AGH (Akademia Górniczo-Hutnicza) University of Science and Technology: Journals
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Decomposition of displacement vectors from InSAR images of mining areas – a case study
Full text linkMining activities are a major anthropogenic driver of ground surface deformation, often resulting in complex subsidence phenomena that are difficult to characterize using conventional geodetic methods. Interferometric synthetic aperture radar (InSAR) provides a powerful means of detecting displacements over large areas, but decomposing its line-of-sight (LOS) measurements into full 3D displacement vectors remains a challenge, especially when limited to data from two satellite tracks. This paper presents an iterative decomposition algorithm that supplements the classical two-LOS system with an empirical relationship between horizontal displacement components and the slope of the subsidence trough, derived from established mining deformation theories. The algorithm is validated through both a theoretical “blind” test case and three real-life examples of mining-induced seismic deformation in the Legnica-Głogów Copper District (LGCD), Poland. The results show that the proposed method significantly improves the accuracy of displacement vector estimation compared to classical decomposition techniques. This approach not only enhances our understanding of mining-induced ground movements but also offers practical benefits for ground surface deformation monitoring and hazard assessment in subsidenceprone regions
Rynek energii, ekonomia i prognozowanie gospodarki
Full text linkFundamentalnym elementem trwającej w Polsce transformacji energetycznej są procesy ekonomiczne, obejmujące zarówno kwestie finansowania i optymalizacji kosztów systemowych, jaki maksymalizacji pozytywnych efektów zewnętrznych. W artykule przedstawiono doświadczenia naukowo-badawcze autorów w zakresie wspomnianej problematyki. Omówiono projekt Obserwatorium Transformacji Energetycznej finansowany w ramach konkursu strategicznego programu badań naukowych i prac rozwojowych „Społeczny i gospodarczy rozwój Polski w warunkach globalizujących się rynków” GOSPOSTRATEG przez Narodowe Centrum Badań i Rozwoju. Przedstawiono także wieloletnie doświadczenie Zespołu Modelowania Systemów Energetycznych AGH (ESMLabAGH) w konstruowaniu wielowymiarowych modeli paliwowo--energetycznych. Zaprezentowane wyniki dowodzą unikalnego potencjału AGH w obszarze monitorowania i programowania ekonomiczno-technologicznych aspektów TE w Polsce, który pozwala w większym stopniu stosować zasadę evidence-based policymaking do kreowania polityki klimatycznej i gospodarczej w wymiarze krajowym,regionalnym i lokalnym
Inteligentna dystrybucja
Full text linkAby transformacja energetyczna (TE) była możliwa, oprócz wykorzystania źródeł odnawialnych konieczne jest zastosowanie m.in. nowych technologii pomiarowych, komunikacyjnych i analitycznych. Efektywna transformacja nie zaistnieje bez inteligentnej sieci elektroenergetycznej. Prognozuje się, że do 2030 r. w Unii Europejskiej pojawi się kilkadziesiąt milionów pojazdów elektrycznych i pomp ciepła, nastąpi znacząca elektryfikacja transportu, przemysłu oraz sfery komunalnej. TAURON Dystrybucja S.A. od co najmniej kilku lat prowadzi wiele działań inwestycyjnych oraz badawczo-rozwojowych w tym obszarze. Jednym z przykładów jest projekt mikrosieci. Firma realizuje szereg inicjatyw wspierających rozwój inteligentnej infrastruktury sieciowej, zgodnie z założeniami Strategicznej Agendy Badawczej TAURON Dystrybucja S.A. Kluczowym zadaniem Spółki jest zapewnienie i utrzymanie wysokich standardów jakości energii elektrycznej w zmiennym otoczeniu dynamicznego rozwoju OZE. W tym celu konieczne będzie wytworzenie nowych narzędzi, które pomogą OSD w pełnieniu funkcji moderatora sieci. Komisja Europejska ogłosiła, że zamierza wspierać unijnych operatorów systemu przesyłowego (OSP) i operatorów systemu dystrybucyjnego (OSD) w procesie tworzenia cyfrowego bliźniaka (digital twin) europejskiej sieci elektroenergetycznej
Developing explainablemachine learning model usingaugmented concept activation vector
Full text linkMachine learning models use high-dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made by these models. We propose a method for measuring the correlation between high-level concepts and the decisions made by a machine learning model. Our method can isolate the impact of a given high-level concept and accurately measure it quantitatively. Additionally, this study aims to determine the prevalence of frequent patterns in machine learning models, which often occur in imbalanced datasets. We have successfully applied the proposed method to fundus images and managed to quantitatively measure the impact of radiomic patterns on the model’s decisions
A PARALLEL APPROACH FOR METAHEURISTICS SOLVING THE LABS PROBLEM USING CPU AND GPU
Full text linkThe paper contributes to solving the low autocorrelation binary sequence (LABS) problem that remains an open hard-optimization problem with many applications. The current direction of research is focused on developing algorithms dedicated to parallel architectures such as GPGPU or multi-core CPUs. The paper follows this direction and proposes new heuristics developed from the steepest-descent local search algorithm that extends the notion of a neighborhood of a given sequence. The introduced algorithms utilise the parallel nature of multicore CPUs and provide an effective method of solving the LABS problem. The efficiency levels of SDSL and the new algorithm are presented; to ensure an effective comparison, they were both implemented in the same manner. The comparison shows that exploring the larger neighborhood improves the efficiency of the search method
