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Tropical Lake Sentani in the Indonesian Province Papua consists of four separate basins and is surrounded by a catchment with a very diverse geology. We characterized the surface sediment (upper 5 cm) of the lake's four sub-basins based on multivariate statistical analyses (principal component analysis, hierarchical clustering) of major element compositions obtained by X-ray fluorescence scanning. Three types of sediment are identified based on distinct compositional differences between rivers, shallow/proximal and deep/distal lake sediments. The different sediment types are mainly characterized by the correlation of elements associated with redox processes (S, Mn, Fe), carbonates (Ca), and detrital input (Ti, Al, Si, K) derived by river discharge. The relatively coarse-grained river sediments mainly derive form the mafic catchment geology and contribution of the limestone catchment geology is only limited. Correlation of redox sensitive and detrital elements are used to reveal oxidation conditions, and indicate oxic conditions in river samples and reducing conditions for lake sediments. Organic carbon (TOC) generally correlates with redox sensitive elements, although a correlation between TOC and individual elements change strongly between the three sediment types. Pyrite is the quantitatively dominant reduced sulfur mineral, monosulfides only reach appreciable concentrations in samples from rivers draining mafic and ultramafic catchments. Our study shows large spatial heterogeneity within the lake's sub-basins that is mainly caused by catchment geology and topography, river runoff as well as the bathymetry and the depth of the oxycline. We show that knowledge about lateral heterogeneity is crucial for understanding the geochemical and sedimentological variations recorded by these sediments. The highly variable conditions make Lake Sentani a natural laboratory, with its different sub-basins representing different depositional environments under identical tropical climate conditions.
Estimation-of-distribution algorithms (EDAs) are randomized search heuristics that create a probabilistic model of the solution space, which is updated iteratively, based on the quality of the solutions sampled according to the model. As previous works show, this iteration-based perspective can lead to erratic updates of the model, in particular, to bit-frequencies approaching a random boundary value. In order to overcome this problem, we propose a new EDA based on the classic compact genetic algorithm (cGA) that takes into account a longer history of samples and updates its model only with respect to information which it classifies as statistically significant. We prove that this significance-based cGA (sig-cGA) optimizes the commonly regarded benchmark functions OneMax (OM), LeadingOnes, and BinVal all in quasilinear time, a result shown for no other EDA or evolutionary algorithm so far. For the recently proposed stable compact genetic algorithm-an EDA that tries to prevent erratic model updates by imposing a bias to the uniformly distributed model-we prove that it optimizes OM only in a time exponential in its hypothetical population size. Similarly, we show that the convex search algorithm cannot optimize OM in polynomial time.
Understanding the recombination dynamics of organic and perovskite solar cells has been a crucial prerequisite in the steadily increasing performance of these promising new types of photovoltaics. Surface recombination in particular has turned out to be one of the last remaining roadblocks, which specifically reduces the open circuit voltage. In this study, the relationship between the rate of surface recombination and the density of charge carriers is analyzed, revealing a cubic dependence between these two parameters. This hypothesis is then tested and verified with the recombination dynamics of an organic solar cell known to exhibit significant surface recombination and a high energy proton irradiated CH3NH3PbI3 pemvskite solar cell during white light illumination. Incidentally, these results can also explain recombination orders exceeding the commonly known threshold for bimolecular recombination that have been observed in some studies without the need for a charge carrier dependent bimolecular recombination coefficient.
Based on an analysis of continuous monitoring of farm animal behavior in the region of the 2016 M6.6 Norcia earthquake in Italy, Wikelski et al., 2020; (Seismol Res Lett, 89, 2020, 1238) conclude that animal activity can be anticipated with subsequent seismic activity and that this finding might help to design a "short-term earthquake forecasting method." We show that this result is based on an incomplete analysis and misleading interpretations. Applying state-of-the-art methods of statistics, we demonstrate that the proposed anticipatory patterns cannot be distinguished from random patterns, and consequently, the observed anomalies in animal activity do not have any forecasting power.