In this paper, a novel auxetic structure with rotating squares with holes is investigated. The unit cell of the structure consists of four units in the shape of a square with cut corners and holes. Finally, the structure represents a kind of modified auxetic structure made of rotating squares with holes or sheets of material with regularly arranged diamond and square cuts. Effective and dynamic properties of these structures depend on geometrical properties of the structure. The structures are characterized by an effective Poisson’s ratio from negative to positive values (from about minus one to about plus one). Numerical analysis is made for different geometrical features of the unit cells. The simulations enabled the determination of the dynamic characteristic of the analyzed structures using vibration transmission loss, transmissibility, and mechanical impedance. Numerical calculations were conducted using the finite element method. In the analyzed cases of cellular auxetic structures, a linear elasticity model of the material is assumed. The dynamic characteristic of modified rotating square structures is strongly dependent not only on frequency. The dynamic behavior could also be enhanced by adjusting the geometric parameter of the structure. Auxetic and non-auxetic structures show different static and dynamic properties. The dynamic properties of the analyzed structures were examined in order to determine the frequency ranges of dynamic loads for which the values of mechanical impedance and transmissibility are appropriate.

This paper considers the synthesis of the four-bar mechanism. It is treated here as an optimization problem, in which an objective function is defined. To solve this problem, a metaheuristic called the virus optimization algorithm is employed. Furthermore, a new path-repairing technique recently published by Sleesongsom and Bureerat is applied instead of the very common technique related to the application of a penalty function. This makes the search by means of the metaheuristic more efficient. Furthermore, the obtained results are very accurate.

This paper is dedicated to the analysis of a foot prosthesis optimization process, with a particular focus on the application of optimization algorithms and unconventional materials, such as auxetic materials. The study aims to enhance prosthesis performance by minimizing the difference between the ground reaction force generated by the prosthetic foot and that of a natural limb. In the initial part of the study, the basic topics concerning the parameterization of the foot prosthesis geometry and the preparation of a finite element model for human gait are discussed. In the subsequent part of the study, the focus is on the optimization process, in which algorithms were applied to adjust the prosthesis structure to the patient’s individual needs. The optimization process utilized a finite element method gait model. After validating the FEM, an algorithm generating the prosthesis geometry based on the given parameters was developed. These parameters were optimized using the VOA, comparing FEM gait model data on vertical ground reaction force with experimental results. The results of the foot prosthesis optimization are presented through a comparison of different structural models. The study also demonstrates the application of auxetic materials, which, due to their unique mechanical properties, can enhance foot prosthesis efficiency. Simulations were performed using multi-material topology optimization. The results obtained for different gait phases were compared.

Brain–computer interfaces (BCIs) have garnered significant interest due to their potential to enable communication and control for individuals with limited or no ability to interact with technologies in a conventional way. By applying electrical signals generated by brain cells, BCIs eliminate the need for physical interaction with external devices. This study investigates the performance of traditional classifiers—specifically, linear discriminant analysis (LDA) and support vector machines (SVMs)—in comparison with a hybrid neural network model for EEG-based gesture classification. The dataset comprised EEG recordings of seven distinct gestures performed by 33 participants. Binary classification tasks were conducted using both raw windowed EEG signals and features extracted via bandpower and the empirical wavelet transform (EWT). The hybrid neural network architecture demonstrated higher classification accuracy compared to the standard classifiers. These findings suggest that combining featuring extraction with deep learning models offers a promising approach for improving EEG gesture recognition in BCI systems.