An Unconditionally Stable Iterative Decoupled Algorithm for Multiple-Network Poroelasticity

In this work, we introduce an iterative decoupled algorithm designed for addressing the quasi-static multiple-network poroelasticity problem. This problem pertains to the simultaneous modeling of fluid flow and deformations within an elastic porous medium permeated by multiple fluid networks, each with distinct characteristics. Our approach focuses on the total-pressure-based formulation, which treats the solid displacement, total pressure, and network pressures as primary unknowns. This formulation transforms the original problem into a combination of the generalized Stokes problem and the parabolic problem, offering certain advantages such as mitigating elastic locking effects and streamlining the discretization process. Notably, the algorithm ensures unconditional convergence to the solution of the total-pressure-based coupled algorithm. To validate the accuracy and efficiency of our method, we present numerical experiments. The robustness of the algorithm with respect to the physical parameters and the discretization parameters is carefully investigated.Comment: to be submitte

Large-Scale Simulation of Neural Networks with Biophysically Accurate Models on Graphics Processors

Efficient simulation of large-scale mammalian brain models provides a crucial computational means for understanding complex brain functions and neuronal dynamics. However, such tasks are hindered by significant computational complexities. In this work, we attempt to address the significant computational challenge in simulating large-scale neural networks based on the most biophysically accurate Hodgkin-Huxley (HH) neuron models. Unlike simpler phenomenological spiking models, the use of HH models allows one to directly associate the observed network dynamics with the underlying biological and physiological causes, but at a significantly higher computational cost. We exploit recent commodity massively parallel graphics processors (GPUs) to alleviate the significant computational cost in HH model based neural network simulation. We develop look-up table based HH model evaluation and efficient parallel implementation strategies geared towards higher arithmetic intensity and minimum thread divergence. Furthermore, we adopt and develop advanced multi-level numerical integration techniques well suited for intricate dynamical and stability characteristics of HH models. On a commodity CPU card with 240 streaming processors, for a neural network with one million neurons and 200 million synaptic connections, the presented GPU neural network simulator is about 600X faster than a basic serial CPU based simulator, 28X faster than the CPU implementation of the proposed techniques, and only two to three times slower than the GPU based simulation using simpler spiking models

Biofilm growth kinetics and nutrient (N/P) adsorption in an urban lake using reclaimed water: A quantitative baseline for ecological health assessment

Reclaimed wastewater reuse represents an effective method for partial resolution of increasing urban water shortages; however, reclaimed water may be characterized by significant contaminant loading, potentially affecting receiving ecosystem (and potentially human) health. The current study examined biofilm growth and nutrient adsorption in Olympic Lake (Beijing), the largest artificial urban lake in the world supplied exclusively by reclaimed wastewater. Findings indicate that solid particulate, extracellular polymeric substance (EPS) and metal oxide (Al, Fe, Mn) constituent masses adhere to a bacterial growth curve during biofilm formation and growth. Peak values were observed after ≈30 days, arrived at dynamic stability after ≈50days and were affected by growth matrix surface roughness. These findings may be used to inform biofilm cultivation times for future biomonitoring. Increased growth matrix surface roughness (10.0μm) was associated with more rapid biofilm growth and therefore an increased sensitivity to ecological variation in reclaimed water. Reclaimed water was found to significantly inhibit biofilm nutrient adsorption when compared with a “natural water” background, with elevated levels of metal oxides (Al, Fe, and Mn) and EPS representing the key substances actively influencing biofilm nutrient adsorption in reclaimed water. Results from the current study may be used to provide a quantitative baseline for future studies seeking to assess ecosystem health via monitoring of biofilms in the presence of reclaimed water through an improved quantitative understanding of biofilm kinetics in these conditions

Biofilm microbial community structure in an urban lake utilizing reclaimed water

Analyses of biofilm community structure may potentially be employed for aquatic ecosystem health assessment, however, to date, biofilm diversity within urban lakes using reclaimed water has not been examined. Accordingly, the microbial community diversity and structure of biofilms from the surface of multiple matrices with varying roughness (0.1, 1.0 and 10.0 μm) were characterized using a suite of molecular techniques including scanning electron microscopy, genetic fingerprinting and phospholipid-derived fatty acid analyses. Samples were largely comprised of inorganic particles, algae and numerous bacterial species; 12 phospholipid-derived fatty acid (PLFA) types were identified, significantly less than typically associated with sewage. Both growth matrix surface roughness and biofilm growth phase were shown to concur with significantly different microbial quantity and community structures. Gram-negative bacteria bacillus i15:03OH and 18:0 were the dominant bacterial genera, collectively comprising ≈75 % of identified PLFA species content. Calculated species diversity (H) and species dominance (D) exhibited identical correlational patterns with measured water quality parameters; significant positive correlations were exhibited with respect to Mg2, while significant negative correlations were found for NO3, TP, BOD, COD, SP, PO4, SO4 and pH. Results indicate that analyses of biofilm formation and structure could be effectively used to undertake integrated assessments of the ecological health of lake systems using reclaimed water. Further work is required to elucidate the optimum conditions for sample collection and analytical interpretation

Poly(benzimidazobenzophenanthroline)-Ladder-Type Two-Dimensional Conjugated Covalent Organic Framework for Fast Proton Storage

Electrochemical proton storage plays an essential role in designing next-generation high-rate energy storage devices, e.g., aqueous batteries. Two-dimensional conjugated covalent organic frameworks (2D c-COFs) are promising electrode materials, but their competitive proton and metal-ion insertion mechanisms remain elusive, and proton storage in COFs is rarely explored. Here, we report a perinone-based poly(benzimidazobenzophenanthroline) (BBL)-ladder-type 2D c-COF for fast proton storage in both a mild aqueous Zn-ion electrolyte and strong acid. We unveil that the discharged C−O− groups exhibit largely reduced basicity due to the considerable π-delocalization in perinone, thus affording the 2D c-COF a unique affinity for protons with fast kinetics. As a consequence, the 2D c-COF electrode presents an outstanding rate capability of up to 200 A g−1 (over 2500 C), surpassing the state-of-the-art conjugated polymers, COFs, and metal–organic frameworks. Our work reports the first example of pure proton storage among COFs and highlights the great potential of BBL-ladder-type 2D conjugated polymers in future energy devices