Nitrifying Bioreactors Integrated into Shrimp and Prawn Hatchery Systems: Molecular Characterization of the Nitrifying Consortia,Reactor Kinetics, Modeling and Validation

National Centre for Aquatic Animal Health, Cochin University of Science and Technology

Kinetics of martensitic transitions in Cu-Al-Mn under thermal cycling: Analysis at multiple length scales

In this paper we study the evolution of the kinetic features of the martensitic transition in a Cu-Al-Mn single crystal under thermal cycling. The use of several experimental techniques including optical microscopy, calorimetry, and acoustic emission, has enabled us to perform an analysis at multiple scales. In particular, we have focused on the analysis of avalanche events (associated with the nucleation and growth of martensitic domains), which occur during the transition. There are significant differences between the kinetics at large and small length scales. On the one hand, at small length scales, small avalanche events tend to sum to give new larger events in subsequent loops. On the other hand, at large length scales the large domains tend to split into smaller ones on thermal cycling. We suggest that such different behavior is the necessary ingredient that leads the system to the final critical state corresponding to a power-law distribution of avalanches.

Culture and growth kinetics of selected nanoplankters

The present study on naoplankton is based on the isolation and development of unialgai culturas from the inshore waters at Cochin. characterization of their growth assimilation products. ecophysioiogy and evaluation of nutritional quality. The work was carried out during the period 1980-1983. The nanoplankters were isolated and grown in the labratory as batch cultures to study the increase in cell population, the photosynthetic pigment: ana physioiogical activity. The chemical composition of these organisms and their rate of excretion were also determined. The environmental factors physical and chemical that influence the growth of these Cultures were defined by conducting independentexperiments. These cultures of the isolated nanoplankters have raised indoor and fed to the larvae of edible oyster to test their suitability as live-food.

Kinetics and mechanism of urea - formaldehyde and related reactions

Urea-formaldehyde resins find numerous applications in adhesive, textile finishing and moulded plastic industries. Kinetic investigations of the reactions of urea and its related compounds with formaldehyde in aqueous acid, alkaline and neutral media have been carried out. A thin—layer chromatographic method was developed for the separation and estimation of the products of these reactions. Using this technique the various initial steps in the reactions were analysed and the rate constants have been determined.

Reduction of radar cross section of targets using periodic strip grating technique

The- classic: experiment of Heinrich Hertz verified the theoretical predict him of Maxwell that kxnfli radio and light waves are physical phenomena governed by the same physical laws. This has started a.rnnJ era of interest in interaction of electromagnetic energy with matter. The scattering of electromagnetic waves from a target is cleverly utilized im1 RADAR. This electronic system used tx> detect and locate objects under unfavourable conditions or obscuration that would render the unaided eye useless. It also provides a means for measuring precisely the range, or distance of an object and the speed of a moving object. when an obstacle is illuminated by electromagnetic waves, energy is dispersed in all directions. The dispersed energy depends on the size, shape and composition of the obstacle and frequency and nature of the incident wave. This distribution of energy’ is known as ‘scattering’ and the obstacle as ‘scatterer’ or 'target'.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Cure Kinetics and Sorption Characteristics of Neoprene-Based Rubber Ferrite Composites

Rubber ferrite composites were prepared by incorporating nickel ferrite in a neoprene rubber matrix. Kinetics of the cure reaction were determined from the rheometric torque values and found to follow first-order kinetics. Analysis of the swelling behavior of the rubber ferrite composites in toluene elucidates the mechanism of solvent penetration and sorption characteristics, and reveals the extent of the physical interaction of the ferrite particles with the neoprene rubber matrix. Mechanical properties of rubber ferrite composites were determined, which support the reinforcing nature of nickel ferrite to the neoprene rubber matrix. These results show that magnetic composites with the required processing safety can be prepared economically by incorporating higher amounts of nickel ferrite in the neoprene rubber matrix

Studies on Advanced Oxidation Processes for the Removal of Acetamiprid from Wastewater

