This work focused on introducing a new two-dimensional (2D) chaotic system. It is combined of some existing maps, the logistic, iterative chaotic map with infinite collapse, and Henon maps; we called it 2D-LCHM. The assessment of the actual performance of 2D-LCHM presents its sensitivity to a tiny change in the initial condition. Besides, its dynamics behavior is very complicated. It also has hyperchaotic properties and good ergodicity. The proposed system is occupied with designing a new image encryption system. Changing the image pixels’ locations is the primary step in the encryption process. The original image is splitting into four blocks to create four different keys based on 2D-LCHM; this reduces the computation time and increases the complexity. To obtain the encryption image, we have to repeat the partitioning process several times for each block. The encryption image’s efficiency is shown through some performance analysis such as; image histogram, the number of pixels changes rate (NPCR), the unified average changing intensity (UACI), pixels correlation, and entropy. The proposed system is compared with some efficient encryption algorithms in terms of chaocity attributes and image performance; the analysis result showed consistent improvement.
The objective of this study was to correlate the binding of drugs on a very popular nanoparticulate polymeric matrix; PLGA nanoparticles with their main constitutional, electronic and physico-chemical descriptors. Gaussian Processes (GPs) was the artificial intelligence machine learning method that was utilized to fulfil this task. The method could successfully model the results where optimum values of the investigated descriptors of the loaded drugs were deduced. A percentage bias of 12.68 % ± 2.1 was obtained in predicting the binding energies of a group of test drugs. As a conclusion, GPs could successfully model the drugs-PLGA interactions associated with a good predicting power. The GPs-predicted binding energies (ΔG) can easily be projected to the drugs loading as was previously proven. Adopting the “Pharmaceutics Informatics” approach can save the pharmaceutical industry and the drug delivery scientists a lot of exerted resources, efforts and time.
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