Elena Mereuta

The steering linkage represents a key subsystem of any automobile, playing a direct role in vehicle handling, driving safety, and overall comfort. Within this mechanism, the tie rod and tie rod end are crucial for transmitting steering forces from the gear to the wheel hub. A typical issue that gradually develops in these components is the clearance appearing in the spherical joint, caused by wear, corrosion, and repeated operational stresses. Even small clearances can noticeably reduce stiffness and natural frequencies, making the system more sensitive to vibration and premature failure. In this work, the effect of spherical joint clearance on the dynamic behavior of the tie rod-tie rod end assembly was analyzed through numerical simulation combined with experimental observation. Three-dimensional CAD models were meshed with tetrahedral elements and subjected to modal analysis under several clearance conditions, while boundary constraints were set to replicate real operating conditions. Experimental measurements on a dedicated test rig were used to assess joint clearance and wear in service parts. The results indicate a strong nonlinear relationship between clearance magnitude and modal response, with PTFE bushing degradation identified as the main source of clearance. These findings link the evolution of clearance to the change in vibration characteristics, providing useful insight for diagnostic approaches and predictive maintenance aimed at improving steering reliability and vehicle safety.

This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as ‘artificial intelligence’, ‘machine learning’, ‘sports injury’, and ‘risk prediction’. While AI’s predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges.

In this study, the wave energy potential in the coastal region of Romania was analyzed. For this purpose, a reanalysis data set downloaded from ECMWF (European Centre for Medium-Range Weather Forecasts) was used. The reanalysis data contains information for various models. In this analysis the significant height of combined wind waves and swell, the mean wave direction and the mean wave period were analyzed in order to determine the potential of extracting clean energy from waves. The aim of this research is to analyze some performances factors (capacity factor, rated capacity, operating capacity, annul electricity production) of some of the most used wave energy convertors.

The aim of this study is to evaluate the efficiency of extracting clean energy in the Dobrogea region of Romania. More precisely was analyzed the wind potential in correspondence with some economical parameters that determines the efficiency of extracting clean energy. For this purpose, a data set that contains wind zonal and meridional components for a period of 20 years was processed. In this study, the data set was downloaded from the European Centre for Medium-Range Weather Forecasts. By analyzing the levelized cost of electricity, the annual energy production, the annual expensive and other parameters it can be easily observed that implementing new project for extracting renewable energy can be efficient.

The objective of this work is to analyze the wind and wave conditions along the coasts of the European seas. The emphasis is put on the mean and maximum values. The areas studied are characterized by intense maritime activities, including traffic, as well as various harbor and offshore operations. In the present study, 35 years of data (1983–2017) coming from the European Centre for Medium-Range Weather Forecasts (ECMWF) were processed, corresponding to 40 different geographical locations. Thus, these 40 reference points are defined for some of the most relevant offshore locations in the coastal environments targeted. As regards the data considered in the analysis, two different sets were used. The first corresponds to the wave model, while the second to the atmospheric model, both operated by ECMWF. Finally, it can be concluded that the proposed work provides a global perspective related to the average and maximum wind and wave conditions and to a further extent on the climate dynamics along the coasts of the European seas.

The main objective of the present study is to quantify the recent past and explore the near future wind power potential in the Black Sea basin, evaluating the possible changes. Furthermore, an analysis of the wind climate in the target area was also performed. The wind resources have been assessed using the wind fields provided by various databases. Thus, the wind power potential from the recent past was assessed based two different sources covering each one the 30-year period (1981–2010). The first source is the ERA-Interim atmospheric reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), while the second source represents the hindcast wind fields simulated by a Regional Climate Model (RCM) and provided by EURO-CORDEX databases. The estimation of the near future wind power potential was made based on wind fields simulated by the same RCM under future climate projections, considering two Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and RCP8.5) and they cover also a 30-year time interval (2021–2050). Information in various reference points were analyzed in detail. Several conclusions resulted from the present work. Thus, as regards the mean wind power potential in winter season, in 51% of the locations a significant increase is projected in the near future (both scenarios). Besides providing a detailed description of the wind conditions from the recent past over the Black Sea basin considering two major sources, the novelty of the present work consists in the fact that it gives an estimation of the expected wind climate in the target area for the near future period and at the same time an evaluation of the climate change impacts on the wind speed and wind power potential.

The objective of this work is to analyze the wind and wave energy potential in the proximity of the Greek islands. Thus, by evaluating the synergy between wind and waves, a more comprehensive picture of the renewable energy resources in the target area is provided. In this study, two different data sources are considered. The first data set is provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the ERA-Interim project and covers an 11-year period, while the second data set is Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) and covers six years of information. Using these data, parameters such as wind speed, significant wave height (SWH) and mean wave period (MWP) are analyzed. The following marine areas are targeted: Ionian Sea, Aegean Sea, Sea of Crete, Libyan Sea and Levantine Sea, near the coastal environment of the Greek islands. Initially, 26 reference points were considered. For a more detailed analysis, the number of reference points was narrowed down to 10 that were considered more relevant. Since in the island environments the resources are in general rather limited, the proposed work provides some outcomes concerning the wind and wave energy potential and the synergy between these two natural resources in the vicinity of the Greek islands. From the analysis performed, it can be noticed that the most energetic wind conditions are encountered west of Cios Island, followed by the regions east of Tinos and northeast of Crete. In these locations, the annual average values of the wind power density (Pwind) are in the range of 286–298.6 W/m². Regarding the wave power density (Pwave), the most energetic locations can be found in the vicinity of Crete, north, south and southeast of the island. There, the wave energy potential is in the range of 2.88–2.99 kW/m.