Pollution of water with pesticides has become a threat to the man, material and environment. The pesticides released to the environment reach the water bodies through run off. Industrial wastewater from pesticide manufacturing industries contains pesticides at higher concentration and hence a major source of water pollution. Pesticides create a lot of health and environmental hazards which include diseases like cancer, liver and kidney disorders, reproductive disorders, fatal death, birth defects etc. Conventional wastewater treatment plants based on biological treatment are not efficient to remove these compounds to the desired level. Most of the pesticides are phyto-toxic i.e., they kill the microorganism responsible for the degradation and are recalcitrant in nature. Advanced oxidation process (AOP) is a class of oxidation techniques where hydroxyl radicals are employed for oxidation of pollutants. AOPs have the ability to totally mineralise the organic pollutants to CO2 and water. Different methods are employed for the generation of hydroxyl radicals in AOP systems. Acetamiprid is a neonicotinoid insecticide widely used to control sucking type insects on crops such as leafy vegetables, citrus fruits, pome fruits, grapes, cotton, ornamental flowers. It is now recommended as a substitute for organophosphorous pesticides. Since its use is increasing, its presence is increasingly found in the environment. It has high water solubility and is not easily biodegradable. It has the potential to pollute surface and ground waters. Here, the use of AOPs for the removal of acetamiprid from wastewater has been investigated. Five methods were selected for the study based on literature survey and preliminary experiments conducted. Fenton process, UV treatment, UV/ H2O2 process, photo-Fenton and photocatalysis using TiO2 were selected for study. Undoped TiO2 and TiO2 doped with Cu and Fe were prepared by sol-gel method. Characterisation of the prepared catalysts was done by X-ray diffraction, scanning electron microscope, differential thermal analysis and thermogravimetric analysis. Influence of major operating parameters on the removal of acetamiprid has been investigated. All the experiments were designed using central compoiste design (CCD) of response surface methodology (RSM). Model equations were developed for Fenton, UV/ H2O2, photo-Fenton and photocatalysis for predicting acetamiprid removal and total organic carbon (TOC) removal for different operating conditions. Quality of the models were analysed by statistical methods. Experimental validations were also done to confirm the quality of the models. Optimum conditions obtained by experiment were verified with that obtained using response optimiser. Fenton Process is the simplest and oldest AOP where hydrogen peroxide and iron are employed for the generation of hydroxyl radicals. Influence of H2O2 and Fe2+ on the acetamiprid removal and TOC removal by Fenton process were investigated and it was found that removal increases with increase in H2O2 and Fe2+ concentration. At an initial concentration of 50 mg/L acetamiprid, 200 mg/L H2O2 and 20 mg/L Fe2+ at pH 3 was found to be optimum for acetamiprid removal. For UV treatment effect of pH was studied and it was found that pH has not much effect on the removal rate. Addition of H2O2 to UV process increased the removal rate because of the hydroxyl radical formation due to photolyis of H2O2. An H2O2 concentration of 110 mg/L at pH 6 was found to be optimum for acetamiprid removal. With photo-Fenton drastic reduction in the treatment time was observed with 10 times reduction in the amount of reagents required. H2O2 concentration of 20 mg/L and Fe2+ concentration of 2 mg/L was found to be optimum at pH 3. With TiO2 photocatalysis improvement in the removal rate was noticed compared to UV treatment. Effect of Cu and Fe doping on the photocatalytic activity under UV light was studied and it was observed that Cu doping enhanced the removal rate slightly while Fe doping has decreased the removal rate. Maximum acetamiprid removal was observed for an optimum catalyst loading of 1000 mg/L and Cu concentration of 1 wt%. It was noticed that mineralisation efficiency of the processes is low compared to acetamiprid removal efficiency. This may be due to the presence of stable intermediate compounds formed during degradation Kinetic studies were conducted for all the treatment processes and it was found that all processes follow pseudo-first order kinetics. Kinetic constants were found out from the experimental data for all the processes and half lives were calculated. The rate of reaction was in the order, photo- Fenton>UV/ H2O2>Fenton> TiO2 photocatalysis>UV. Operating cost was calculated for the processes and it was found that photo-Fenton removes the acetamiprid at lowest operating cost in lesser time. A kinetic model was developed for photo-Fenton process using the elementary reaction data and mass balance equations for the species involved in the process. Variation of acetamiprid concentration with time for different H2O2 and Fe2+ concentration at pH 3 can be found out using this model. The model was validated by comparing the simulated concentration profiles with that obtained from experiments. This study established the viability of the selected AOPs for the removal of acetamiprid from wastewater. Of the studied AOPs photo- Fenton gives the highest removal efficiency with lowest operating cost within shortest time.

