Studies on Catalysis by Nano Titania Modified with Metals and Nonmetals

Semiconductor photocatalysis has received much attention during last three decades as a promising solution for both energy generation and environmental problems. Heterogeneous photocatalytic oxidation allows the degradation of organic compounds into carbon dioxide and water in the presence of a semiconductor catalyst and UV light source. The •OH radicals formed during the photocatalytic processes are powerful oxidizing agents and can mineralise a number of organic contaminants. Titanium dioxide (TiO2), due to its chemical stability, non-toxicity and low cost represents one of the most efficient photocatalyst. However, only the ultraviolet fraction of the solar radiation is active in the photoexcitation processes using pure TiO2 and although, TiO2 can treat a wide range of organic pollutants the effectiveness of the process for pollution abatement is still low. A more effective and efficient catalyst therefore must be formulated. Doping of TiO2 was considered with the aim of improving photocatalytic properties. In this study TiO2 catalyst was prepared using the sol-gel method. Metal and nonmetal doped TiO2 catalysts were prepared. The photoactivity of the catalyst was evaluated by the photodegradation of different dyes and pesticides in aqueous solution. High photocatalytic degradation of all the pollutants was observed with doped TiO2. Structural and optical properties of the catalysts were characterized using XRD, BET surface area, UV-Vis. DRS, CHNS analysis, SEM, EDX, TEM, XPS, FTIR and TG. All the catalysts showed the anatase phase. The presence of dopants shifts the absorption of TiO2 into the visible region indicating the possibility of using visible light for photocatalytic processes.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

“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.

“Catalysis by Nanocrystalline Ceria Modified with Transition Metals”

The objective of the present work is to improve the textural and structural properties of cerium oxide by the incorporation of transition metals as well as sulphate ions. We have incorporated tungsten, molybdenum and chromium oxide into pure as well as sulphated cerium oxide and the catalytic systems thus prepared were characterised using various techniques. lndustrially important reactions such as acetalization and deacetalization, oxidative dehydrogenation of ethylbenzene, MTBE synthesis and Beckmann rearrangement of cinnamaldoxime and salicylaldoxime have been selected for the measurement of the catalytic activity of the systems. The work is presented in eight chapters

Chemically Modified Glassy Carbon Electrode as Sensors for Various Pharmaceuticals

Voltammetric methods are applicable for the determination of a wide variety of both organic and inorganic species. Its features are compact equipment, simple sample preparation, short analysis time, high accuracy and sensitivity. Voltammetry is especially suitable for laboratories in which only a few parameters have to be monitored with a moderate sample throughput. Of various electrode materials, glassy carbon electrode is particularly useful because of its high electrical conductivity, impermeability to gases, high chemical resistance, reasonable mechanical and dimensional stability and widest potential range of all carbonaceous electrodes. Electrode modification is a vigorous research area by which the electrochemical determination of various analyte species is facilitated. The scope of pharmaceutical analysis includes the analytical investigation of pure drug, drug formulations, impurities and degradation products of drugs, biological samples containing the drugs and their metabolites with the aim of obtaining data that can contribute to the maximal efficacy and maximal safety of drug therapy. This thesis presents the modification of glassy carbon electrode using metalloporphyrin and dyes and subsequently using these modified electrodes for the determination of various pharmaceuticals. The thesis consists of 9 chapters.

‘‘catalysis by nano sulfated titania modified by transition metals"

in the present study, we have prepared and evaluated the physical and chemical properties and catalytic activities of transition metal loaded sulfated titania via the sol-gel route. Sol-gel method is widely used for preparing porous materials having controlled properties and leads to the formation of oxide particles in nano range, which are spherical or interconnected to each other. Characterization using various physico-chemical techniques and a detailed study of acidic properties are also carried out. Some reactions of industrial importance such as Friedel-Crafts reaction, fen-butylation of phenol,Beckmann rearrangement of cyclohexanone oxime, nitration of phenol and photochemical degradation of methylene blue have been selected for catalytic activity study in the present venture. The work is organized into eight chapters

Fixed Point Decimal Multiplication Using RPS Algorithm

Decimal multiplication is an integral part of financial, commercial, and internet-based computations. A novel design for single digit decimal multiplication that reduces the critical path delay and area for an iterative multiplier is proposed in this research. The partial products are generated using single digit multipliers, and are accumulated based on a novel RPS algorithm. This design uses n single digit multipliers for an n × n multiplication. The latency for the multiplication of two n-digit Binary Coded Decimal (BCD) operands is (n + 1) cycles and a new multiplication can begin every n cycle. The accumulation of final partial products and the first iteration of partial product generation for next set of inputs are done simultaneously. This iterative decimal multiplier offers low latency and high throughput, and can be extended for decimal floating-point multiplication.

