COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

Background And Novelty: When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance.

Novel assessment tools for inland aquaculture in the western Godavari delta region of Andhra Pradesh.

The production of fisheries and shrimp has been twice every 10 years for the previous five decades, making it the most rapidly expanding food industry. This growth is due to intensive farming and the conversion of agriculture into aquaculture in many parts of South Asia. Furthermore, intensive aquaculture generates positive economic growth but leads to environmental degradation without proper monitoring.

Plant microbe based remediation approaches in dye removal: A review.

Increased industrialization demand using synthetic dyes in the newspaper, cosmetics, textiles, food, and leather industries. As a consequence, harmful chemicals from dye industries are released into water reservoirs with numerous structural components of synthetic dyes, which are hazardous to the ecosystem, plants and humans. The discharge of synthetic dye into various aquatic environments has a detrimental effect on the balance and integrity of ecological systems.