• Energy Research
• 2021-2025close

Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real‐time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology‐enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES‐ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC‐PSO, and PSO‐COGENT algorithms, the created HES‐ACO algorithm was simulated at CloudSim and found to optimize all parameters.

Zanthoxylum rhetsa (ZR) is used traditionally to manage a variety of ailments, including diabetes. Oxidative stress may accelerate the diabetic condition. The available antidiabetic and antioxidant drugs have many shortcomings including resistance, inefficiency, higher dose, side effects and costs. The goal of the current investigation was to assess the antioxidant capacity and antidiabetic activity of an ethanolic extract of Zanthoxylum rhetsa root bark (ZRRB) through in vitro, in vivo, and in silico methods. The antioxidant capacity of the ZRRB extract was measured using both the DPPH radical assay and the total antioxidant activity test. The oral glucose tolerance test (OGTT) and alloxan-induced diabetic mice model were also used to examine in vivo antidiabetic efficacy. Phytochemicals identification was done by GCMS analysis. Additionally, computational methods such as molecular docking, ADMET analysis, and molecular dynamics (MD) modeling were performed to determine the above pharmacological effects. The extract demonstrated significant DPPH scavenging activity (IC50 = 42.65 μg/mL). In the OGTT test and alloxan-induced diabetes mice model, the extract effectively lowered blood glucose levels. Furthermore, in vitro inhibition of pancreatic α-amylase studies demonstrated the ZRRB extract as a good antidiabetic crude drug (IC50 = 81.45 μg/mL). GCMS investigation confirmed that the crude extract contains 16 major phytoconstituents, which were docked with human peroxiredoxin-5, α-amylase, and sulfonylurea receptor 1. Docking and pharmacokinetic studies demonstrated that among 16 phytoconstituents, 6H-indolo[3,2,1-de] [1,5]naphthyridin-6-one (CID: 97176) showed the highest binding affinity to targeted enzymes, and imitated Lipinski’s rule of five. Furthermore, MD simulation data confirmed that the aforementioned compound is very steady to the binding site of α-amylase and sulfonylurea receptor 1 receptors. Findings from in vitro, in vivo and in silico investigation suggest that ZRRB extract contains a lead compound that could be a potent source of antidiabetic drug candidate.