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.

Study of cyclohexanol decomposition reaction over the ferrospinels, A1−xCuxFe2O4 (A=Ni or Co and x=0, 0.3, 0.5, 0.7 and 1), prepared by ‘soft’ chemical methods

Preparation of simple and mixed ferrospinels of nickel, cobalt and copper and their sulphated analogues by the room temperature coprecipitation method yielded fine particles with high surface areas. Study of the vapour phase decomposition of cyclohexanol at 300 °C over all the ferrospinel systems showed very good conversions yielding cyclohexene by dehydration and/or cyclohexanone by dehydrogenation, as the major products. Sulphation very much enhanced the dehydration activity over all the samples. A good correlation was obtained between the dehydration activities of the simple ferrites and their weak plus medium strength acidities (usually of the Brφnsted type) determined independently by the n-butylamine adsorption and ammonia-TPD methods. Mixed ferrites containing copper showed a general decrease in acidities and a drastic decrease in dehydration activities. There was no general correlation between the basicity parameters obtained by electron donor studies and the ratio of dehydrogenation to dehydration activities. There was a leap in the dehydrogenation activities in the case of all the ferrospinel samples containing copper. Along with the basic properties, the redox properties of copper ion have been invoked to account for this added activity.

Surface properties and catalytic activity of ferrospinels of nickel, cobalt and copper, prepared by soft chemical methods

Ferrospinels of nickel, cobalt and copper and their sulphated analogues were prepared by the room temperature coprecipitation route to yield samples with high surface areas. The intrinsic acidity among the ferrites was found to decrease in the order: cobalt> nickel> copper. Sulphation caused an increase in the number of weak and medium strong acid sites, whereas the strong acid sites were left unaffected. Electron donor studies revealed that copper ferrite has both the highest proportion of strong sites and the lowest proportion of weak basic sites. All the ferrite samples proved to be good catalysts for the benzoy lation of toluene with benzoyl chloride. copper and cobalt ferrites being much more active than nickel ferrite. The catalytic activity for benzoylation was not much influenced by sulphation, but it increased remarkably with calcination temperature of the catalyst. Surface Lewis acid sites, provided by the octahedral cations on the spinel surface, are suggested to be responsible for the catalytic activity for the benzoylation reaction.

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

Characterization of low dielectric constant polyaniline thin film synthesized by ac plasma polymerization technique

Polyaniline thin films were prepared by ac plasma polymerization technique. Capacitance, dielectric loss, dielectric constant and ac conductivity of these films were investigated in the frequency range from 100 Hz to 1MHz and in the temperature range from 300 to 373 K. Capacitance and dielectric loss decreased with frequency and increased with temperature. This type of behaviour was found to be in good agreement with an existing model. The ac conductivity σ(ω) was found to vary as ωs with the index s 1. Annealing of polyaniline thin films in high vacuum at 373K for 1 h was found to reduce the dielectric loss. FTIR studies reveal that the aromatic ring is retained in the polyaniline thin films, which enhances the thermal stability of the polymer films

Large enhanced dielectric permittivity in polyaniline passivated core-shell nano magnetic iron oxide by plasma polymerization

Commercial samples of Magnetite with size ranging from 25–30nm were coated with polyaniline by using radio frequency plasma polymerization to achieve a core shell structure of magnetic nanoparticle (core)–Polyaniline (shell). High resolution transmission electron microscopy images confirm the core shell architecture of polyaniline coated iron oxide. The dielectric properties of the material were studied before and after plasma treatment. The polymer coated magnetite particles exhibited a large dielectric permittivity with respect to uncoated samples. The dielectric behavior was modeled using a Maxwell–Wagner capacitor model. A plausible mechanism for the enhancement of dielectric permittivity is proposed

Contact potential induced enhancement of magnetization in polyaniline coated nanomagnetic iron oxides by plasma polymerization

The present work derives motivation from the so called surface/interfacial magnetism in core shell structures and commercial samples of Fe3O4 and c Fe2O3 with sizes ranging from 20 to 30 nm were coated with polyaniline using plasma polymerization and studied. The High Resolution Transmission Electron Microscopy images indicate a core shell structure after polyaniline coating and exhibited an increase in saturation magnetization by 2 emu/g. For confirmation, plasma polymerization was performed on maghemite nanoparticles which also exhibited an increase in saturation magnetization. This enhanced magnetization is rather surprising and the reason is found to be an interfacial phenomenon resulting from a contact potential.

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems