Muhammad Qasim

Bridging the Gap between Fiat and Crypto: Why Web3 Cross-Border Payments Need Web2 Thinking: By Muhammad Qasim

Introduction: The Language Powering Modern AI

High-valent metal species (iron, manganese, cobalt, copper, and ruthenium) based advanced oxidation processes (AOPs) have emerged as sustainable technologies for water remediation.

Detrital zircon U–Pb dating from the Muti Formation sheds light on sediment sources and foreland basin development along the northeastern Arabian margin during the Late Cretaceous. The siliciclastic-rich Muti Formation was deposited in a syn-obduction foreland basin that formed as the Semail Ophiolite advanced. Zircon age spectra from eastern (Nakhal and Sayga) and western (Murri) sections are dominated by Neoproterozoic–Cambrian ages (450–900 Ma), linked to the Pan-African orogeny and the Arabian–Nubian Shield, indicating these as the main sediment sources. The Murri section also contains older Mesoproterozoic to Archean zircons, likely recycled from the Nafun Group (part of the Huqf Supergroup), suggesting reworking of ancient Gondwanan cover sequences rather than direct input from the Indian craton. Additional Permian zircons reflect input from Arabian Plate magmatic rocks, while Jurassic–Cretaceous grains indicate material derived from the Semail Ophiolite and related arc terranes. Overall, the Muti Formation records a mixed sediment supply from the Arabian Shield, reworked Gondwanan sandstones, and ophiolitic detritus, marking the transition from a passive margin to a flexural foreland basin. The dominance of Pan-African zircon ages highlights continued recycling of Gondwanan sequences and refines models of Late Cretaceous basin evolution in northern Oman, underscoring the complex, multi-cycle nature of sedimentation in this tectonically active setting.

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The growing global demand for food production, coupled with the increasing threat of plant diseases, necessitates advanced and automated solutions for crop health monitoring. Among various crops, pome fruits such as apples and pears are widely cultivated yet highly susceptible to multiple diseases that can significantly reduce yield and quality. Existing approaches for disease detection and severity classification are often limited by their dependency on manual inspection and their inability to handle complex real-world imagery, especially when multiple diseases coexist on a single leaf. To address these limitations, this research introduces a novel dual-model deep learning framework for multi-disease severity detection and classification in pome fruit leaves. A fine-tuned MobileNetV2 backbone is employed to extract high-level discriminative features from a specialized pome leaf dataset annotated with multiple disease types and severity levels. The proposed system integrates a lightweight Lite-U-Net for semantic segmentation to isolate diseased regions and an enhanced Lite-YOLACT for instance segmentation using a linear combination of prototype masks and mask coefficients. Moreover, a new multi-disease severity scale is proposed to quantify the impact of multiple coexisting infections on a single leaf, an aspect not addressed in previous studies. To enhance interpretability, an improved Grad-CAM technique generates visual heatmaps highlighting the most influential regions in the model’s decision-making process, providing transparency and validation for agricultural experts. Experimental evaluations demonstrate that the proposed framework achieves 95% accuracy in disease severity estimation, effectively identifying and grading multiple infections simultaneously. This study represents a significant step forward in precision agriculture, offering an efficient, interpretable, and scalable deep learning solution for real-world crop health monitoring and management. The source code and trained models are publicly available at: https://github/mqasim0787/Multi-Disease-Severity.

Rawalpindi:A severe heat spell is hitting the population in most parts of the country including this region and according to health experts, prolonged exposure to the extreme heat in the existing...

by and Muhammad Qasim Rana Department of Construction Economics and Management, Faculty of Engineering and the Built Environment, University of Cape Town, Rondebosch, Cape Town 7700, South Africa

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Rawalpindi:The onset of extreme hot and humid weather conditions in monsoon may put the population in twin cities at greater risk of contracting diarrhoea, dysentery, cholera, gastro, typhoid,...