Tibor Krenicky

Dr. Tibor Krenicky

Technical University of Košice, Košice, Košický, Slovakia
orcid

Research Focus & Metrics

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 September 2006 in Journal of Magnetism and Magnetic Materials
Verified
Journal Article
Closed access
ISIvan Skorvanek
Ivan Skorvanek
JMJozef Marcin
Jozef Marcin
Tibor KrenickyTibor Krenicky
Tibor Krenicky
JKJozef Kováč
Jozef Kováč
PŠPeter Švec
Peter Švec
DJDušan Janičkovič
Dušan Janičkovič

17 March 2003 in Physica Status Solidi (a)
Verified
Journal Article
Closed access
PDF file
ISIvan Skorvanek
Ivan Skorvanek
PŠPeter Švec
Peter Švec
JMJ. Marcin
J. Marcin
JKJ. Kováč
J. Kováč
Tibor KrenickyTibor Krenicky
Tibor Krenicky
MDM. Deanko
M. Deanko

1 February 2015 in Manufacturing Technology
Verified
Journal Article
Open access
PDF file
Tibor KrenickyTibor Krenicky
Tibor Krenicky
Spatial measurement and evaluation of the surface profile parameters provide valuable and useful information about the relationship between the geometric characteristics of the surface and its functional properties. Evaluating the structure of...

1 December 2011 in Key Engineering Materials
Verified
Journal Article
Closed access
PDF file
Tibor KrenickyTibor Krenicky
Tibor Krenicky
Miroslav RimárMiroslav Rimár
Miroslav Rimár
Vibrations of cutting head act as co-factor causing striation of machined work-piece surface, deteriorating its surface quality. In this paper, measurement and evaluation of vibrations generated during the AWJ machining is performed using specialized diagnostic systems. In conclusion, means to minimize influence of cutting-head vibrations on the machined work-piece surface quality are discussed.

26 September 2024 in Sustainability
Verified
Journal Article
Open access
PDF file
MTMagdalena Tutak
Magdalena Tutak
Tibor KrenickyTibor Krenicky
Tibor Krenicky
RPRastislav Pirník
Rastislav Pirník
Jarosław BrodnyJarosław Brodny
Jarosław Brodny
WGWiesław Grebski
Wiesław Grebski
During energy transition, where sustainability and environmental protection are increasingly prioritized, ensuring safety in coal exploitation remains a critical issue, especially in the context of worker safety. This research focuses on predicting methane concentrations in underground mines, which is vital for both safety and operational efficiency. The article presents a methodology developed to predict methane concentrations at specific points in mine workings using artificial neural networks. The core of this methodology is a forecasting model that allows for the selection and adjustment of the neural network to the phenomenon being studied. This model, based on measurements of ventilation parameters, including methane concentrations in a given area, enables the prediction of gas concentrations at measurement points. The results indicate that with appropriate neural network selection and based on ventilation measurements, it is possible to forecast methane concentrations at acceptable levels in selected excavation points. The effectiveness of these forecasts depends on their timing and the input data to the model. The presented example of applying this methodology in a real mine working demonstrates its high efficiency. The best results were obtained for a 5 min forecast, with slightly less accuracy for longer times (10, 15, 30, and 60 min), though all results remained at an acceptable level. Therefore, it can be concluded that the developed methodology can be successfully applied in underground mining operations to forecast dangerous methane concentrations. Its implementation should improve mining efficiency by reducing instances of exceeding permissible methane concentrations and enhance occupational safety.

29 October 2022 in Management Systems in Production Engineering
Verified
Journal Article
Open access
PDF file
Tibor KrenickyTibor Krenicky
Tibor Krenicky
LHLiudmyla Hrebenyk
Liudmyla Hrebenyk
VCVadym Chernobrovchenko
Vadym Chernobrovchenko
The Analytical Hierarchy Process (AHP) is one of the multi-criteria methods with tools that are effective for decision-making in complementary or alternative medicine (CAM). This approach, in combination with other available methods, such as the Utrecht Method, allows medical professionals and patients themselves to take a balanced choice of special treatment and health control. This is particularly important for pregnant women who suffer from nausea and vomiting during pregnancy (NVP). The mechanisms of toxicosis occur, and the way of treatment have certain uncertainties. The decision-making process for therapeutic care must consider several factors, the determination of which is the result of collaboration between the patient and the healthcare provider. This paper presents the results of a decision-making study regarding the use of the ginger medicinal plant in helping a woman with NVP symptoms. The research was conducted using the original questionnaire according to the methodology proposed by the developers of the Utrecht Method. The chosen respondents were healthcare professionals with experience in treating NVP and pregnant women. The Analytical Hierarchy Process (AHP) capabilities in decision-making were shown in an example of analyzing the benefits, risks, and side effects of using ginger during pregnancy. The combination of two methodological approaches allowed to successfully connect the proven facts of using CAM therapy with the subjective assessment of all stakeholders. The study demonstrates that this approach successfully uses decision-making by structuring hierarchical decision elements and weighing the criteria involved in the decision-making issue. The article examines the practicality and effectiveness of using AHP when making decisions regarding the treatment and improvement of the condition of pregnant women with NVP, and summarizes the quantitative and qualitative indicators of the effectiveness of this method. The presented combination of two methodological approaches in decision-making allows for choosing an optimized medical strategy for supporting pregnant women through CAM therapy.