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Abstract: Life-Cycle Assessment (LCA) is a methodology that is used extensively for evaluating the environmental impacts of products and processes throughout their lifetime. The method is highly dependent on the quality and accuracy of the underlying data. Moreover, the data acquisition process can be subjective, raising concerns about potential inconsistencies. In this study, we perform Benford’s law conformity tests (first digit) on all numerical data in ecoinvent, focusing on individual compartments (air, water, soil, and natural resources) and environmental elementary flows (carbon, toxic substances, greenhouse gases, and heavy metals), and discrepancies across continents are examined. Life Cycle Inventory data met the requirements of Benford’s law and generally exhibited high conformity. Substantial differences in conformity were observed between Africa and Europe. Individual processes and measurements were inspected to further isolate potential sources of the non-conformity. The statistical significance of the results was increased using open-source databases available on OpenLCA Nexus, including WorldSteel, OzLCI2019, ELCD, NEEDS, BioenergieDat, and Exiobase. Finally, the Environmental Performance Index (EPI) was used, and a strong correlation between continental Benford conformity results and corresponding EPI scores was observed. The findings suggest that discrepancies in conformity across continents reflect differences in data transparency and reporting practices. European datasets generally show higher conformity, likely owing to the use of more standardized methodologies. In contrast, data from regions with limited infrastructure or less established LCA practices tend to show lower conformity. Benford’s Law offers a simple and computationally efficient alternative to conventional data quality assessments without requiring additional metadata or probabilistic modeling. Its application can support the detection of systemic biases and improve the reliability of LCA-based indicators such as environmental product declarations.
Keywords: anomaly detection, Benford’s law, data integrity
Published in RUP: 12.06.2025; Views: 30; Downloads: 2
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Abstract: Fig tree is fruit species, widely distributed throughout the Adriatic region, and is valued as a key component of the Mediterranean diet. Despite its importance for the region’s fruit-growing industry, the genetic makeup of Adriatic fig cultivars has not yet been thoroughly investigated. A comprehensive molecular characterization of 213 fig accessions from the Eastern Adriatic Coast was conducted to assess genotype-specific molecular profiles and genetic diversity using seven Simple Sequence Repeat markers. Aim of this research was to catalogue the cultivars and clarify cases of synonymy and homonymy. The research included three genebank collections: the Institute of Adriatic Crops in Split (Croatia), the Institute of Agriculture and Tourism in Poreč (Croatia), and the Centre for Subtropical Cultures Bar, Biotechnical Faculty University of Montenegro, alongside additional accessions collected from family farms and nursery in Croatia and Slovenia. The analysis revealed 122 alleles, indicating substantial genetic diversity. A total of 80 unique genotypes were identified, along with 51 cases of synonymy or homonymy and 24 accessions with unknown nomenclature. The phylogenetic tree highlighted the heterogeneous nature of the fig population along the East Adriatic Coast. Notably, each of the three major clusters contained accessions from all three countries, underscoring the high genetic diversity across the region. These defined molecular profiles provide a foundation for the effective conservation of fig cultivars, the establishment of high-quality mother blocks, nursery production, and future breeding programs.
Keywords: plant genetic resources, fig germplasm, genetic profiles, SSR markers, synonyms and homonyms
Published in RUP: 11.06.2025; Views: 32; Downloads: 1
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Abstract: In this paper we introduce a problem closely related to the Cage Problem and the Degree Diameter Problem. For integers k ≥ 2, g ≥ 3 and d ≥ 1, we define a (k; g, d)-graph to be a k-regular graph with girth g and diameter d. We denote by n₀(k; g, d) the smallest possible order of such a graph, and, if such a graph exists, we call it a (k; g, d)-cage. In particular, we focus on (k; 5, 4)-graphs. We show that n₀(k; 5, 4) ≥ k² + k + 2 for all k, and report on the determination of all (k; 5, 4)-cages for k = 3, 4 and 5 and of examples with k = 6, and describe some examples of (k; 5, 4)-graphs which prove that n₀(k; 5, 4) ≤ 2k² for infinitely many k.
Keywords: cages, girth, degree-diameter problem
Published in RUP: 10.06.2025; Views: 60; Downloads: 1
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Abstract: Study of brain function often involves analyzing task-related switching between intrinsic brain networks, which connect various brain regions. Functional brain connectivity analysis methods aim to estimate these networks but are limited by the statistical constraints of windowing functions, which reduce temporal resolution and hinder explainability of highly dynamic processes. In this work, we propose a novel approach to functional connectivity analysis through the explainability of EEG classification. Unlike conventional methods that condense raw data into extracted features, our approach inflates raw EEG data by decomposition into meaningful components that explain processes in the application domain. To uncover the brain connectivity that affects classification decisions, we introduce a new method of dynamic influence data inflation (DIDI), which extracts signals representing interactions between electrode regions. These inflated data are then classified using an end-to-end neural network classifier architecture designed for raw EEG signals. Saliency map estimation from trained classifiers reveals the connectivity dynamics affecting classification decisions, which can be visualized as dynamic connectivity support maps for improved interpretability. The methodology is demonstrated on two publicly available datasets: one for imagined motor movement classification and the other for emotion classification. The results highlight the dual benefits of our approach: in addition to providing interpretable insights into connectivity dynamics it increases classification accuracy.
Keywords: EEG, functional connectivity, data inflation, classification, explainability, saliency maps
Published in RUP: 04.06.2025; Views: 105; Downloads: 8
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Abstract: The main goal of this work is to create a dataset of Uzbek language verbs. This dataset stores information about which words verbs are derived from and with which affixes. The affixes are classified into distinct categories. With the help of this dataset, it is possible to determine from which parts of speech each Uzbek verb is derived and with which affixes. It also plays a key role in identifying verbs in Uzbek language texts and developing rule-based models for their analysis. Additionally, this dataset plays a key role in building various artificial intelligence models for the morphological and syntactic analysis of Uzbek language texts. Verbs play a crucial role in learning any language; therefore, students in schools and higher education institutions can also use this dataset during the learning process. The obtained dataset serves as a valuable resource for researchers and practitioners interested in Uzbek language processing tasks.
Keywords: verb phrase, Uzbek language, Uzbek web corpus, verb form, verb affixes
Published in RUP: 02.06.2025; Views: 100; Downloads: 7
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