Muhammad Usman

Brain tumors pose a serious threat to global health, making accurate and early detection essential for effective treatment planning. While Magnetic Resonance Imaging (MRI) is widely used for diagnosis, manual interpretation is time-consuming and subject to error. This has prompted increasing use of deep learning for automated tumor classification. We propose a novel framework based on the Swin Transformer V2 architecture for classifying brain tumors in MRI scans into glioma, meningioma, pituitary tumor, and non-tumor categories. The design introduces two core innovations: a Dual-Branch Down-sampling module and an Enhanced Attention Mechanism, which improve multi-scale feature representation and computational efficiency. Using a dataset of 7,023 grayscale MRI images, the proposed model achieved an accuracy of 98.97%, outperforming ResNet50 (90.39%) and DenseNet121 (93.20%). It maintained precision, recall, and F1-scores above 98% across all classes and showed improved training efficiency. These results demonstrate the model’s potential as a robust and efficient diagnostic support system for brain tumor classification.

Human action recognition has become crucial in computer vision, with growing applications in surveillance, human–computer interaction, and healthcare. Traditional approaches often use broad feature representations, which may miss subtle variations in timing and movement within action sequences. Our proposed One-to-Many Hierarchical Contrastive Learning (OTM-HC) framework maps the input into multi-layered feature vectors, creating a hierarchical contrast representation that captures various granularities within a human skeleton sequence temporal and spatial domains. Using sequence-to-sequence (Seq2Seq) transformer encoders and downsampling modules, OTM-HC can distinguish between multiple levels of action representations, such as instance, domain, clip, and part levels. Each level contributes significantly to a comprehensive understanding of action representations. The OTM-HC model design is adaptable, ensuring smooth integration with advanced Seq2Seq encoders. We tested the OTM-HC framework across four datasets, demonstrating improved performance over state-of-the-art models. Specifically, OTM-HC achieved improvements of 0.9% and 0.6% on NTU60, 0.4% and 0.7% on NTU120, and 0.7% and 0.3% on PKU-MMD I and II, respectively, surpassing previous leading approaches across these datasets. These results showcase the robustness and adaptability of our model for various skeleton-based action recognition tasks.

The Belt and Road Initiative (BRI) is a significant economic development strategy directed by China. Its primary objective is to establish connectivity across a vast region encompassing over 70 countries in Asia, Europe, and Africa. This endeavor significantly impacts worldwide development, economic advancement, and environmental sustainability. Nevertheless, insufficient pertinent evidence exists when exploring the correlation between urbanization, rural energy poverty (Rural_EP), and carbon emissions (CO2_Em) in the BRI region. The present study examines panel data encompassing 48 countries participating in BRI from 2001 to 2020. This research addresses existing gaps by employing the System-GMM and Driscoll and Kraay Standard Error (DKSE) models to investigate factors influencing CO2_Em. The findings indicate that the presence of energy poverty in rural areas is associated with higher levels of CO2_Em, while urbanization has a mitigating effect on such emissions. Furthermore, adopting production methods and environmentally sustainable technologies by foreign corporations leads to a decrease in CO2_Em, thereby providing evidence of a pollution halo effect in BRI. Moreover, economic growth and industrialization have detrimental environmental consequences, primarily through the amplification of CO2_Em. Based on the empirical evidence, the study proposes policy measures that advocate for promoting renewable energy sources, adopting sustainable urban development practices, implementing energy conservation strategies, and establishing carbon pricing mechanisms.