Ni(II) and Ru(II) Schiff base complexes as catalysts for the reduction of benzene

Two new complexes, [MII(L)(Cl)(H2O)2]·H2O (where M=Ni or Ru and L = heterocyclic Schiff base, 3- hydroxyquinoxaline-2-carboxalidene-4-aminoantipyrine), have been synthesized and characterized by elemental analysis, FT-IR, UV–vis diffuse reflectance spectroscopy, FAB-MASS, TG–DTA, AAS, cyclic voltammetry, conductance and magnetic susceptibility measurements. The complexes have a distorted octahedral structure andwere found to be effective catalysts for the hydrogenation of benzene. The influence of several reaction parameters such as reaction time, temperature, hydrogen pressure, concentration of the catalyst and concentration of benzenewas tested. A turnover frequency of 5372 h−1 has been found in the case of ruthenium complex for the reduction of benzene at 80 ◦C with 3.64×10−6 mol catalyst, 0.34 mol benzene and at a hydrogen pressure of 50 bar. In the case of the nickel complex, a turnover frequency of 1718 h−1 has been found for the same reaction with 3.95×10−6 mol catalyst under similar experimental conditions. The nickel complex shows more selectivity for the formation of cyclohexene while the ruthenium complex is more selective for the formation of cyclohexane

Studies on Soft Bycatch Reduction Devices for Selective Trawling

Trawling, though an efficient method of fishing, is known to be one of the most non-selective methods of fish capture. The bulk of the wild caught penaeid shrimps landed in India are caught by trawling.In addition to shrimps, the trawler fleet also catches considerable amount of non-shrimp resources. The term bycatch means that portion of the catch other than target species caught while fishing, which are either retained or discarded. Bycatch discards is a serious problem leading to the depletion of the resources and negative impacts on biodiversity. In order to minimize this problem, trawling has to be made more selective by incorporating Bycatch Reduction Devices (BRDs). There are several advantages in using BRDs in shrimp trawling. BRDs reduce the negative impacts of shrimp trawling on marine community. Fishers could benefit economically from higher catch value due to improved catch quality, shorter sorting time, lower fuel costs, and longer tow duration. Adoption of BRDs by fishers would forestall criticism by conservation groups against trawling.

Investigations on Hard Bycatch Reduction Devices for Selective Trawling

In the context of Indian fisheries there is a paucity of information on bycatch, in general, and bycatch reduction technologies, in particular. In this study, a detailed investigation on trawl bycatch and bycatch reduction measures is attempted with a view to evolve optimized BRDs for improving selectivity of commercial shrimp trawls. The objectives of the study included design and development of hard bycatch reduction devices (BRDs), comparative evaluation of hard bycatch reduction devices, for selective trawling, bycatch characterisation of the trawl landings, off Central Kerala; and investigations on status of the existing trawling systems operated off Central Kerala.

Electrochemical Reduction and Voltammetric Determination of Metronidazole Benzoate at Modified Carbon Paste Electrode

A metalloporphyrin incorporated carbon paste sensor has been developed for the determination of metronidazole benzoate (MTZB). Zn(II) complex of 5,10,15,20-tetrakis (3-methoxy-4-hydroxy phenyl) porphyrin (TMHPP) was used as the active material. The MTZB gave a well-defined reduction peak at - 0.713V in 0.1 mol l -1 phosphate buffer solution of pH around 7. Compared with bare carbon paste electrode (CPE), the TMHPP Zn(II) modified electrode significantly enhanced the reduction peak current of MTZB as well as lowered its reduction potential. Under optimum conditions the reduction peak current was proportional to MTZB concentration over the range 1×10-3 mol1-1 to 1×10-5mol1-1. The detection limit was found to be 4.36×10-6mol1-1 . This sensor has been successfully applied for the determination of MTZB in pharmaceutical formulations and urine samples.

Catalytic activity of lanthanum oxide for the reduction of cyclohexanone

Lanthanum oxide, La2O3 has been found to be an effective catalyst for the liquid phase reduction of cyclohexanone. The catalytic activities of La2O3 activated at 300, 500 and 800·C and its mixed oxides with alumina for the reduction of cyclohexanone with 2-propanol have been determined and the data parallel that of the electron donating properties of the catalysts. The electron donating properties of the catalysts have been determined from the adsorption of electron acceptors of different electron affinities on the surface of these oxides.

Invertase immobilised on montmorillonite: reusability enhancement and reduction in leaching

Invertase was immobilised on microporous montmorillonite K-10 via adsorption and covalent binding. The immobilised enzymes were tested for sucrose hydrolysis activity in a batch reactor. Km for immobilised systems was greater than free enzyme. The immobilised forms could be reused for 15 continuous cycles without any loss in activity. After 25 cycles, 85% initial activity was retained. A study on leaching of enzymes showed that 100% enzyme was retained even after 15 cycles of reuse. Leaching increased with reaction temperature. Covalent binding resisted leaching even at temperatures of 70 °C.

Applications of Fractal Based Structures in Radar Cross Section Reduction

This thesis presents the Radar Cross Section measurements of different geometric structures such as flat plate,cylinder, corner reflector and circular cone loaded with fractal based metallo dielectric structures.Use of different fractal geometris,metallizations of different shapes as well as the frequency tanability is investigated for TE and TM polarization of the incident electromagnetic field.Application of fractal based metallo-dielectric structures results in RCS reduction over a wide range of frequency bands.RCS enhancement of dihedral corner is observed at certain acute and obtuse corner angles.The experimental results are validated using electromagnetic simulation softwares.

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

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.