Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly lines. Proper estimation supports the right use of resources and more accurate and cost-effective delivery to the customers. This paper presents the prediction of OEE by comparing human prediction with one of the techniques of supervised machine learning through a real-life example. In addition to descriptive statistics, takt time-based decision trees are applied and the target-oriented OEE prediction model is presented. This concept takes into account recent data and assembly line targets with different weights. Using the model, the value of OEE can be predicted with an accuracy of within 1% on a weekly basis, four weeks in advance.

At the end of the 19th and at the beginning of the 20th centuries Budapest the capital of the Hungarian kingdom underwent a significant development. During this period the number of inhabitants of the city multiplied, as a consequence several new city districts were constructed. The functional and structural arrangement of these new residential buildings was very similar in respect of the materials used and the technologies applied. A considerable part of these buildings still constitutes the determining element of the townscape. It is one of the most burning issues of the present day Budapest that a part of these buildings are in very bad condition. Due to the limited financial resources it is an essential task to set up priority ranking of the renovation, upgrading and renewal of buildings with similar arrangement. In our case the traditional (two-valued) logic is unsuitable for modelling or handling the given phenomenon. When describing the status of a load-bearing building structure the terminology of "appropriate condition" cannot be handled by Boolean logic, since it cannot be strictly determined where the borderline is between appropriate or inappropriate condition. In the case of the majority of the linguistic characteristics of this type there is a well noticeable joint element, which expresses a kind of inaccuracy or uncertainty. For such tasks the application of fuzzy signatures is considered to be one of the possible solutions. The fuzzy type inaccuracy somehow links with human thinking. Fuzzy logic is an extension of the two-valued logic, which makes it possible to define transitions, too. By applying the fuzzy singleton signatures a status-determining and ranking model was created, which is suitable for the qualification and ranking of buildings of similar age and structural arrangement. This model was used for the first time on a database, which is based on expert opinions, relating to a given stock of residential building. The modelling of the load-bearing structural condition of the residential buildings is a complicated task, the components where of are well-structured and a hierarchical structure can be built therefrom. Based on the foregoing a tree-structure, necessary for the examination of the load-bearing structures of buildings, has been proposed. In this framework primary and secondary structures were distinguished during the examination, which would be linked with qualification values based on their arrangement, materials and conditions (in accordance with fuzzy logic). The significance of the load-bearing structure of various building elements was taken into account by using relevance weights. For the aggregation of the fuzzy singleton signature the Weighted Relevance Aggregation Operation (WRAO) was used. The aggregate status descriptors, characteristic of the status of a building, provided the basis for the ranking of buildings in case of a certain stock of buildings. Discrepancies of the results achieved by method using membership functions of approximate and fine set of values, conclusions were drawn as to when and which method should be used in practise. The results of this study may make it easier to realize rehabilitation ideas of similar residential areas, and also can be efficiently used during utilization and renewal of certain buildings in bad condition. Copyright © 2013 IAHS.

In agriculture, computer vision and image processing are essential for monitoring crops and controlling robots and actuators. In this work, the detection of ripe tomato fruit was the main aim. During the tomato-ripping process, the green tomato turns to red in several color stages (Ambrus et al., 2024). While the chlorophyll concentration decreases, the lycopene concentration increases. The sugar and the acid increase parallel to lycopene. The RGB camera can capture the process but needs to convert HSV color space to identify the tomato. The successful identification depends on the direct illumination volume. The experiment contains 4 ripe tomatoes and 15 different artificial illumination levels. The measurements show that the results are similar to or constantly above 3,000 lx illumination. However, under 3,000 lx, the detected size of tomatoes looks smaller and smaller depending on the weakness of illumination. Around 1,600 lx, it is possible to measure only half of the real size of the tomato. It shows that using the right amount of light is crucial to precise measurement in HSV color space. This research highlights the critical importance of proper illumination in ensuring accurate image analysis for tasks like industrial tomato segmentation. It emphasizes the need for adaptable lighting solutions, particularly in varying weather conditions, and the balance between adequate light and energy efficiency.

Scientific research focusing on ice hockey is growing, although a complex model describing team performance is yet to be added to the knowledge base. The purpose of the study is to finalize the authors' proposed model of ice hockey team performance and gain insights on how the included factors contribute to the operation of the team and the coach. Based on the processed literature, it was assumed that the psychological aspect is among the key factors contributing to team performance. Semi-structured interviews were conducted with highly qualified experts on Hungarian ice hockey (five national team coaches and five senior national team players). The results indicate that the psychological factors of the coach and the team are essential for high team performance, along with the influence ability of both sides, creating two-way communication and feedback loops. The practical knowledge of the coach was emphasized over theoretical knowledge, and the team's tactical knowledge was emphasized over technical knowledge. It also emerged that the coach must know the team well in order to make appropriate decisions. The role of the coach is no longer to act as a stressor, but rather to set a good example as a role model while remaining open to feedback from the team's side. It was concluded that although many psychological methods are available to improve performance, the use of these methods has not yet been sufficiently exploited. While the use of these methods could improve performance, the team could experience more success and make sporting activity a fundamental part of players' health through bonding and belonging.

