The article presents the results of research on the assessment of vibrational comfort of children carried in child seats. The work analyzes the impact of the child's mass and the speed of the car passing through the speed bump on the level of vibration to which the passenger (child) transported in the child seat mounted on the rear seat of the car is exposed. The tested car seats were installed in a passenger car using the ISOfix base. During the tests, the measured values were the acceleration of the seat child seat, the backrest of the child seat, the rear seat of the car and the child seat ISOfix base. As part of the research, car drive through the speed bump were carried out at different car speeds and with different mass simulating the mass of a child. The test results were analyzed in order to determine the magnitude of vibrations generated during car drive through the speed bump. Based on the results obtained, the level of vibrational comfort of a child trip in a child seat was assessed.

The article presents a model of an anthropometric dummy designed for low velocity crash tests, designed in ADAMS. The model consists of rigid bodies connected with special joints with appropriately selected stiffness and damping. The simulation dummy has the appropriate dimensions, shape, and mass of individual elements to suit a 50 percentile male. The purpose of this article is to draw attention to low speed crash tests. Current dummies such as THOR and Hybrid III are used for crash tests at speeds above 40 km/h. In contrast, the low-speed test dummy currently used is the BioRID-II dummy, which is mainly adapted to the whiplash test at speeds of up to 16km/h. Thus, it can be seen that there is a gap in the use of crash test dummies. There are no low-speed dummies for side and front crash tests, and there are no dummies for rear crash tests between 16 km/h and 25 km/h. Which corresponds to a collision of a passenger vehicle with a hard obstacle at a speed of 30 km/h. Therefore, in collisions with low speeds of 20 km/h, the splash airbag will probably not be activated. The article contains the results of a computer simulation at a speed of 20 km/h vehicle out in the ADAMS program. These results were compared with the experimental results of the laboratory crash test using volunteers and the Hybrid III dummy. The simulation results are the basis for building the physical model dummy. The simulation aims to reflect the greatest possible compliance of the movements of individual parts of the human body during a collision at low speed.

This review paper analyzes the recent applications of computational mechanics methods in combination with machine learning (ML) and neural network (NN) techniques, as found in the literature published between 2015 and 2024. We present how ML and NNs are enhancing traditional computational methods, such as the finite element method, enabling the solution of complex problems in material modeling, surrogate modeling, inverse analysis, and uncertainty quantification. We categorize current research by considering the specific computational mechanics tasks and the employed ML/NN architectures. Furthermore, we discuss the current challenges, development opportunities, and future directions of this dynamically evolving interdisciplinary field, highlighting the potential of data-driven approaches to transform the modeling and simulation of mechanical systems. The review has been updated to include pivotal publications from 2025, reflecting the rapid evolution of the field in multiscale modeling, data-driven mechanics, and physics-informed/operator learning. Accordingly, the timespan is now 2015–2025, with a focused inclusion of high-impact contributions from 2024 to 2025.

The increasing demands for the reliability of modern industrial equipment and structures necessitate advanced techniques for design, monitoring, and analysis. This review article presents the latest research advancements in the application of machine learning techniques to vibration and acoustic signal analysis from 2015 to 2024. A total of 96 peer-reviewed scientific publications were examined, selected using a systematic Scopus-based search. The main research areas include processes such as modeling and design, health management, condition monitoring, non-destructive testing, damage detection, and diagnostics. In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. These techniques were applied across a wide range of engineering domains, including civil infrastructure, transportation systems, energy installations, and rotating machinery. Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. The findings indicate a clear shift towards deep learning-based methods and multisensor data fusion, accompanied by increasing use of automatic feature extraction and interest in transfer learning, few-shot learning, and unsupervised approaches. This review aims to provide a comprehensive understanding of the current state and future directions of machine learning applications in vibration and acoustics, outlining the field’s evolution and identifying its key research challenges and innovation trajectories.

This literature review addresses a major research gap in electromobility by providing a comprehensive synthesis of machine learning (ML) and deep learning (DL) applications for forecasting energy consumption, managing battery state of charge (SoC), and integrating electric vehicles (EVs) with charging infrastructure and smart grids, including vehicle-to-grid (V2G) systems. Despite the rapid increase in publications between 2016 and 2025, few comparative studies systematically evaluate ML/DL approaches, their effectiveness in specific applications, and their limitations under real-world conditions. To bridge this gap, this review analyzes 95 publications, covering methods from ensemble learners (e.g., Random Forest, XGBoost) to advanced hybrids (e.g., LSTM + MPC). Key influencing factors such as driving style, topography, and weather are considered. This review identifies persistent challenges, including the lack of standardized datasets, limited model generalization, and high computational demands. It also outlines research directions, such as adaptive online learning and integration with V2X technologies. By consolidating current knowledge, this review supports engineers, EV system designers, and policymakers in planning effective energy management and charging strategies, thereby contributing to the sustainable development of electromobility.

Mobile phones accompany us in every area of life and at the same time keep us busy while performing other basic activities such as running, cycling, driving or walking in traffic. The problem of using cell phones while driving is of interest to many scientists around the world. This offense is severely punished in many countries. The problem of using mobile phones in moving vehicles has been largely resolved; however, there is a visible high percentage of pedestrians and cyclists who are used to checking their devices while moving in traffic. The aim of the article is to get acquainted with the issues of using mobile phones by pedestrians when crossing a pedestrian crossing and to escalate the problem. Despite the introduction of a ban on the use of mobile phones when crossing a pedestrian crossing in the Polish law on road traffic, many road users still use phones. The conducted observation shows that at least 2 out of 10 people pass through the pedestrian crossing with a mobile phone in their hand.