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Abstract: The study evaluates relative heat stress in the Natura 2000 site Kras under various climate change scenarios using Multiscale Geographically Weighted Regression (MGWR). Optical and thermal satellite imagery and five future air temperature scenarios were utilized to downscale thermal conditions and evaluate relative heat stress. Results indicate significant spatiotemporal variability in LST, with the southeastern region being particularly susceptible to elevated heat stress. Projections show an overall increase in heat stress due to climate change. The study emphasizes the need for spatially explicit analyses and adaptive strategies to mitigate heat stress impacts on ecosystems and human populations.
Keywords: land surface temperature, remote sensing, spatial modelling, MGWR, NDVI
Published in DKUM: 21.01.2026; Views: 0; Downloads: 0
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Abstract: The technology of information modelling and knowledge bases addresses the complexities of modelling in digital transformation and digital innovation, reaching beyond the traditional borders of information systems and academic research in computer science. This book presents 21 papers from the 31st International conference on Information Modeling and Knowledge Bases (EJC 2021), hosted by the Department Informatik of the University of Applied Sciences in Hamburg, Germany, and held as a virtual event from 7 to 9 September 2021 due to restrictions caused by the Corona virus. The conference provides a research forum for academics and practitioners dealing with information and knowledge to exchange scientific results and experiences, and EJC 2021 covered a wide range of themes extending knowledge discovery through conceptual modeling, knowledge and information modeling and discovery, linguistic modeling, cross-cultural communication and social computing, environmental modeling and engineering, and multimedia data modeling and systems. As always, the conference was open to new topics related to its main themes, meaning the content emphasis of the EJC conferences is always able to adapt to the changes taking place in the research field, and the 21 papers included here after rigorous review, selection and upgrading are the result of presentations, comments, and discussions during the conference. Providing an up to the minute overview of the technology of information modeling and knowledge bases, the book will be of interest to all those working in the field.
Keywords: information modelling, knowledge bases, knowledge management, expert systems, artificial intelligence, information systems, database
Published in DKUM: 20.01.2026; Views: 0; Downloads: 0
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Abstract: The volume and complexity of information, together with the number of abstraction levels and the size of data and knowledge bases, grow continually. Data originating from diverse sources involves a combination of data from traditional legacy sources and unstructured data requiring backwards modeling, meanwhile, information modeling and knowledge bases have become important contributors to 21st-century academic and industrial research. This book presents the proceedings of EJC 2023, the 33rd International Conference on Information Modeling and Knowledge Bases, held from 5 to 9 June 2023 in Maribor, Slovenia. The aim of the EJC conferences is to bring together experts from different areas of computer science and from other disciplines that share the common interest of understanding and solving the problems of information modeling and knowledge bases and applying the results of research to practice. The conference constitutes a research forum for the exchange of results and experiences by academics and practitioners dealing with information and knowledge bases. The topics covered at EJC 2023 encompass a wide range of themes including conceptual modeling; knowledge and information modeling and discovery; linguistic modeling; cross-cultural communication and social computing; environmental modeling and engineering; and multimedia data modeling and systems. In the spirit of adapting to the changes taking place in these areas of research, the conference was also open to new topics related to its main themes. Providing a current overview of progress in the field, this book will be of interest to all those whose work involves the use of information modeling and knowledge bases.
Keywords: information modelling, knowledge bases, knowledge management, expert systems, artificial intelligence, information systems, database
Published in DKUM: 20.01.2026; Views: 0; Downloads: 0
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Abstract: This paper shows the possibility of applying artificial intelligence methods in milling, as one of the most common machining operations. The main goal of the research is to obtain reliable intelligent models for selected output characteristics of the milling process, depending on the input parameters of the process: depth of cut, cutting speed and feed to the tooth. One of the problems is certainly determining the value of input parameters of the processing process depending on the objective function, i.e. the output characteristics of the milling process. The selected objective functions in this paper are the temperature in the cutting zone and arithmetic mean roughness of the machined surface. The paper examines the accuracy of three models based on artificial intelligence, obtained through artificial neural networks, fuzzy logic, and genetic algorithms. Based on the mean percentage error of deviation, conclusions were drawn as to which of the three models is most adequately applied and implemented in appropriate process systems, which are based on artificial intelligence.
Keywords: artificial intelligence, artificial neural networks (ANN), fuzzy logic, genetic algorithms (GA), face milling, modelling, surface roughness, cutting temperature
Published in DKUM: 15.01.2026; Views: 0; Downloads: 1
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Abstract: Reconfigurable Manufacturing Systems (RMSs) are widely used to produce small batches of customized products in the current manufacturing environment. We comprehensively optimized the assembly transport strategy, production process, and production configuration of a reconfigurable flow shop (RFS). Firstly, three assembly transfer strategies, one-to-one, one-to-many, and many-to-many, are proposed for an RFS, given the specific process limitations. In addition, a production simulation model of the RFS is established by the Plant Simulation software to verify and compare those three strategies with realistic production constraints considered. Moreover, the production processes are optimized, and the optimal buffer configuration and vehicle configuration are optimized by the design of experiment (DOE) method. After the optimization processes, the throughput and facility utilization under each strategy increases significantly. Additionally, the optimal buffer size and vehicle quantity under each strategy are determined and compared. The one-to-one strategy can maximize the production output, but it requires the most production resources. In addition, the many-to-many strategy is more efficient than the one-to-many strategy. Our study provides a variety of assembly transport strategies for an RFS and offers an efficient optimization method for production performance and production configuration.
Keywords: Reconfigurable Manufacturing Systems, discrete event simulation, assembly transport strategy, optimization, plant simulation, reconfigurable flow shop, production configuration, simulation modelling
Published in DKUM: 13.01.2026; Views: 0; Downloads: 0
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