0.101 ± 0.001) had the largest and least values for the nitric oxide scavenging activity. This study shows that Capsicum varieties exhibit bioactive componds similarities and variations with implications in hybridization, taxonomy and conservation.