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113. Experimental drought results in a decline of ectomycorrhizae of Quercus pubescens Willd.Tanja Mrak, Tina Unuk Nahberger, Olivera Maksimović, Hojka Kraigher, Mitja Ferlan, 2025, original scientific article Abstract: Experimental drought decreased the vitality of ectomycorrhiza and reduced the number of unique morphotypes. Quercus pubescens (Willd.) is an ectomycorrhizal (ECM) tree species that is capable of withstanding occasional drought events, but the response of its ectomycorrhiza to drought is not well known. An experiment with two rain exclusion plots and two natural precipitation regime plots was established in a secondary sub-Mediterranean oak forest. ECM roots were sampled before the experiment and after 11 months of rain exclusion. ECM root tips were divided into vital and non-vital and quantified. Morphoanatomical characterization and molecular identification were performed for vital ectomycorrhizae to obtain diversity indices and perform community analyses. Soil water content (SWC) in rain exclusion plots was reduced by approx. 6 vol.% relative to natural precipitation regime and was devoid of major peaks in SWC after rain events. After 11 months, ECM vitality and species richness were significantly reduced on rain exclusion plots compared to the natural precipitation regime while ECM community was reduced to a small subset of the most frequent morphotypes, with strongly decreased number of the unique morphotypes. The reduction of unique morphotypes as a result of rain exclusion may compromise the functional diversity of ectomycorrhiza in their role of nutrient uptake, while the reduction of ECM vitality may decrease the absorptive surface for water and nutrients.
Keywords: ectomycorrhizal fungi, Pubescent oak, Sub-Mediterranean, karst, drought stress, rain exclusion
Published in DiRROS: 12.12.2024; Views: 69; Downloads: 11
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114. Subjective assessment of sedentary behavior between theory and practice : pilot study using the “Sedentary meter”Ana Cikač, Kaja Teraž, Saša Pišot, 2024, original scientific article Abstract: Although sedentary behavior (SB) is still an under-researched area, some studies have shown a significant association between prolonged sitting and an increased risk of mortality, due to various causes, independent of physical activity. Despite the health risks, there are currently no specific guidelines for individuals to self-assess their SB. A pilot observational study was conducted as part of the “Knowledge for Health” event. A short online quiz “Sedentary meter” was developed, consisting of a pictorial scale to help event participants assess their daily sedentary time and to promote a better understanding of the associated health risks. The quiz questions were formulated based on the WHO definition of SB. The participants’ task was to subjectively estimate the amount of sedentary time in various types of SB on a typical day. The results obtained for SB could then be immediately compared with the figurative scale based on the WHO guidelines. The analysis confirmed SB (533.0±224.7 min/day) in all age groups, although possible differences according to the type of SB were noted. Despite statistically non-significant differences, those between age groups may indicate the extent to which SB can be individualized. The differences between age groups may indicate the importance of considering SB which can be targeted based on each age group's daily routine. The simple tool for accessing SB raised awareness of which specific type of SB accounts for the majority of participants' daily sedentary time. The self-critical acceptance of the “poor results” across all age groups shows the effectiveness of the initiative in raising awareness of SB issues.
Keywords: sedentary behavior, self-assessment, pilot study
Published in DiRROS: 12.12.2024; Views: 85; Downloads: 38
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115. Opt2Vec - a continuous optimization problem representation based on the algorithm's behavior : A case study on problem classificationPeter Korošec, Tome Eftimov, 2024, original scientific article Abstract: Characterization of the optimization problem is a crucial task in many recent optimization research topics (e.g., explainable algorithm performance assessment, and automated algorithm selection and configuration). The state-of-the-art approaches use exploratory landscape analysis to represent the optimization problem, where for each one, a set of features is extracted using a set of candidate solutions sampled by a sampling strategy over the whole decision space. This paper proposes a novel representation of continuous optimization problems by encoding the information found in the interaction between an algorithm and an optimization problem. The new problem representation is learned using the information from the states/positions in the optimization run trajectory (i.e., the candidate solutions visited by the algorithm). With the novel representation, the problem can be characterized dynamically during the optimization run, instead of using a set of candidate solutions from the whole decision space that have never been observed by the algorithm. The novel optimization problem representation is called Opt2Vec and uses an autoencoder type of neural network to encode the information found in the interaction between an optimization algorithm and optimization problem into an embedded subspace. The Opt2Vec representation efficiency is shown by enabling different optimization problems to be successfully identified using only the information obtained from the optimization run trajectory.
