An Exceptionally Stable and Widespread Hydrated Amorphous Calcium Carbonate Precipitated By the Dog Vomit Slime Mold Fuligo Septica (Myxogastria)

Biogenic amorphous calcium carbonate (ACC) is typically metastable and can rapidly transform through aging, dehydration, and/or heating to crystalline calcium carbonate. Gaining insight into its structure and properties is typically hampered by its tendency to crystallize over short time periods once isolated from the host organism, and also by the small quantities that are usually available for study. Here, we describe an exceptionally stable hydrated ACC (HACC) precipitated by the cosmopolitan slime mold, Fuligo septica (L.) F.H. Wigg. (1780). A single slime mold can precipitate up to one gram of HACC over the course of one night. Powder x-ray diffraction (XRD) patterns, transmission electron microscopy (TEM) images, infrared absorption (IR) spectra, and lack of optical birefringence are consistent with an amorphous material. XRD simulations supported by thermogravimetric (TG) and evolved gas analysis (EGA) data suggest an intimate association of organic matter with ~1-nm-sized ACC units that have monohydrocalcite- and calcite-like nano-structural properties. It is postulated that this association imparts the extreme stability of the HACC by preventing loss of H2O and subsequent crystallization. The composition, structure, and thermal behavior of the HACC precipitated by F. septica collected over 8000 km apart, and in markedly different environments, suggests a common structure, as well as similar biochemical and biomineralization mechanisms for the HACC formation.

New mid-Cretaceous cryptic slime mold beetles and the early evolution of Sphindidae (Coleoptera: Cucujoidea)

The cryptic slime mold beetles, Sphindidae, are a moderately diverse cucujoid beetle family, whose members are obligately tied to slime molds throughout their life. The fossil record of sphindid beetles is sparse; stem-sphindids and crown-group members of uncertain systematic placement have been reported from Cretaceous ambers. Here we review the Mesozoic fossil record of Sphindidae and report a new sphindid genus and species, Trematosphindus newtonigen. et sp. nov., from Albian/Cenomanian amber from northern Myanmar (ca. 99 Ma). Trematosphindus is set apart from all other sphindids by the presence of distinct lateral cavities on the anterior pronotal angles. Our phylogenetic analysis identifies Trematosphindus as an early-diverging genus within Sphindidae, sister to the remainder of the family except Protosphindus, or Protosphindus and Odontosphindus. The new fossils provide evidence that basal crown slime mold beetles begun to diversify by the mid-Cretaceous, providing a valuable calibration point for understanding timescale of sphindid co-evolution with slime molds.

Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization

In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solution. The optimization performance of the proposed DESMAOA was analyzed by using 23 classical benchmark functions. Firstly, the impacts of different components are discussed. Then, the exploitation and exploration capabilities, convergence behaviors, and performances are evaluated in detail. Cases at different dimensions also were investigated. Compared with the SMA, AOA, and another five well-known optimization algorithms, the results showed that the proposed method can outperform other optimization algorithms with high superiority. Finally, three classical engineering design problems were employed to illustrate the capability of the proposed algorithm for solving the practical problems. The results also indicate that the DESMAOA has very promising performance when solving these problems.

An Entropic Gradient Structure in the Network Dynamics of a Slime Mold

The approach to equilibrium in certain dynamical systems can be usefully described in terms of information-theoretic functionals. Well-studied models of this kind are Markov processes, chemical reaction networks, and replicator dynamics, for all of which it can be proven, under suitable assumptions, that the relative entropy (informational divergence) of the state of the system with respect to an equilibrium is nonincreasing over time. This work reviews another recent result of this type, which emerged in the study of the network optimization dynamics of an acellular slime mold, Physarum polycephalum. In this setting, not only the relative entropy of the state is nonincreasing, but its evolution over time is crucial to the stability of the entire system, and the equilibrium towards which the dynamics is attracted proves to be a global minimizer of the cost of the network.