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.

Background: Multiple sclerosis is often a longitudinal disease continuum with an initial relapsing-remitting phase (RRMS) and later secondary progression (SPMS). Most currently approved therapies are not sufficiently effective in SPMS. Early detection of SPMS conversion is therefore critical for therapy selection. Important decision-making tools may include testing of partial cognitive performance and magnetic resonance imaging (MRI). Aim of the work: To demonstrate the importance of cognitive testing and MRI for the prediction and detection of SPMS conversion. Elaboration of strategies for follow-up and therapy management in practice, especially in outpatient care. Material and methods: Review based on an unsystematic literature search. Results: Standardized cognitive testing can be helpful for early SPMS diagnosis and facilitate progression assessment. Annual use of sensitive screening tests such as Symbol Digit Modalities Test (SDMT) and Brief Visual Memory Test-Revised (BVMT-R) or the Brief International Cognitive Assessment for MS (BICAMS) test battery is recommended. Persistent inflammatory activity on MRI in the first three years of disease and the presence of cortical lesions are predictive of SPMS conversion. Standardized MRI monitoring for features of progressive MS can support clinically and neurocognitively based suspicion of SPMS. Discussion: Interdisciplinary care of MS patients by clinically skilled neurologists, supported by neuropsychological testing and MRI, has a high value for SPMS prediction and diagnosis. The latter allows early conversion to appropriate therapies, as SPMS requires different interventions than RRMS. After drug switching, clinical, neuropsychological, and imaging vigilance allows stringent monitoring for neuroinflammatory and degenerative activity as well as treatment complications.

The digital era has transformed education, making digital literacy essential for teachers to integrate technology and enhance student outcomes effectively. This study aims to examine how school culture influences teachers’ performance through their digital literacy, focusing on junior high school teachers in Malang City, East Java, Indonesia. Employing a quantitative approach, data were collected from 214 teachers out of a 457 population using questionnaires. The analysis was conducted through AMOS for Confirmatory Factor Analysis (CFA), SPSS for descriptive statistics, and PLS-SEM for hypothesis testing. The findings reveal that school culture significantly affects teachers’ digital literacy (Ho1) and teacher performance (Ho2) with supportive and innovative environments, while rigid cultures limit creativity. Furthermore, digital literacy was found to enhance teachers’ performance (Ho3) and mediate the impact of school culture on teachers’ performance (Ho4), enhancing teachers’ effectiveness in planning, implementing, and evaluating instruction. This study highlights the critical role of school culture in shaping digital literacy and offers new insights for improving teacher practices in diverse educational settings. Moreover, the role of education policies in fostering a collaborative school culture that enhances teachers’ digital literacy and performance, leading to improved educational outcomes, plays a crucial implication.

This study investigates the willingness of Indonesian consumers, particularly in West Java, to pay for green products by applying and expanding the Theory of Planned Behavior (TPB). It examines how perceived green product value and willingness to pay premiums influence consumer intentions and behavior toward green purchases. The research highlights the gap between consumers’ willingness to pay for environmentally friendly products and the actual sales of such products. By incorporating perceived value and willingness to pay into the TPB framework, the study aims to find what factors that can address the gap particularly in a developing country context to contribute to shaping a pro-environmental socio-cultural community in Indonesia and mitigates country’s significant environmental challenges. In the context of 251 young consumers in Indonesia, this study finds that subjective norms do not significantly influence purchase intentions. However, attitudes and behavioral controls do effectively encourage green behavior, suggesting that societal norms for green behavior may not be fully established. In addition, while willingness to pay a premium and perceived value of green purchases can influence green behavior, consumers are generally reluctant to pay higher prices for environmentally friendly products.

This study aims to analyze, investigate the implications, and identify differences in the progress of the effect of institutional changes and organizational transformation in Indonesian higher education. The structuration analysis shows that examining the conditions that have resulted in the replication and modification of social systems is the focus of the structuration analysis. The image of structuration theory conveys both a sense of regularity and continuity, as well as respect for the labor that must be done daily and the mundane but essential tasks that must be completed. The finding of this study is that with the mandate that universities have been given to implement the three primary pillars that support Indonesia’s higher education system, the difficulty level of the problem facing Indonesia’s higher education system has increased. We suggest a future research agenda and highlight the changes and transformations in power, interests, and alliances that affect the evolution of higher education institutions.

The H3N2 influenza virus is spiking dramatically, which is a major concern worldwide and in India. The multifunctional hetero-trimer influenza virus RNA-dependent RNA polymerase (RdRP) is involved in the generation of viral mRNA and is crucial for viral infectivity, which is directly related to the virus’s ability to survive. The goal of the current work was to use molecular docking to determine how the RdRP protein might be affected by powerful bioactive chemicals found in Calotropis gigantia latex. By applying CB-dock 2 analysis and 2D interactions, an in-silico docking study was conducted using a GC-FID (gas chromatography with flame-ionization detection) based composition profile. Tocospiro A (15%), Amyrin (7%), and Gombasterol A were found by GC-FID to be the main phytocompounds in the latex of Calotropis gigantia. The docking result showed that ligands were effectively bound to RdRP. According to interaction studies, RdRP/ligand complexes create hydrogen bonds, van der Waals forces, pi-alkyl bonds, alkyl bonds, and pi-Sigma bonds. Therefore, it was suggested that Calotropis gigantia latex may represent a possible herbal remedy to attenuate H3N2 infections based on the above findings of the fragrance profile and docking.