Published in DiRROS: 11.12.2024; Views: 71; Downloads: 21
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117. Using machine learning methods to assess module performance contribution in modular optimization frameworksAna Kostovska, Diederick Vermetten, Peter Korošec, Sašo Džeroski, Carola Doerr, Tome Eftimov, 2024, original scientific article Abstract: Modular algorithm frameworks not only allow for combinations never tested in manually selected algorithm portfolios, but they also provide a structured approach to assess which algorithmic ideas are crucial for the observed performance of algorithms. In this study, we propose a methodology for analyzing the impact of the different modules on the overall performance. We consider modular frameworks for two widely used families of derivative-free black-box optimization algorithms, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and differential evolution (DE). More specifically, we use performance data of 324 modCMA-ES and 576 modDE algorithm variants (with each variant corresponding to a specific configuration of modules) obtained on the 24 BBOB problems for 6 different runtime budgets in 2 dimensions. Our analysis of these data reveals that the impact of individual modules on overall algorithm performance varies significantly. Notably, among the examined modules, the elitism module in CMA-ES and the linear population size reduction module in DE exhibit the most significant impact on performance. Furthermore, our exploratory data analysis of problem landscape data suggests that the most relevant landscape features remain consistent regardless of the configuration of individual modules, but the influence that these features have on regression accuracy varies. In addition, we apply classifiers that exploit feature importance with respect to the trained models for performance prediction and performance data, to predict the modular configurations of CMA-ES and DE algorithm variants. The results show that the predicted configurations do not exhibit a statistically significant difference in performance compared to the true configurations, with the percentage varying depending on the setup (from 49.1% to 95.5% for modCMA and 21.7% to 77.1% for DE)
Keywords: evolutionary computation, modular algorithm frameworks, DE
Published in DiRROS: 11.12.2024; Views: 79; Downloads: 30
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118. Geometric matching and bottleneck problemsSergio Cabello, Siu-Wing Cheng, Otfried Cheong, Christian Knauer, 2024, published scientific conference contribution Abstract: Let $P$ be a set of at most $n$ points and let $R$ be a set of at most $n$ geometric ranges, such as disks or rectangles, where each $p \in P$ has an associated supply $s_{p} > 0$, and each $r \in R$ has an associated demand $d_{r} > 0$. A (many-to-many) matching is a set ${\mathcal A}$ of ordered triples $(p,r,a_{pr}) \in P \times R \times {\mathbb R}_{>0}$ such that $p \in r$ and the $a_{pr}$'s satisfy the constraints given by the supplies and demands. We show how to compute a maximum matching, that is, a matching maximizing $\sum_{(p,r,a_{pr}) \in {\mathcal A}} a_{pr}$. Using our techniques, we can also solve minimum bottleneck problems, such as computing a perfect matching between a set of $n$ red points $P$ and a set of $n$ blue points $Q$ that minimizes the length of the longest edge. For the $L_\infty$-metric, we can do this in time $O(n^{1+\varepsilon})$ in any fixed dimension, for the $L_2$-metric in the plane in time $O(n^{4/3 + \varepsilon})$, for any $\varepsilon > 0$.
Keywords: many-to-many matching, bipartite, planar, geometric matching, approximation
Published in DiRROS: 11.12.2024; Views: 54; Downloads: 23
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119. Izpad dohodka v gozdarskem sektorju v obdobju od 16. 3. 2020 do 30. 6. 2020Nike Krajnc, Darja Stare, Špela Ščap, Matevž Triplat, 2020, professional article Keywords: covid-19, tržne razmere, les, gozdarstvo, izpad dohodka
Published in DiRROS: 11.12.2024; Views: 69; Downloads: 19
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