Synthesis, Characterization and Catalytic Activity of Nanocrystalline Ceria Modified With Zirconia

The use of catalysts in chemical and refining processes has increased rapidly since 1945, when oil began to replace coal as the most important industrial raw material. Catalysis has a major impact on the quality of human life as well as economic development. The demand for catalysts is still increasing since catalysis is looked up as a solution to eliminate or replace polluting processes. Metal oxides represent one of the most important and widely employed classes of solid catalysts. Much effort has been spent in the preparation, characterization and application of metal oxides. Recently, great interest has been devoted to the cerium dioxide (CeO2) containing materials due to their broad range of applications in various fields, ranging from catalysis to ceramics, fuel cell technologies, gas sensors, solid state electrolytes, ceramic biomaterials, etc., in addition to the classical application of CeO2 as an additive in the so-called three way catalysts (TWC) for automotive exhaust treatment. Moreover, it can promote water gas shift and steam reforming reactions, favours catalytic activity at the interfacial metal-support sites. The solid solutions of ceria with Group IV transitional-metals deserve particular attention for their applicability in various technologically important catalytic processes. Mesoporous CeO2−ZrO2 solid solutions have been reported to be employed in various reactions which include CO oxidation, soot oxidation, water-gas shift reaction, and so on. Inspired by the unique and promising characteristics of ceria based mixed oxides and solid solutions for various applications, we have selected ceria-zirconia oxides for our studies. The focus of the work is the synthesis and investigation of the structural and catalytic properties of modified and pure ceria-zirconia mixed oxide.

A Data Mining Approach for the Identification of Adverse Drug Events (ADE) resulting from Drug-Drug Interactions (DDI) to improve pharmacovigilance

In the current study, epidemiology study is done by means of literature survey in groups identified to be at higher potential for DDIs as well as in other cases to explore patterns of DDIs and the factors affecting them. The structure of the FDA Adverse Event Reporting System (FAERS) database is studied and analyzed in detail to identify issues and challenges in data mining the drug-drug interactions. The necessary pre-processing algorithms are developed based on the analysis and the Apriori algorithm is modified to suit the process. Finally, the modules are integrated into a tool to identify DDIs. The results are compared using standard drug interaction database for validation. 31% of the associations obtained were identified to be new and the match with existing interactions was 69%. This match clearly indicates the validity of the methodology and its applicability to similar databases. Formulation of the results using the generic names expanded the relevance of the results to a global scale. The global applicability helps the health care professionals worldwide to observe caution during various stages of drug administration thus considerably enhancing pharmacovigilance

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Parallel Genetic Algorithm for Document Image Compression Optimization

This work proposes a parallel genetic algorithm for compressing scanned document images. A fitness function is designed with Hausdorff distance which determines the terminating condition. The algorithm helps to locate the text lines. A greater compression ratio has achieved with lesser distortion

Planar UWB Antenna with Modified Slotted Ground Plane

A compact coplanar waveguide-fed (CPW) monopole antenna for ultra-wideband wireless communication is presented. The proposed antenna comprises of a CPW-fed beveled rectangular patch with a modified slotted ground. The overall size of the antenna is 30 mm 27 mm 1.6 mm. The lower edge of the band is attained by properly decoupling the resonant frequencies due to the extended ground plane and the beveled rectangular patch of the antenna. The upper edge of the radiating band is enhanced by beveling the ground plane corners near the feed point. Experimental results show that the designed antenna operates in the 2.7–12 GHz band, for S11 10 dB with a gain of 2.7–5 dBi. Both the frequency domain and time domain characteristics of the antenna are investigated using antenna transfer function. It is observed that the antenna exhibits identical radiation patterns and reasonable transient characteristics over the entire operating band

A Compact Modified Ground CPW Fed Antenna for UWB Applications

A Coplanar waveguide fed compact planar monopole antenna with a modified ground plane is presented. Measured and simulated results reveal that the antenna operates in the Ultra Wide Band with almost constant group delay throughout the band. Developed design equations of the antenna are validated for different substrates. Time domain performance of the antenna is also discussed in order to assess its suitability for impulse radio applications

Effect of thermal annealing on Fe40Ni38B18Mo4 thin films: modified Herzer model for magnetic evolution

Magnetic properties of nano-crystalline soft magnetic alloys have usually been correlated to structural evolution with heat treatment. However, literature reports pertaining to the study of nano-crystalline thin films are less abundant. Thin films of Fe40Ni38B18Mo4 were deposited on glass substrates under a high vacuum of ≈ 10−6 Torr by employing resistive heating. They were annealed at various temperatures ranging from 373 to 773K based on differential scanning calorimetric studies carried out on the ribbons. The magnetic characteristics were investigated using vibrating sample magnetometry. Morphological characterizations were carried out using atomic force microscopy (AFM), and magnetic force microscopy (MFM) imaging is used to study the domain characteristics. The variation of magnetic properties with thermal annealing is also investigated. From AFM and MFM images it can be inferred that the crystallization temperature of the as-prepared films are lower than their bulk counterparts. Also there is a progressive evolution of coercivity up to 573 K, which is an indication of the lowering of nano-crystallization temperature in thin films. The variation of coercivity with the structural evolution of the thin films with annealing is discussed and a plausible explanation is provided using the modified random anisotropy model

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.