Robustness of Physics-Informed Neural Networks to Noise in Sensor Data

Physics-Informed Neural Networks (PINNs) have been shown to be an effective way of incorporating physics-based domain knowledge into neural network models for many important real-world systems. They have been particularly effective as a means of inferring system information based on data, even in cases where data is scarce. Most of the current work however assumes the availability of high-quality data. In this work, we further conduct a preliminary investigation of the robustness of physics-informed neural networks to the magnitude of noise in the data. Interestingly, our experiments reveal that the inclusion of physics in the neural network is sufficient to negate the impact of noise in data originating from hypothetical low quality sensors with high signal-to-noise ratios of up to 1. The resultant predictions for this test case are seen to still match the predictive value obtained for equivalent data obtained from high-quality sensors with potentially 10x less noise. This further implies the utility of physics-informed neural network modeling for making sense of data from sensor networks in the future, especially with the advent of Industry 4.0 and the increasing trend towards ubiquitous deployment of low-cost sensors which are typically noisier

Design of Turing Systems with Physics-Informed Neural Networks

Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of constituent diffusion and reaction. Critically, these parameters, such as diffusion coefficient, heavily influence the mode and type of the final pattern, and quantitative characterization and knowledge of these parameters can aid in bio-mimetic design or understanding of real-world systems. However, the use of numerical methods to infer these parameters can be difficult and computationally expensive. Typically, adjoint solvers may be used, but they are frequently unstable for very non-linear systems. Alternatively, massive amounts of iterative forward simulations are used to find the best match, but this is extremely effortful. Recently, physics-informed neural networks have been proposed as a means for data-driven discovery of partial differential equations, and have seen success in various applications. Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction-diffusion systems in the steady-state for scientific discovery or design. Our proof-of-concept results show that the method is able to infer parameters for different pattern modes and types with errors of less than 10\%. In addition, the stochastic nature of this method can be exploited to provide multiple parameter alternatives to the desired pattern, highlighting the versatility of this method for bio-mimetic design. This work thus demonstrates the utility of physics-informed neural networks for inverse parameter inference of reaction-diffusion systems to enhance scientific discovery and design

Caffeic Acid Phenylethyl Amide Protects against the Metabolic Consequences in Diabetes Mellitus Induced by Diet and Streptozocin

Caffeic acid phenyl ester is distributed wildly in nature and has antidiabetic and cardiovascular protective effects. However, rapid decomposition by esterase leads to its low bioavailability in vivo. In this study, chronic metabolic and cardiovascular effects of oral caffeic acid phenylethyl amide, whose structure is similar to caffeic acid phenyl ester and resveratrol, were investigated in ICR mice. We found that caffeic acid phenylethyl amide protected against diet or streptozocin-induced metabolic changes increased coronary flow and decreased infarct size after global ischemia-reperfusion in Langendorff perfused heart. Further study indicated that at least two pathways might be involved in such beneficial effects: the induction of the antioxidant protein MnSOD and the decrease of the proinflammatory cytokine TNFα and NFκB in the liver. However, the detailed mechanisms of caffeic acid phenylethyl amide need further studies. In summary, this study demonstrated the protective potential of chronic treatment of caffeic acid phenylethyl amide against the metabolic consequences in diabetes mellitus

Demographic and Clinical Characteristics of Patients with Severe Asthma in the Asian Pacific Region : data from the International Severe Asthma Registry (ISAR)

Peer reviewe

Generation of Reactive Oxygen Species by Polyenylpyrroles Derivatives Causes DNA Damage Leading to G2/M Arrest and Apoptosis in Human Oral Squamous Cell Carcinoma Cells

10.1371/journal.pone.0067603PLoS ONE86-POLN

Fermentative hydrogen production from starch using natural mixed cultures

Batch and continuous tests were conducted to evaluate fermentative hydrogen production from starch (at a concentration of chemical oxygen demand (COD) 20 g/L) at 35 °C by a natural mixed culture of paper mill wastewater treatment sludge. The optimal initial cultivation pH (tested range 5-7) and substrate concentration (tested range 5-60-gCOD/L) were evaluated by batch reactors while the effects of hydraulic retention time (HRT) on hydrogen production, as expressed by hydrogen yield (HY) and hydrogen production rate (HPR), were evaluated by continuous tests. The experimental results indicate that the initial cultivation pH markedly affected HY, maximum HPR, liquid fermentation product concentration and distribution, butyrate/acetate concentration ratio and metabolic pathway. The optimal initial cultivation pH was 5.5 with peak values of HY 1.1 mol-H2/mol-hexose maximum HPR 10.4 mmol-H2/L/h and butyrate concentration 7700 mg-COD/L. In continuous hydrogen fermentation, the optimal HRT was 4 h with peak HY of 1.5 mol-H2/mol-hexose, peak HPR of 450 mmol-H2/L/d and lowest butyrate concentration of 3000 mg-COD/L. The HPR obtained was 280% higher than reported values. A shift in dominant hydrogen-producing microbial population along with HRT variation was observed with Clostridium butyricum, C. pasteurianum, Klebshilla pneumoniae, Streptococcus sp., and Pseudomonas sp. being present at efficient hydrogen production at the HRTs of 4-6 h. Strategies based on the experimental results for optimal hydrogen production from starch are proposed

Temperature effects on fermentative hydrogen production from xylose using mixed anaerobic cultures

Sewage sludge microflora were anaerobically cultivated in a chemostat-type anaerobic bioreactor at temperatures of 30-55 ◦C, a pH of 7.1 and a hydraulic retention time of 12 h to determine the hydrogen production efficiency from xylose (20 g-COD/L). It was demonstrated that hydrogen production of the enriched sewage sludge microflora (dominated by Clostridia species) was temperature-dependent in hydrogen gas content, hydrogen yield, hydrogen production rate (HPR) and specific HPR with values of 25.1-42.2% (v/v), 0.4-1.4 mol-H2/mol-xylose, 0.06-0.24 mol-H2/L-day and 0.02-0.10 mol-H2/g-VSS-day, respectively, and the above values peaked when being operated at 50 ◦C. A transition temperature of 45 ◦C existed by having a lowest hydrogen production efficiency. Butyrate and ethanol were the major soluble metabolite products for thermophilic and mesophilic fermentation, respectively. The liquid metabolite concentration fractions and microbial community analysis indicate that the differences in hydrogen production efficiency between each tested temperature might relate to the shifts in metabolic pathway or microbial community. Thermodynamic analysis using HPR values and Arrhenius equation showed that the activation energy was 74.7 kJ/mol. Strategies based on temperature control for optimal hydrogen production from xylose using natural mixed cultures are proposed

Quantitative analysis of microorganism composition in a pilot-scale fermentative biohydrogen production system

Five specific real-time polymerase chain reaction primers targeting the 16S rRNA gene of Clostridium spp., Klebsiella spp., Streptococcus spp., Pseudomonas spp., and Bifidobacterium spp., and two primer sets targeting the hydrogenase genes of hydrogen-producing Clostridium pasteurianum and Clostridium butyricum were designed and tested in the present study to quantify the microorganisms in fermentative biohydrogen production systems. The former primers revealed the composition of all coexisting microorganisms, whereas the latter ones provided information on which clostridia were responsible for the biohydrogen production in various operational conditions. When sucrose was selected as the feeding substrate, the biogas production and hydrogen production rate (HPR) of the system increased as the percentage of Clostridium spp. (especially C. pasteurianum) increased. The cell count of C. pasteurianum increased up to 90% of the total cell population when the system approached its maximum hydrogen production. C. butyricum was identified as the main hydrogen-producing clostridium in the condensed molasses soluble wastewater feeding system, but there was no significant correlation between system HPR and C. butyricum cell count. At the same time, other microorganisms, such as Bifidobacterium spp. and Klebsiella spp., were the predominant ones throughout the whole operation and possibly caused the unsatisfied biohydrogen production. The composition of microorganisms is the principal factor affecting biohydrogen production. Aside from the well-known hydrogen-producing Clostridium spp., several other microorganisms not only coexist but can also significantly affect system performance. The monitoring method established in the present study provides a fast quantification procedure to help operators understand how the system works and therefore quickly respond in operations