Non-history-based DEM model for predictions of numerical earthquakes
Full text linkStick-slip phenomena roughly describe the behavior of a tectonic fault. A simplified model of stick-slip events is often assumed in laboratory experiments and numerical simulations of laboratory earthquakes. This work proposes a more advanced approach. The Discrete Element Method (DEM) was used to generate a numerical model for simulating the laboratory earthquakes in which the granular layer was taken into account. The proposed model takes into account an irregular, random pattern of stress increase and decrease in such a system. At 5,000 selected, regularly spaced time points, the so-called “checkpoints”, 25 parameters were measured, describing the average state of all particles forming the numerical fault at a given moment. The created dataset was used to train the Random Forest algorithm, and then, as part of the tests, this algorithm was used to predict subsequent stick-slip events. The algorithm made predictions solely on the basis of information about the current parameters of the particles. Importantly, the predictions made did not use the history of previous stick-slip events. Feature Importance and SHapley Additive exPlanations (SHAP) were used to assess the contribution of individual particle physical parameters to the prediction results
Rare earth elements in supergene zone: a case study of the xenotime-(Y)-chernovite-(Y) solid solution from Rędziny, Sudetes, SW Poland
Full text linkRare earth element (REE) mineralization has been documented at Rędziny, located on the eastern margin of the Karkonosze granite (Sudetes, Poland). Minerals of the REE[(As,P)O₄] group with a tetragonal zircontype structure, primarily xenotime-(Y)-chernovite-(Y), Y(PO4)-Y(AsO4) solid solutions, have been identified. These minerals occur as euhedral grains and also as intergrowths with secondary arsenates and silicates, filling cracks, fractures, and voids. Their textural diversity and paragenetic relationships with Ca-, Cu-, Pb-, and Bi-bearing arsenates indicate a crystallization sequence involving successive mineral-forming episodes. Local enrichments in REEs have also been recorded in common supergene arsenates, such as Ca(Pb)-Cu phases including conichalcite (CaCu(AsO4)(OH)) and duftite (PbCu(AsO4)(OH)). Minerals of the xenotime-(Y)-chernovite-(Y) solid solution correspond to intermediate compositions, with the chernovite end-member molar fraction ranging from 0.46 to 0.89. Based on EPMA analyses, yttrium is the dominant cation, accompanied by considerable amounts of middle rare earth elements (MREEs), especially neodymium (up to 12.60 wt.%; 0.21 apfu), samarium (up to 10.39 wt.%; 0.167 apfu), and gadolinium (up to 6.72 wt.%; 0.107 apfu). Some chemical compositions also show a trend towards the gasparite-(LREE) (LREE(AsO4)) compositional field. Xenotime-(Y)-chernovite-(Y) minerals display microporosity, likely resulting from dissolution, metasomatic alteration, and subsequent recrystallization. The REEs at Rędziny likely originated from both evolved, late-stage hydrothermal fluids and rocks of the metamorphic envelope of the Karkonosze granite, where REEs were mobilized by post-magmatic fluids. Subsequent supergene processes may have further enhanced secondary enrichment
Exploratory analysis of elements in incineration bottom ash with numerous values below the detection limit using selected substitution methods
Full text linkThis study investigates the influence of substitution methods for left-censored values on exploratory data analysis (EDA) of the incineration bottom ash (IBA). IBA, a by-product of municipal solid waste incineration, contains a wide range of economically valuable elements, many of which are frequently reported below detection limits due to analytical constraints. The study aims to evaluate the impact of different substitution methods on descriptive statistics, correlation analysis, and regression modeling outcomes. Four widely used substitution approaches were compared: (i) replacement with half of the detection limit, (ii) random values from a uniform distribution, (iii) robust regression on order statistics (ROS), and (iv) tobit regression (applied in both small and large variants). Five trace elements with different proportions of censored values (13–67%) were analyzed using a dataset of 52 weekly samples collected throughout 2021 at the Krakow Thermal Waste Treatment Plant. The impact of each method was assessed using descriptive statistics, Pearson correlation matrices, and multiple linear regression models. Additional analyses incorporated 11 auxiliary elements to enhance correlation and regression model robustness.The results show that substitution methods significantly affect data distributions, particularly for elements with high censoring rates. ROS and tobit regression produced more stable statistical outputs and narrower histograms compared to simpler methods. Furthermore, regression model performance improved with substitution compared to raw data, with tobit methods demonstrating the highest accuracy for elements with strong inter-element correlations. The findings provide methodological guidance for reliable data handling in IBA analysis and recovery assessments
