A spiking half-cognitive model for classification

This paper describes a spiking neural network that learns classes. Following a classic Psychological task, the model learns some types of classes better than other types, so the net is a spiking cognitive model of classification. A simulated neural system, derived from an existing model, learns natural kinds, but is unable to form sufficient attractor states for all of the types of classes. An extension of the model, using a combination of singleton and triplets of input features, learns all of the types. The models make use of a principled mechanism for spontaneous firing, and a compensatory Hebbian learning rule. Combined, the mechanisms allow learning to spread to neurons not directly stimulated by the environment. The overall network learns the types of classes in a fashion broadly consistent with the Psychological data. However, the order of speed of learning the types is not entirely consistent with the Psychological data, but may be consistent with one of two Psychological systems a given person possesses. A Psychological test of this hypothesis is proposed.     

Multi‐bit Boolean model for chemotactic drift of Escherichia coli

Dynamic biological systems can be modelled to an equivalent modular structure using Boolean networks (BNs) due to their simple construction and relative ease of integration. The chemotaxis network of the bacterium Escherichia coli (E. coli ) is one of the most investigated biological systems. In this study, the authors developed a multi‐bit Boolean approach to model the drifting behaviour of the E. coli chemotaxis system. Their approach, which is slightly different than the conventional BNs, is designed to provide finer resolution to mimic high‐level functional behaviour. Using this approach, they simulated the transient and steady‐state responses of the chemoreceptor sensory module. Furthermore, they estimated the drift velocity under conditions of the exponential nutrient gradient. Their predictions on chemotactic drifting are in good agreement with the experimental measurements under similar input conditions. Taken together, by simulating chemotactic drifting, they propose that multi‐bit Boolean methodology can be used for modelling complex biological networks. Application of the method towards designing bio‐inspired systems such as nano‐bots is discussed.Inspec keywords: cell motility, microorganisms, Boolean functionsOther keywords: multibit Boolean approach, conventional BNs, high‐level functional behaviour, steady‐state responses, chemoreceptor sensory module, drift velocity, chemotactic drifting, multibit Boolean methodology, complex biological networks, bio‐inspired systems, multibit Boolean model, chemotactic drift, dynamic biological systems, equivalent modular structure, Boolean networks, simple construction, chemotaxis network, bacterium Escherichia coli, biological systems     

Joint fatigue-based optimal posture prediction for maximizing endurance time in box carrying task

Multibody System Dynamics - In this study, the three-compartment controller fatigue model is integrated with an inverse dynamics optimization routine to predict the optimal posture, joint fatigue,...     

Growth of compact arrays of optical quality single crystalline ZnO nanorods by low temperature method

We report the synthesis and optical properties of compact and aligned ZnO nanorod arrays (dia, ∼ 50–200 nm) grown on a glass substrate with varying seed particle density. The suspension of ZnO nanoparticles (size, ∼ 15 nm) of various concentrations are used as seed layer for the growth of nanorod arrays via selfassembly of ZnO from solution. We studied the effect of various growth parameters (such as seeding density, microstructure of the seed layer) as well as the growth time on the growth and alignment of the nanorods. We find that the growth, areal density and alignment of the nanorods depend on the density of seed particles which can be controlled. It is observed that there is a critical density of the seed particles at which nanorod arrays show maximum preferred orientation along [002] direction. The minimum and maximum radius of the aligned nanorods synthesized by this method lie in the range 50–220 nm which depend on the seeding density and time of growth. These nanorods have a bandgap of 3.3 eV as in the case of bulk crystals and show emission in the UV region of the spectrum (∼ 400 nm) due to excitonic recombination and defect related emission in the visible region.     

Use of alumina dispersant in alumina-spinel castable: Comparison between in situ and preformed spinels

The role of alumina dispersant as a flow modifier, completely replacing fume silica, on the properties of preformed and in situ spinel-containing low cement high alumina castables was investigated with variations in spinel content and granulometry. Both 10 and 20 wt.% spinel-containing compositions were prepared using preformed spinel or magnesia in alumina castable compositions with vibratable and self-flowing consistency. The castables underwent conventional processing methods and accessed for various refractory-related parameters post-heat treatments. No glassy phase was observed in the castables upon firing. The presence of self-flowing consistency led to improved characteristics. Preformed spinel-containing compositions showed improved density, strength, hot strength, and thermal shock resistance properties, whereas slag corrosion and penetration resistances were found to be higher for in situ spinel-containing castable.     

