The thermal imprint process of polymer micro-patterning is widely applied in areas such as manufacturing of optical parts, solar energy, bio-mechanical devices and chemical chips. Polycarbonate (PC), as an amorphous polymer, is often used in thermoforming processes because of its good replication characteristics. In order to obtain replicas of the best quality, the imprint parameters (e.g., pressure, temperature, time, etc.) must be determined. Therefore finite element model of the hot imprint process of lamellar periodical microstructure into PC has been created using COMSOL Multiphysics. The mathematical model of the hot imprint process includes three steps: heating, imprinting and demolding. The material properties of amorphous PC strongly depend on the imprint temperature and loading pressure. Polycarbonate was modelled as an elasto-plastic material, since it was analyzed below the glass transition temperature. The hot imprint model was solved using the heat transfer and the solid stress-strain application modes with thermal contact problem between the mold and polycarbonate. It was used for the evaluation of temperature and stress distributions in the polycarbonate during the hot imprint process. The quality of the replica, by means of lands filling ratio, was determined as well.

Frequency up-conversion is a promising technique for energy harvesting in low frequency environments. In this approach, abundantly available environmental motion energy is absorbed by a Low Frequency Resonator (LFR) which transfers it to a high frequency Piezoelectric Vibration Energy Harvester (PVEH) via impact or magnetic coupling. As a result, a decaying alternating output signal is produced, that can later be collected using a battery or be transferred directly to the electric load. The paper reports an impact-coupled frequency up-converting tandem setup with different LFR to PVEH natural frequency ratios and varying contact point location along the length of the harvester. RMS power output of different frequency up-converting tandems with optimal resistive values was found from the transient analysis revealing a strong relation between power output and LFR-PVEH natural frequency ratio as well as impact point location. Simulations revealed that higher power output is obtained from a higher natural frequency ratio between LFR and PVEH, an increase of power output by one order of magnitude for a doubled natural frequency ratio and up to 150% difference in power output from different impact point locations. The theoretical results were experimentally verified.

The type of the generator was chosen to be piezoelectric because it is most widely used to capture the energy of mechanical vibrations and convert it into electricity. A 2D generator model was chosen for the analysis of the dynamics of the energy harvester (VEH) for a free body moving in space at vibro shock mode. The computational schemes of the model are presented. A mathematical model describing the dynamics is developed: generator casing, inertial mass, nonlinear contact/impact. The mathematical model is implemented by the Runge-Kutta numerical method in the Matlab system. Analysis of experimental and modeling results is performed: comparison of generator eigen frequencies, the logarithmic decrement and the damping coefficient – the error is in range of 5%. This generator can be used to collect mechanical energy of water waves, wind, human movements, and other environmental factors in the presence of the body's free movement trajectory in space.

Aeshna Cyanea is a species of dragonfly in the Aesnidea family. In this study, morphology and mechanical properties of dragonfly were investigated. The location of the nodus on the wing was observed. A SEM study was also carried out to get a closer look at the mechanical structure of the dragonfly wing. A dynamic study of the wing was carried out to and the first resonant frequency of the wing was found. Future research will cover dragonfly wing mathematical model and simulation of the wing dynamics.

Implementation of computer-generated hologram of the emblem of Kaunas University of Technology by means of Fourier transformation from greyscale image, and transferring of the hologram to PMMA using electronic lithography are analyzed in this paper. Gerchberg-Saxton algorithm of synthesis of computer generated hologram and sample of computer generated hologram are presented as well.http://dx.doi.org/10.5755/j01.mech.17.3.512

The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems.