Synergistic interaction of biocatalyst with bio-anode as a function of electrode materials

Comprehensive study was performed to understand the synergistic interaction between the biocatalyst and anode in terms of electron discharge (ED) pattern and microbial growth by varying electrode (bio-anode) materials viz., graphite, aluminum, brass, copper, nickel and stainless steel. Experiments were performed in bio-electrochemical cell consisting of three electrodes (bio-anode as working electrode, carbon rod as counter electrode and Ag/AgCl(S) as reference electrode) employing anaerobic mixed culture as anodic biocatalyst. Voltammetric and chronoamperometric analysis were used to enumerate the ED and redox reactions. Presence of higher microbial population and dominance of Gram positive bacteria with higher ED supported graphite function as a good bio-anode material. Nickel and stainless steel showed higher ED after graphite associated with dominance of Gram positive bacterial population. Although higher ED was noticed with brass, metal oxidation and decrement in ED with time doesn’t support its function as bio-anode. In spite of higher ED than nickel and stainless steel, aluminum and copper showed significant metal oxidation leading to change in both physical and electrochemical properties along with dominant growth of Gram negative bacteria. This study gives a comprehensive idea on biocatalyst interaction with anode in extracellular electron transfer which is important in improving the anode performance. Juxtaposing the results, it can be deduced that the outcome of the present study can be extended to all bio-electrochemical systems including microbial fuel cell (MFC).     

Thermal management issues in a PEMFC stack – A brief review of current status

Understanding the thermal effects is critical in optimizing the performance and durability of proton exchange membrane fuel cells (PEMFCs). A PEMFC produces a similar amount of waste heat to its electric power output and tolerates only a small deviation in temperature from its design point. The balance between the heat production and its removal determines the operating temperature of a PEMFC. These stringent thermal requirements present a significant heat transfer challenge. In this work, the fundamental heat transfer mechanisms at PEMFC component level (including polymer electrolyte, catalyst layers, gas diffusion media and bipolar plates) are briefly reviewed. The current status of PEMFC cooling technology is also reviewed and research needs are identified.     

Systemic inflammatory load in humans is suppressed by consumption of two formulations of dried,encapsulated juice concentrate

Chronic inflammation contributes to an increased risk for developing chronic conditions such as cardiovascular disease, diabetes, and cancer. A high “inflammatory load” is defined as elevated inflammation markers in blood or other tissues. We evaluated several markers of systemic inflammation from healthy adults and tested the hypothesis that two formulations of encapsulated fruit and vegetable juice powder concentrate with added berry powders (FVB) or without (FV) could impact markers of inflammatory load. Using a double‐blind, placebo‐controlled approach, 117 subjects were randomly assigned to receive placebo, FV, or FVB capsules. Blood was drawn at baseline and after 60 d of capsule consumption. We measured inflammatory markers (high sensitivity C‐Reactive Protein, Monocyte Chemotactic Protein‐1, Macrophage Inflammatory Protein 1‐β, and Regulated upon Activation, Normal T cell Expressed and Secreted), superoxide dismutase, and micronutrients (β‐carotene, vitamin C, and vitamin E). Results showed Monocyte Chemotactic Protein‐1, Macrophage Inflammatory Protein 1‐β, and RANTES levels were significantly reduced and superoxide dismutase and micronutrient levels were significantly increased in subjects consuming both FV and FVB, relative to placebo. Data suggest a potential health benefit by consuming either formulation of the encapsulated juice concentrates through their anti‐inflammatory properties.     

Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.     

A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects

BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use.     

Microstructure and residual stress distribution of similar and dissimilar electron beam welds – Maraging steel to medium alloy medium carbon steel

The influence of parent metal heat treatment condition on the residual stress distribution in dissimilar metal welds of maraging steel to quenched and tempered medium alloy medium carbon steel has been investigated. It has been observed that the residual stress distribution would be more compressive if the maraging steel is in soft condition. This is attributed to stress absorbing nature of highly yielding soft maraging steel.     

Response of Materials During Sliding on Various Surface Textures

In the present investigation, soft materials, such as Al-4Mg alloy, high-purity Al and pure Mg pins were slid against hard steel plates of various surface textures to study the response of materials during sliding. The experiments were conducted using an inclined pin-on-plate sliding apparatus under both dry and lubricated conditions in an ambient environment. Two kinds of frictional response, namely steady-state and stick-slip, were observed during sliding. In general, the response was dependent on material pair, normal load, lubrication, and surface texture of the harder material. More specifically, for the case of Al-4Mg alloy, the stick-slip response was absent under both dry and lubricated conditions. For Al, stick-slip was observed only under lubricated conditions. For the case of Mg, the stick-slip response was seen under both dry and lubricated conditions. Further, it was observed that the amplitude of stick-slip motion primarily depends on the plowing component of friction. The plowing component of friction was the highest for the surfaces that promoted plane strain conditions and was the lowest for the surfaces that promoted plane stress conditions near the surface.     

Effects of ventilation rate per person and per floor area on perceived air quality,sick building syndrome symptoms,and decision‐making

Ventilation rates (VRs) in buildings must adequately control indoor levels of pollutants; however, VRs are constrained by the energy costs. Experiments in a simulated office assessed the effects of VR per occupant on perceived air quality (PAQ), Sick Building Syndrome (SBS) symptoms, and decision‐making performance. A parallel set of experiments assessed the effects of VR per unit floor area on the same outcomes. Sixteen blinded healthy young adult subjects participated in each study. Each exposure lasted four hours and each subject experienced two conditions in a within‐subject study design. The order of presentation of test conditions, day of testing, and gender were balanced. Temperature, relative humidity, VRs, and concentrations of pollutants were monitored. Online surveys assessed PAQ and SBS symptoms and a validated computer‐based tool measured decision‐making performance. Neither changing the VR per person nor changing the VR per floor area, had consistent statistically significant effects on PAQ or SBS symptoms. However, reductions in either occupant‐based VR or floor‐area‐based VR had a significant and independent negative impact on most decision‐making measures. These results indicate that the changes in VR employed in the study influence performance of healthy young adults even when PAQ and SBS symptoms are unaffected.     

Artificial neural networks for recognition of 3-d partial dischargepatterns

Partial discharge (PD) measurements have been carried out over the years to assess insulation systems in power apparatus for their integrity and design deficiencies. Digital PD recording, processing and its presentation as 3-d patterns are recent trends in both industry and testing laboratories. Interpretation of these patterns can lead to evaluation of the cause of PD. A need arises to look for methods in the domain of pattern recognition for automating this process. In this context, this paper presents results to demonstrate the possibility of using pattern recognition capabilities offered by a multilayer neural network to recognize 3-d PD patterns     

The use of fractal features for recognition of 3-D dischargepatterns

This work presents further results on the use of fractal features for recognition of 3-D partial discharge patterns. Two fractal features, the fractal dimension and lacunarity were calculated from 3-D discharge patterns and their power to discriminate among various discharge patterns was analyzed. The results indicate that fractal features possess fairly reasonable discriminating abilities