Study on the Effect of Polysulfide Content on the Micromorphology and Spontaneous Combustion Characteristics of Coal

This paper aimed to study the effect of the polysulfide content on the micromorphology and spontaneous combustion characteristics of coal, in order to develop more targeted prevention and treatment strategies. To this end, this study selected the method of mixing different sulfides with very low sulfur content raw coal to prepare the coal samples to be tested. Various parameters, such as true density, porosity, micromorphology, and oxygen uptake of the different sulfur samples, were tested. The results reveal that sulfide had a certain expansion effect on the coal body and improved the pore structure of coal, and the porosity increased with the increase of the sulfur content. After adding iron (II) disulfide (FeS2) and iron (II) sulfide (FeS) powder to the original coal sample, the number of fine particles on the surface increased significantly. After increasing the oxidation temperature, the lamellar structure disintegrated, and the massive coal body was broken into several fine particles, which promoted the spontaneous combustion of coal. Polysulfide promotes the low-temperature oxygen absorption of coal and shortens the natural firing period of coal. FeS has a slightly greater effect on increasing the tendency of coal to spontaneously combust and shortening the shortest natural firing period of coal. Before the addition of FeS2 and FeS to the coal samples, the coal production amount was not much different below 80–90°C, and then, the gap gradually widened. Under the same temperature condition of coal, carbon monoxide (CO) production basically occurred first as the sulfur content increased. When FeS2 and FeS were added, the sulfur content of the coal samples was 3 and 4%, respectively, and the production of CO and ethene (C2H4) was the largest. Although the peak areas of aliphatic hydrocarbon, aromatic hydrocarbon, hydroxyl group, and carbonyl group in the coal samples with FeS were different, they all reached their maximum value when the sulfur content was 4%.

Phase noise-induced double coherence resonances in a neuronal model

Phase noise-induced single coherence resonance (CR) has been reported in previous studies. It is reported here that double CRs can be induced in the FitzHugh–Nagumo (FHN) model by phase noise when the oscillation period of phase noise is much larger than the firing period of the FHN model. By analyzing peaks in the power spectrums for the fast voltage variable and the coefficient variations (CVs) of interspike interval (ISI) series, we find that double CRs corresponding to the frequency of phase noise and the firing frequency of the FHN model respectively appear at small and large noise intensities. This implies that there are double chances for the FHN model to take advantage of the benefits of phase noise. Possible causes of the single CR are also discussed.

All Phase Resetting Curves Are Bimodal, but Some Are More Bimodal Than Others

Phase resetting curves (PRCs) are phenomenological and quantitative tools that tabulate the transient changes in the firing period of endogenous neural oscillators as a result of external stimuli, for example, presynaptic inputs. A brief current perturbation can produce either a delay (positive phase resetting) or an advance (negative phase resetting) of the subsequent spike, depending on the timing of the stimulus. We showed that any planar neural oscillator has two remarkable points, which we called neutral points, where brief current perturbations produce no phase resetting and where the PRC flips its sign. Since there are only two neutral points, all PRCs of planar neural oscillators are bimodal. The degree of bimodality of a PRC, that is, the ratio between the amplitudes of the delay and advance lobes of a PRC, can be smoothly adjusted when the bifurcation scenario leading to stable oscillatory behavior combines a saddle node of invariant circle (SNIC) and an Andronov-Hopf bifurcation (HB).

Period Focusing Induced by Network Feedback in Populations of Noisy Integrate-and-Fire Neurons

The population dynamics of an ensemble of nonleaky integrate-and-fire stochastic neurons is studied. The model selected allows for a detailed analysis of situations where noise plays a dominant role. Simulations in a regime with weak to moderate interactions show that a mechanism of excitatory message interchange among the neurons leads to a decrease in the firing period dispersion of the individual units. The dispersion reduction observed is larger than what would be expected from the decrease in the period. This ‘period focusing’ is explained using a mean-field model. It is a dynamical effect that arises from the progressive decrease of the effective firing threshold as a result of the messages received by each unit from the rest of the population. A back-of-the-envelope formula to calculate this nontrivial dispersion reduction and a simple geometrical description of the effect are also provided.