Considering radial basis function neural network for effective solution generation in metaheuristic algorithms.

In many engineering optimization problems, the number of function evaluations is severely limited by the time or cost constraints. These limitations present a significant challenge in the field of global optimization, because existing metaheuristic methods typically require a substantial number of function evaluations to find optimal solutions. This paper presents a new metaheuristic optimization algorithm that considers the information obtained by a radial basis function neural network (RBFNN) in terms of the objective function for guiding the search process. Initially, the algorithm uses the maximum design approach to strategically distribute a set of solutions across the entire search space. It then enters a cycle in which the RBFNN models the objective function values from the current solutions. The algorithm identifies and uses key neurons in the hidden layer that correspond to the highest objective function values to generate new solutions. The centroids and standard deviations of these neurons guide the sampling process, which continues until the desired number of solutions is reached. By focusing on the areas of the search space that yield high objective function values, the algorithm avoids exhaustive solution evaluations and significantly reduces the number of function evaluations. The effectiveness of the method is demonstrated through a comparison with popular metaheuristic algorithms across several test functions, where it consistently outperforms existing techniques, delivers higher-quality solutions, and improves convergence rates.

Image segmentation with Cellular Automata.

Image segmentation is a computer vision technique that involves dividing an image into distinct and meaningful regions or segments. The objective was to partition the image into areas that share similar visual characteristics. Noise and undesirable artifacts introduce inconsistencies and irregularities in image data. These inconsistencies severely affect the ability of most segmentation algorithms to distinguish between true image features, leading to less reliable and lower-quality results. Cellular Automata (CA) is a computational concept that consists of a grid of cells, each of which can be in a finite number of states. These cells evolve over discrete time steps based on a set of predefined rules that dictate how a cell's state changes according to its own state and the states of its neighboring cells. In this paper, a new segmentation approach based on the CA model was introduced. The proposed approach consisted of three phases. In the initial two phases of the process, the primary objective was to eliminate noise and undesirable artifacts that can interfere with the identification of regions exhibiting similar visual characteristics. To achieve this, a set of rules is designed to modify the state value of each cell or pixel based on the states of its neighboring elements. In the third phase, each element is assigned a state that is chosen from a set of predefined states. These states directly represent the final segmentation values for the corresponding elements. The proposed method was evaluated using different images, considering important quality indices. The experimental results indicated that the proposed approach produces better-segmented images in terms of quality and robustness.

Kinematic Changes in a Mouse Model of Penetrating Hippocampal Injury and Their Recovery After Intranasal Administration of Endometrial Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Locomotion speed changes appear following hippocampal injury. We used a hippocampal penetrating brain injury mouse model to analyze other kinematic changes. We found a significant decrease in locomotion speed in both open-field and tunnel walk tests. We described a new quantitative method that allows us to analyze and compare the displacement curves between mice steps. In the tunnel walk, we marked mice with indelible ink on the knee, ankle, and metatarsus of the left and right hindlimbs to evaluate both in every step. Animals with hippocampal damage exhibit slower locomotion speed in both hindlimbs. In contrast, in the cortical injured group, we observed significant speed decrease only in the right hindlimb. We found changes in the displacement patterns after hippocampal injury. Mesenchymal stem cell-derived extracellular vesicles had been used for the treatment of several diseases in animal models. Here, we evaluated the effects of intranasal administration of endometrial mesenchymal stem cell-derived extracellular vesicles on the outcome after the hippocampal injury. We report the presence of vascular endothelial growth factor, granulocyte-macrophage colony-stimulating factor, and interleukin 6 in these vesicles. We observed locomotion speed and displacement pattern preservation in mice after vesicle treatment. These mice had lower pyknotic cells percentage and a smaller damaged area in comparison with the nontreated group, probably due to angiogenesis, wound repair, and inflammation decrease. Our results build up on the evidence of the hippocampal role in walk control and suggest that the extracellular vesicles could confer neuroprotection to the damaged hippocampus.