Local differences in air pollution levels of different positions of even close points along a street can be dramatic due to mainly meteorological parameters and local geometry. Air pollution monitoring systems provide a huge amount of temporal data, but the number of monitoring sites is far from sufficient for fine spatial investigations. On the other hand, for reliable air pollution modelling and correct prediction of expositions, a more detailed spatial distribution of samples is needed. Small-scale differences and street canyons are often investigated by models. However, such small-scale real field data from direct measurements are rarely available. In this paper we provide evidence by measurements to the existence of significant small-scale differences in air pollution levels and provide data for model validation. Hence, we performed measurements inside a single crossing and along three main roads in the city of Győr. Air samples were sucked into Tedlar bags and NOx concentration was determined by a gas analyser using a chemiluminescent method. On the sampling days manual traffic counting and noise measurements were performed and meteorological data were also taken at the same time. Daily rhythm and spatiotemporal variation of NOx expositions were investigated and compared to traffic data and data from the two monitoring containers of Győr. We observed a similar daily profile as expected in an average workday out of heating season: a sharp peak in the morning and a wider rise of nitrogen oxide concentration in the afternoon. Canyon effects were detected inside the investigated crossing. It caused a difference in pollutant levels of the two sides of the street. Despite a relatively small number of sampling sites, significant differences were detected both inside the crossing level and between main streets at p = 0.055.

Haptic keyboards bring the same touch sensation for pianists such acoustic grand pianos. One of the main parts of a haptic device is the actuator, that exerts fairly high and fast changing force. This paper presents the design issues of a voice coil motor type actuator assembly. Magnetic field simulations are carried out to investigate the interaction between actuator units.

Can the advancement of electromobility (e-mobility) be the key to achieving sustainable development and transportation in urban areas? While electric vehicles typically produce less environmental impact compared to conventional cars—such as reduced noise and air pollution—they still generate various externalities, including congestion costs, accidents, and adverse effects on landscapes and public spaces. In contrast, light e-micro mobility vehicles, particularly e-scooters, have emerged as a new transportation alternative in many cities worldwide and appear to facilitate a shift towards more sustainable modes of transport due to their advantages. However, e-micromobility can only contribute to sustainable urban development if careful planning and development are implemented. This study explores the history, current status, and future potential of e-micro mobility advancements. As part of the primary research, we assessed the socio-economic profiles and usage behaviors of e-micro mobility users in Győr, Hungary. We also examined the share of e-micro mobility users in the modal split, their travel patterns, and their needs and desires. The findings indicated that e-micro mobility vehicles primarily substitute walking and public transport, with fewer trips being made by private cars. Ultimately, the study concluded that a shift from car usage to e-micro mobility is achievable, provided there are suitable policy frameworks and sustainable urban and transportation development strategies in place.

In watershed modeling research, it is practical to subdivide a watershed into smaller units or sub-watersheds for modeling purposes. The ability of a model to simulate the watershed system depends on how well watershed processes are represented by the model and how well the watershed system is described by model input. This study is conducted to evaluate the impact of watershed subdivision and different weather input datasets on streamflow simulations using the soil and water assessment tool model. For this purpose, Cuhai-Bakonyer watershed was chosen as a study area. Two climate databases and four subdivision variations levels were evaluated. The model streamflow predictions slightly effected by subdivision impact. The climate datasets showed significant differences in streamflow predictions.

Climate change and irregular precipitation patterns have increasingly threatened groundwater sustainability in semi-arid regions like the Eastern Mediterranean. Specifically, in coastal Syria, the lack of quantitative understanding regarding aquifer recharge mechanisms hinders effective water resource management. To address this, this study investigates the dynamic relationship between rainfall and groundwater levels in the Al-Hsain Basin coastal plain using 48 months of monitoring data (2020–2024) from 35 wells. We employed a unified analytical framework combining statistical methods (correlation, regression) with advanced time–frequency techniques (Wavelet Coherence) to capture recharge behavior across diverse Quaternary, Neogene, and Cretaceous strata. The results indicate strong climatic control on groundwater dynamics, particularly in shallow Quaternary wells, which exhibit rapid recharge responses (lag < 1 month). In contrast, deeper aquifers showed delayed and buffered responses. A dual-variable model incorporating temperature significantly improved prediction accuracy (R² = 0.97), highlighting the role of evapotranspiration. These findings provide a transferable diagnostic framework for identifying recharge zones and supporting adaptive groundwater governance in data-scarce semi-arid environments.