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Abstract: In the inland waters of the Balkans, many brown trout populations have been severely depleted. Therefore, identifying potential threats to their continued survival and ranking populations based on their biological and evolutionary importance enables setting conservation priorities. To assess the sustainability of the brown trout populations in the territory of Serbia (central Balkans), a modification of the ESHIPPO model was performed. The main modification involves incorporating the investigated populations’ genetic structure into the model. Therefore, the new ESHIPPOsalmo model includes an analysis of biological parameters and the impact of multiple factors, including habitat alterations, invasive species, pollution, human population growth, and over-exploitation. In order to investigate individual levels of influence of the model’s analyzed parameters, a combination of supervised and unsupervised machine learning methods was used. The structure of the model is based on general and easily measurable indicators, which enables its application in any salmonid river in the world. By evaluating the parameters of the ESHIPPOsalmo model, we were able to establish that, of the analyzed populations from 46 localities, 37% have a moderate level of sustainability, 43% low, and 20% critically low.
Keywords: brown trout, conservation, genetic structure, habitat alterations, invasive species, sustainability
Published in DiRROS: 23.10.2024; Views: 199; Downloads: 458
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Abstract: Brain–computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI systems. We investigated the impact of time window duration on classification accuracy and false positive rate, employing Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) on data acquired from six post-stroke patients and on the external BCI IVa dataset. EEG signals were recorded and processed using the Common Spatial Patterns (CSP) algorithm for feature extraction. Our results indicate that longer time windows generally enhance classification accuracy and reduce false positives across all classifiers, with LDA performing the best. However, to maintain the real-time responsiveness, crucial for practical applications, a balance must be struck. The results suggest an optimal time window of 1–2 s, offering a trade-off between classification performance and excessive delay to guarantee the system responsiveness. These findings underscore the importance of temporal optimization in MI-BCI systems to improve usability in real rehabilitation scenarios.
Keywords: BCI, EEG, classification, motor imagery
Published in DiRROS: 23.10.2024; Views: 117; Downloads: 477
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Keywords: varstvo gozdov, morfološke analize
Published in DiRROS: 23.10.2024; Views: 129; Downloads: 0
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Keywords: varstvo gozdov, morfološke analize
Published in DiRROS: 23.10.2024; Views: 126; Downloads: 0
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Keywords: varstvo gozdov, morfološke analize, Pseudocercospora pini-densiflorae, rdeči bor, bolezen iglic
Published in DiRROS: 23.10.2024; Views: 144; Downloads: 0
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