Life cycle assessment of biogas production in small-scale household digesters in Vietnam

Small-scale household digesters have been promoted across Asia as a sustainable way of handling manure. The major advantages are that they produce biogas and reduce odor. However their disadvantages include the low recycling of nutrients, because digestate is dilute and therefore difficult to transport, and the loss of biogas as a result of cracks and the intentional release of excess biogas. In this study, life cycle assessment (LCA) methodology was used to assess the environmental impacts associated with biogas digesters in Vietnam. Handling 1,000 kg of liquid manure and 100 kg of solid manure in a system with a biogas digester reduced the impact potential from 4.4 kg carbon dioxide (CO2) equivalents to 3.2 kg CO2 equivalents compared with traditional manure management. However, this advantage could easily be compromised if digester construction is considered in the LCA or in situations where there is an excess of biogas which is intentionally released. A sensitivity analysis showed that biogas digesters could be a means of reducing global warming if methane emissions can be kept low. In terms of eutrophication, farms with biogas digesters had 3 to 4 times greater impacts. In order to make biogas digesters sustainable, methods for recycling digestates are urgently required

Stable Propagation of a Burst Through a One-Dimensional Homogeneous Excitatory Chain Model of Songbird Nucleus HVC

We demonstrate numerically that a brief burst consisting of two to six spikes can propagate in a stable manner through a one-dimensional homogeneous feedforward chain of non-bursting neurons with excitatory synaptic connections. Our results are obtained for two kinds of neuronal models, leaky integrate-and-fire (LIF) neurons and Hodgkin-Huxley (HH) neurons with five conductances. Over a range of parameters such as the maximum synaptic conductance, both kinds of chains are found to have multiple attractors of propagating bursts, with each attractor being distinguished by the number of spikes and total duration of the propagating burst. These results make plausible the hypothesis that sparse precisely-timed sequential bursts observed in projection neurons of nucleus HVC of a singing zebra finch are intrinsic and causally related.Comment: 13 pages, 6 figure

Relative Periodic Solutions of the Complex Ginzburg-Landau Equation

A method of finding relative periodic orbits for differential equations with continuous symmetries is described and its utility demonstrated by computing relative periodic solutions for the one-dimensional complex Ginzburg-Landau equation (CGLE) with periodic boundary conditions. A relative periodic solution is a solution that is periodic in time, up to a transformation by an element of the equation's symmetry group. With the method used, relative periodic solutions are represented by a space-time Fourier series modified to include the symmetry group element and are sought as solutions to a system of nonlinear algebraic equations for the Fourier coefficients, group element, and time period. The 77 relative periodic solutions found for the CGLE exhibit a wide variety of temporal dynamics, with the sum of their positive Lyapunov exponents varying from 5.19 to 60.35 and their unstable dimensions from 3 to 8. Preliminary work indicates that weighted averages over the collection of relative periodic solutions accurately approximate the value of several functionals on typical trajectories.Comment: 32 pages, 12 figure

NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee

Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different input samples, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model's mechanisms in an unsupervised manner. Our experimental results show that neurons identified by NeuCEPT not only have strong influence on the model's predictions but also hold meaningful information about model's mechanisms.Comment: 6 main page