ABSTRACT- Purpose. A physically sound derivation for reciprocal power time (RPT) model for kinetic of drug release is given. In order to enhance ibuprofen dissolution, its solid dispersions (SDs) prepared by cogrinding technique using crospovidone (CP), microcrystalline cellulose (MC) and oleaster powder (OP) as a novel carrier and the model applied to the drug release data. Methods. The drug cogrounds with the carriers were prepared and subjected to the dissolution studies. For elucidation of observed in vitro differences, FT-IR spectroscopy, X-ray diffraction patterns, DSC thermograms and laser particle size measurement were conducted. Results. All drug release data fitted very well to newly derived RPT model. The efficiency of the carriers for dissolution enhancement was in the order of: CP>OP>MC. The corresponding release kinetic parameter derived from the model, t50% (time required for 50% dissolution) for the carrier to drug ratio 2:1 were 2.7, 10.2 and 12.6 min, respectively. The efficiency of novel carrier, OP, was between CP and MC. FT-IR showed no interaction between the carriers and drug. The DSC thermograms and X-ray diffraction patterns revealed a slight reduced crystallinty in the SDs. Also grinding reduced mean particle size of drug from 150.7 to 44.4 µm. Conclusion. An improved derivation for RPT model was provided which the parameter of the model, t50%, unlike to previous derivations was related to the most important property of the drug i.e. its solubility. The model described very well drug release kinetics from the solid dispersions. Cogrinding was an effective technique in enhancing dissolution rate of ibuprofen. Elaeagnus angostifolia fruit powder was suggested as a novel potential hydrophilic carrier in preparing solid dispersion of ibuprofen.
High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.
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