Low-Convection-Cooling Slope Cast AlSi7Mg Alloy: A Rheological Perspective

In this paper we made an attempt to assess the solidification and flow behavior of the AlSi7Mg alloy melt flowing down the cooling slope, by calculating the Reynolds number of the flowing melt. It has been found that the length of the laminar regime within the flowing melt (low-convection flow) depends on the angle of slope. The microstructure of as-cast AlSi7Mg alloy processed by low-convection-casting using cooling slope method has been studied. The microstructure reveals dendritic primary α-Al phase with fine fibrous eutectic silicon in the interdendritic regions. The modification of eutectic silicon occurs predominately by the shearing of the solute-rich liquid between the primary α-Al dendrites prior to eutectic solidification as it flows down the cooling slope. Nucleation and growth of the primary silicon dendrites was also observed, which confirms earlier reports on three-layer theory. The mechanism responsible for the refinement of eutectic phase is the enhanced heterogeneous nucleation in the last liquid to solidify.     

Finite Difference Method for Computation of 1D Pollutant Migration through Saturated Homogeneous Soil Media

The physical processes such as advection, dispersion, and diffusion and interaction between the solution and the soil solids such as sorption, biodegradation, and retention processes have been considered in the governing equation used in the present study. Finite difference method has been adopted herein to solve the one-dimensional contaminant transport model to predict the pollutant migration through soil in waste landfill. In the finite difference technique, the velocity field is first determined within a hydrologic system, and these velocities are then used to calculate the rate of contaminant migration by solving the governing equation. A total of seven contaminants have been chosen for analysis to represent a wide variety of wastes both organic and inorganic. A computer software CONTAMINATE has been developed for solution of the contaminant transport model. Results of this study have been compared with existing analytical solution for validation of the proposed solution technique. Design charts for liners have also been developed to facilitate the designers. The liner thickness has been optimized by considering the effect of velocity of advection, dispersion coefficient, and geochemical reactions for all the contaminants of this study.     

Selective Targeting of Human and Animal Pathogens of the Helicobacter Genus by Flavodoxin Inhibitors: Efficacy,Synergy, Resistance and Mechanistic Studies

Antimicrobial resistant (AMR) bacteria constitute a global health concern. Helicobacter pylori is a Gram-negative bacterium that infects about half of the human population and is a major cause of peptic ulcer disease and gastric cancer. Increasing resistance to triple and quadruple H. pylori eradication therapies poses great challenges and urges the development of novel, ideally narrow spectrum, antimicrobials targeting H. pylori. Here, we describe the antimicrobial spectrum of a family of nitrobenzoxadiazol-based antimicrobials initially discovered as inhibitors of flavodoxin: an essential H. pylori protein. Two groups of inhibitors are described. One group is formed by narrow-spectrum compounds, highly specific for H. pylori, but ineffective against enterohepatic Helicobacter species and other Gram-negative or Gram-positive bacteria. The second group includes extended-spectrum antimicrobials additionally targeting Gram-positive bacteria, the Gram-negative Campylobacter jejuni, and most Helicobacter species, but not affecting other Gram-negative pathogens. To identify the binding site of the inhibitors in the flavodoxin structure, several H. pylori-flavodoxin variants have been engineered and tested using isothermal titration calorimetry. An initial study of the inhibitors capacity to generate resistances and of their synergism with antimicrobials commonly used in H. pylori eradication therapies is described. The narrow-spectrum inhibitors, which are expected to affect the microbiota less dramatically than current antimicrobial drugs, offer an opportunity to develop new and specific H. pylori eradication combinations to deal with AMR in H. pylori. On the other hand, the extended-spectrum inhibitors constitute a new family of promising antimicrobials, with a potential use against AMR Gram-positive bacterial pathogens.     

Effect of mixing on the properties of nanocarbon containing Al2O3-C continuous casting refractories

The functional refractories used in steel casting operations are usually made up of alumina-carbon compositions having graphite as the major source of carbon. In recent times, to reduce the carbon content and to enhance the performance by designing microstructure at nano-sized level, several nano carbon sources and organic binders are introduced to traditional carbon-based refractories. The homogenous distribution of nano carbon sources within the refractory composition is important to get the advantages of its use. In the present work, nanocarbon black is used along with graphite in the alumina-carbon system. Three different mixing procedures are followed and how mixing effects the physical and mechanical properties is evaluated. The mixing order with proper nanocarbon distribution throughout the batch composition provided significant improvement in the properties. The microstructural analysis and in situ phase developments in the samples at different temperatures are also studied.