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This research aimed to compare two solvent-based methods for the preparation of amorphous solid dispersions (ASDs) made up of poorly soluble spironolactone and poly­(vinylpyrrolidone-co-vinyl acetate). The same apparatus was used to produce, in continuous mode, drug-loaded electrospun (ES) and spray-dried (SD) materials from dichloromethane and ethanol-containing solutions. The main differences between the two preparation methods were the concentration of the solution and application of high voltage. During electrospinning, a solution with a higher concentration and high voltage was used to form a fibrous product. In contrast, a dilute solution and no electrostatic force were applied during spray drying. Both ASD products showed an amorphous structure according to differential scanning calorimetry and X-ray powder diffraction results. However, the dissolution of the SD sample was not complete, while the ES sample exhibited close to 100% dissolution. The polarized microscopy images and Raman microscopy mapping of the samples highlighted that the SD particles contained crystalline traces, which can initiate precipitation during dissolution. Investigation of the dissolution media with a borescope made the precipitated particles visible while Raman spectroscopy measurements confirmed the appearance of the crystalline active pharmaceutical ingredient. To explain the micro-morphological differences, the shape and size of the prepared samples, the evaporation rate of residual solvents, and the influence of the electrostatic field during the preparation of ASDs had to be considered. This study demonstrated that the investigated factors have a great influence on the dissolution of the ASDs. Consequently, it is worth focusing on the selection of the appropriate ASD preparation method to avoid the deterioration of dissolution properties due to the presence of crystalline traces.

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The ant search is usually used in 111, 1.21, 131, 141-[61 for solving problems like the Traveling Salesman Problem (TSP), where all the points of the search graph can be destinations and the edges between them represent the ways with the appropriate weights.In this article we describe a modification of the ant search for another goal: we search for "wide paths", where most of the graph points won't be targets, only small parts of the path we are looking for. The paths found by the algorithm consist of multiple alternative routes near each other without relevant difference in length (or cost). (The result looks like a wide path on a topographical map.) This leads to a loosely defined path, which is very useful, because obstacle avoidance maneuvers can be completed without leaving the path itself. This means that the AI doesn't have to worry about getting back on the route, which would need additional searches.Using the method of the ants this way leads to n very different search type (in search space handling and in result as well), than the approaches mentioned above.A further advantage of the algorithm is that it provides further information about the search space in conustion with traffic load. I n this aspect, 171 simulates transportation and traffic load, which is partly similar to this case, but still differs in the concept of the search space.Index Tmns-Ant search, collective intelligence, wide paths, nondeterministic search, hormones I. THE ANT SEARCH IN THE NATURE In the nature ants use a hormone called pheromone to find the shortest paths between locations. Every ant is emitting this hormone continuously while moving. If it finds some food, pheromone makes it possible to find the way hack. If an ant senses the presence of pheromone, it follows that path. This way other ants will find the food as well and they begin to move between the nest and the food. If two ants find different ways to the same place, the one finding the shorter path will move more times along it than the other. After a while the pheromone concentration on the shorter path will be stronger and ants on the other one will prefer this one as well. This usually leads to a globally acceptable solution. USING THE METHOD OF THE ANTSNow let's see how we can use the method of the ants in our search spaces. In this section we will describe how we can do it and what problems we have to solve. To use this idea we need lots of ants to move in our search space. To get as fast algorithm as possible, we should use very simple ants. Ants using 4 directions and moving with constant speed seem to be sufficient. Ants are using the following rules for the navigation: they can't pass through each other, prefer to move forward, defer to move backward and they follow the pheromone spores. Randomized selection is used during choosing the direction as well. The paths of pheromone will be "wide", because the ants keep on avoiding each other and don't follow the routes very As time goes on, the concentration of pheromone decreases just as in the natural case. Thi...