بحوث سكوبس — أ.د هادي ياسر عبود الجنابي

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental risks. Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed management. Machine learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate predictions. Machine learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application. © The Author(s) 2024. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)

Obesity has existed for a very long time. Obesity leads to improper physiological metabolism, which in turn produces a host of physiological and social issues in addition to aesthetic concerns. Individuals are working to discover anti-obesity medications and other safe and efficient treatment options. Because pancreatic lipase (PL) is essential to human fat metabolism, PL inhibitors are now used to treat obesity in clinical settings. This research involved partially purifying lipase from the ovine pancreas. One lipase isoenzyme was identified using the CM-cellulose ion exchange chromatography technology. It had a specific activity of 2.467 U/mg protein and a recovery of 272 in comparison to the crude extract. The molecular weight was determined to be 33.8 kilodaltons, and the electrophoresis method produced only one band. Using p-nitrophenyl acetate as a substrate, the capacity of willow bark extracts to inhibit partly purified pancreatic lipase activity was investigated. The inhibitory percentages via treating the enzyme with EthOH 70% and H2O were found to be 27.14 and 61.43, respectively. Furthermore, the Lineweaver-Burk plots revealed the inhibition modes noncompetitive with both extracts. © 2025, Society of Pathological Biochemistry and Hematology. All rights reserved.