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Generation of nanostructured device from mucilaginous seeds of ocimum basilicum for drug delivery and tissue engineering applications using a supercritical fluid assisted process
Quantification of blood flow and wall shear stress in ascending aorta using time-resolved two-dimensional phase contrast magnetic resonance imaging
Efficient conversion of various biomass-derived feedstocks into γ-valerolactone over a composite Au NP/KIT-6 catalyst using formic acid as the hydrogen carrier
Comparative classification of spectrally overlapping Allium seed genotypes using Vis-NIR spectroscopy and hyperspectral imaging with chemometric, machine, and deep learning models
The hidden threat of heavy metal leaching in urban runoff: Investigating the long-term consequences of land use changes on human health risk exposure
Strengthening of Two-Way RC Flat Slabs for Punching Shear Using Steel Dowels and CFRP Sheet with EBROG and EBRIG Methods: An Experimental Study
Novel Cement-Free UHPC with High Gamma-Ray Resistance Using Calcium Oxide-Activated Slag, Iron and Barite Powders, and Steel Fibers
Comparative classification of spectrally overlapping Allium seed genotypes using Vis-NIR spectroscopy and hyperspectral imaging with chemometric, machine, and deep learning models
Single-atom catalysts (SACs) for CO <sub>2</sub> to CO conversion using Cu, Ni, and Co on graphene flakes support; a DFT study
Promising label-free trapping, isolation and detection of extracellular vesicles, using an interdigitated microelectrode array integrated in a microfluidic cell: theoretical and experimental assessments
Predicting hydrogen production in porous foams for steam methane reforming: A combined approach using computational fluid dynamics and machine learning regression models
Improving prediction accuracy of CSM-CERES-Wheat model for water and nitrogen response using a modified Penman-Monteith equation in a semi-arid region
Comparative classification of spectrally overlapping Allium seed genotypes using Vis-NIR spectroscopy and hyperspectral imaging with chemometric, machine, and deep learning models
Predicting hydrogen production in porous foams for steam methane reforming: A combined approach using computational fluid dynamics and machine learning regression models
Trace analysis of basic drugs in micro-volume human plasma samples using a simple device combined with a gas chromatography-flame ionization detector