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The purpose of this paper is to reveal the benefits of organized voluntary emergency services (OVES) in the case of flood events, since such information is mostly not available, but needed to analyze the total effects of disasters and respective responses. Moreover, the efficient allocation of scarce public resources for emergency and risk management should be based on empirical data.

Based on a qualitative framework describing the benefits of OVES, the authors develop different tools for monetizing tangible as well as intangible benefits and apply them for case studies in Austria.

The benefits of volunteer efforts for emergency management cannot be monetized easily, since they are often of intangible character. Nevertheless, we show that the benefits of OVES could be substantial.

As the authors analyze case studies, the results cannot be directly transferred to other regions, but illustrate the empirical dimension of the benefits of OVES. Further research should be undertaken to assess the benefits of avoided losses by OVES using single-object data.

Since many emergency service institutions are involved during/after natural hazards, data availability and exchange should be improved. Objective decisions for investment in emergency services should be based on data of recent hazard events and case studies.

The paper develops a toolbox to evaluate the benefits of OVES and is thus highly valuable for emergency managers, which are responsible for deploying volunteers and non-volunteers in emergency management.

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.