A crucial step in any computational fluid dynamics (CFD) analysis is the discretization of the domain because it influences truncation errors, numerical stability, and the convergence of the model. Therefore, the appropriate selection of numerical mesh parameters crucially contributes to the reliability of the obtained results. Therefore, an innovative approach to reducing the mesh-induced error in CFD analysis of an impinging jet using fuzzy logic is proposed within the paper. The flow parameters were obtained using the Reynolds-averaged Navier–Stokes calculations, based on the mesh parameters obtained using the grid convergence index and fuzzy logic, were compared to each other and to experimental research results. The fuzzy logic approach to define mesh parameters turned out to be a very promising method as it allowed us to obtain results that are qualitatively and quantitatively comparable to commonly used but far more time-consuming methods.

Ibuprofen (IBU) and ketoprofen (KET) are among the world’s most popular and widely used nonsteroidal anti-inflammatory drugs (NSAIDs). Due to their high usage, these drugs have entered the environment, including the soil, and, like any other chemical compound, can have a negative effect on it. Therefore, an attempt was made to evaluate the effects of these two popular drugs on soil bacteria and fungi, the bivalve crustaceans (Heterocipris incongruens) and the growth and development of spring barley. The tested drugs did not show any negative effects on the total bacterial abundance. Effects were observed on the growth and survival of H. incongruens and on the abundance of fungi in the soil at the highest concentrations tested. The presence of IBU and KET in the soil in which spring barley was cultivated caused an increase in the activity of antioxidant enzymes catalase (CAT), superoxide dismutase (SOD), and guaiacol peroxidase (POD); an increase in the content of proline and ascorbic acid (AsA) in the seedlings of this grain; and a decrease in the yield of fresh plant weight, especially at the application of concentrations of 100 and 1000 mg·kg⁻¹ of soil’s dry weight (DW). Effects on barley seed germination potential and germination capacity, plant dry matter content, assimilation pigment content and malondialdehyde (MDA) were also observed at the highest concentrations.

Based on the increased attention, the Special Issue aims to investigate the modeling of complex systems using artificial intelligence and computational methods

The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur’s entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation’s robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.