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End date: 2024 × Start date: 2020 × DDC: 620 × DDC: 690 × WGL Institute: PIK ×

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Now showing 1 - 3 of 3
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    Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment
    (Basel : MDPI AG, 2022) Haselhoff, Timo; Braun, Tobias; Hornberg, Jonas; Lawrence, Bryce T.; Ahmed, Salman; Gruehn, Dietwald; Moebus, Susanne
    As sustainable metropolitan regions require more densely built-up areas, a comprehensive understanding of the urban acoustic environment (AE) is needed. However, comprehensive datasets of the urban AE and well-established research methods for the AE are scarce. Datasets of audio recordings tend to be large and require a lot of storage space as well as computationally expensive analyses. Thus, knowledge about the long-term urban AE is limited. In recent years, however, these limitations have been steadily overcome, allowing a more comprehensive analysis of the urban AE. In this respect, the objective of this work is to contribute to a better understanding of the time-frequency domain of the urban AE, analysing automatic audio recordings from nine urban settings over ten months. We compute median power spectra as well as normalised spectrograms for all settings. Additionally, we demonstrate the use of frequency correlation matrices (FCMs) as a novel approach to access large audio datasets. Our results show site-dependent patterns in frequency dynamics. Normalised spectrograms reveal that frequency bins with low power hold relevant information and that the AE changes considerably over a year. We demonstrate that this information can be captured by using FCMs, which also unravel communities of interlinked frequency dynamics for all settings.
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    Awareness, Experience, and Knowledge of Farming Households in Rural Bangladesh Regarding Mold Contamination of Food Crops: A Cross-Sectional Study
    (Basel : MDPI AG, 2021) Kyei, Nicholas N. A.; Waid, Jillian L; Ali, Nurshad; Gabrysch, Sabine
    Aside from specific environmental conditions, poor agricultural practices contribute to mold and thus the mycotoxin contamination of crops. This study investigated Bangladeshi farming households’ (i) awareness of and experience with mold contamination of food crops; (ii) knowledge and awareness of the timing, causes, and consequences of mold and mycotoxin contamination; and (iii) knowledge of the recommended agricultural practices for controlling and preventing mold contamination of food crops. A survey was conducted with 1280 households in rural areas of Habiganj district, Bangladesh. Basic descriptive statistics were calculated, and mixed-effects linear regression analyses were performed to examine associations between household characteristics and overall knowledge scores. The awareness of mold contamination of food crops was very high (99%; 95% CI: 98–100%) and a shared experience among households (85%; 95% CI: 80–88%). Yet, the majority (80%; 95% CI: 76–84%) demonstrated a low level of knowledge of the timing, causes, and preventive practices regarding mold contamination of crops. Knowledge scores were similar over demographic groups and better for households with more arable land. The findings suggest a generally insufficient knowledge of the conditions that favor mold contamination and the measures for preventing mold contamination of food crops. These findings underline the need for tailored interventions to promote good agricultural practices and reduce mold contamination of food crops.
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    Correlated power time series of individual wind turbines: A data driven model approach
    (Woodbury, NY : American Inst. of Physics, 2020) Braun, Tobias; Waechter, Matthias; Peinke, Joachim; Guhr, Thomas
    Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with crossover behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model of power time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function. © 2020 Author(s).