Learning analysis by reduction from positive data

Analysis by reduction is a linguistically motivated method for checking correctness of a sentence. It can be modelled by restarting automata. In this paper we propose a method for learning restarting automata which are strictly locally testable (SLT-R-automata). The method is based on the concept of identification in the limit from positive examples only. Also we characterize the class of languages accepted by SLT-R-automata with respect to the Chomsky hierarchy.

Noise Reduction Using Low Weight and Constant Weight Coding Techniques

Signalling off-chip requires significant current. As a result, a chip's power-supply current changes drastically during certain output-bus transitions. These current fluctuations cause a voltage drop between the chip and circuit board due to the parasitic inductance of the power-supply package leads. Digital designers often go to great lengths to reduce this "transmitted" noise. Cray, for instance, carefully balances output signals using a technique called differential signalling to guarantee a chip has constant output current. Transmitted-noise reduction costs Cray a factor of two in output pins and wires. Coding achieves similar results at smaller costs.

Using Recurrent Networks for Dimensionality Reduction

This report explores how recurrent neural networks can be exploited for learning high-dimensional mappings. Since recurrent networks are as powerful as Turing machines, an interesting question is how recurrent networks can be used to simplify the problem of learning from examples. The main problem with learning high-dimensional functions is the curse of dimensionality which roughly states that the number of examples needed to learn a function increases exponentially with input dimension. This thesis proposes a way of avoiding this problem by using a recurrent network to decompose a high-dimensional function into many lower dimensional functions connected in a feedback loop.

Understanding Subsystems in Biology through Dimensionality Reduction, Graph Partitioning and Analytical Modeling

Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. This set of subsystems was then used to predict functional relationships between genes, and this approach was shown to score significantly higher than conventional methods when benchmarking them against existing databases. Moreover, a mathematical treatment was developed to treat simple network subsystems based only on their topology (independent of particular parameter values). Application to a problem of experimental interest demonstrated the need for extentions to the conventional model to fully explain the experimental data. Finally, the notion of a subsystem was evaluated from a topological perspective. A number of different protein networks were examined to analyze their topological properties with respect to separability, seeking to find separable subsystems. These networks were shown to exhibit separability in a nonintuitive fashion, while the separable subsystems were of strong biological significance. It was demonstrated that the separability property found was not due to incomplete or biased data, but is likely to reflect biological structure.

Nanocrystalline Ge Flash Memories: Electrical Characterization and Trap Engineering

Conventional floating gate non-volatile memories (NVMs) present critical issues for device scalability beyond the sub-90 nm node, such as gate length and tunnel oxide thickness reduction. Nanocrystalline germanium (nc-Ge) quantum dot flash memories are fully CMOS compatible technology based on discrete isolated charge storage nodules which have the potential of pushing further the scalability of conventional NVMs. Quantum dot memories offer lower operating voltages as compared to conventional floating-gate (FG) Flash memories due to thinner tunnel dielectrics which allow higher tunneling probabilities. The isolated charge nodules suppress charge loss through lateral paths, thereby achieving a superior charge retention time. Despite the considerable amount of efforts devoted to the study of nanocrystal Flash memories, the charge storage mechanism remains obscure. Interfacial defects of the nanocrystals seem to play a role in charge storage in recent studies, although storage in the nanocrystal conduction band by quantum confinement has been reported earlier. In this work, a single transistor memory structure with threshold voltage shift, Vth, exceeding ~1.5 V corresponding to interface charge trapping in nc-Ge, operating at 0.96 MV/cm, is presented. The trapping effect is eliminated when nc-Ge is synthesized in forming gas thus excluding the possibility of quantum confinement and Coulomb blockade effects. Through discharging kinetics, the model of deep level trap charge storage is confirmed. The trap energy level is dependent on the matrix which confines the nc-Ge.