http://103.7.193.12:8080/xmlui/handle/123456789/8 2026-04-17T15:36:19Z 2026-04-17T15:36:19Z ROY, BALARAM http://103.7.193.12:8080/xmlui/handle/123456789/1860 2022-05-18T06:53:36Z 2000-03-01T00:00:00Z

STUDIES ON THE SYNTHESIS OF SOME BIOLOGICALLY ACTIVE HETEROCYCLIC COMPOUNDS ROY, BALARAM STUDIES ON THE SYNTHESIS OF SOME BIOLOGICALLY ACTIVE HETEROCYCLIC COMPOUNDS A DISSERTATION SUBMITTED TO THE UNIVERSITY OF TOKYO IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DOCTOR OF PHILOSOPHY. SUPERVISED BY: PROFESSOER TAKESHI KITAHARA PERIOD: 1996. 4~ 2000. 3 DEPARTMENT OF APPLIED BIOLOGICAL CHEMISTRY THE UNIVERSITY OF TOKYO BALARAM ROY

2000-03-01T00:00:00Z UDDIN, Md. NAZIM http://103.7.193.12:8080/xmlui/handle/123456789/796 2022-04-24T07:54:29Z 2008-02-01T00:00:00Z

New Triruthenium and Triosmium Clusters Bearing Small Organic Ligands UDDIN, Md. NAZIM This thesis describes the synthesis, structures and reactivity of some new triruthenium and triosmium clusters containing phosphorus, sulfur and oxygen donor ligands. Treatment of [Ru3(CO))2] with thianthrene in refluxing toluene afforded [(p4-S)Ru4(pCO)a(CO)o(Ha-17-CeHy)] 2-1, [(Hs-S)Rug(4~CO)2(CO)is(u-1?-Ci2HgS)] 2.2, and [(UsS)Rus(u-CO)2(CO);1(u-7°-C1zHgS)(W4-1)?-C6H4)] 2.3 respectively. Thermolysis of 2.2 in refluxing heptane gave compounds 2.1 and 2.3. A similar thermolysis of 2.3 in refluxing toluene gave 2.1. Treatment of 2.3 with neat MeCN afforded the labile compound [(sS)Rus(p-CO)2(CO)10(-1?-C12HsS)(p4-7)2-C6Hs)(MeCN)] 2.4. The reaction of 2.4 with P(OMe)3 gave the substitution product [(\1s-S)Rus(p-CO)2(CO)10(p-1?-C12HsS)(p4-717- CH.) {P(OMe)3}] 2.5. Compound 2.1 contains a p14-capping sulfido and a py-1n-benzyne ligand, whereas 2.3, 2.4, and 2.5 contain j1s-sulfido and g-1?-benzyne ligands. The latter three compounds provide rare examples of ys-sulfido and metal-assisted opening of the thianthrene ligand on polynuclear centers. In compounds 2.1, 2.3, and 2.4 the 4-1’- benzyne ligand is perpendicular to the Ruy face of the clusters and represents a previously uncharacterized bonding mode for benzyne. Reaction of [Ru3(CO):0(u-dppm)] 3.1 with P(C4H3S)3 gave the simple substitution product [Ru3(CO)o(u1-dppm){P(C4H3S)3}] 3.2, in which the P(C4H3S)3 ligand is monocoordinated through the phosphorus atom. Thermolysis of 3.2 afforded [Ru3(uH)(CO)7(u1-dppm)(113-17’-C4HpS) {u-P(C4H3S)2}] 3.3 and [Rus(CO)s(4-CO)(u-dppm)(H1s1n°>-SCH=CH-CH=C){1-P(C4H3S)2}] 3.4. Compound 3.3 is formed by the C-P and C—-H bonds activation of the coordinated ligand, thus forming a o, x, n':n':n?-vinyl type bridge among the ruthenium atoms. Compound 3.4 contains an unprecedented example of a coordinated metal assisted ring open p1s-7°-1-thia-1,3-butadiene ligand on a triruthenium cluster surface. Treatment of 3.3 with PPh; afforded the equatorially coordinated phosphine substituted compound [Ru3(u-H)(CO)6(p1-dppm)(t3-7?-C4HoS){- P(C4H3S)2}PPh3] 3.5. Reaction of 3.3 with HBr gave [Ru3(y-H)(CO)¢(u-Br)(q'-Br)(pdppm)(is-1)”-CaH2S){H-P(CsH38)2}] 3.6. Il Treatment of [Ru3(CO)j0(u-dppm)] 3.1 with P(C4H30)3 gave the simple substitution product [Ru3(CO)9(u-dppm){P(C4H30)3}] 4.1, in which the P(C4H30) ligand is monocoordinated through the phosphorus atom. Thermolysis of 4.1 in refluxing dichloromethane in the presence of Me3NO furnished the novel compound [Rus(uH)(CO);(u-dppm)(p13-1)':79':1?-C4H20){1-P(C4H30)}] 4.2. Thermolysis of compound 4.2 gave [Ru3(CO)7(u-dppm)(p3-7':79':97-C4H20){13-P(C4H30)}] 4.3. The reaction of [Ru3(-H)(CO)7(p-dppm)(p13-7n':9)':9?-C4H20){-P(C4H3O)2}] 4.2 with PPh3 afforded the PPh; derivative [Ru3(u-H)(CO)(u-dppm)(p13-1)':9':1?-C4H20){1-P(C4H30)2}PPhs] 4.4. Treatment of compound 4.2 with HBr furnished [Ru3(u-H)(CO)¢(p-Br)(n'-Br)(pdppm)(113-n7-C4H20) {u-P(C4H30)2] 4.5. Reaction of the unsaturated cluster [Os3(u-H)(CO)g{p-Ph2PCH2P (Ph)CsH4}] 5.3 with P(C4H3S)3 at room temperature afforded the new compound [Os3(CO)g(u-dppm)(PTh3)2] 5.17. Thermolysis of [Os3(CO)g(u1-dppm) {P(C4H3S)3)}2] 5.17 in toluene at 80 °C gave two new compounds [Os3(CO)o(u-dppm){P(C4H3S)3}] 5.18 and [Os3(u-H)(CO)7{pPh)PCH>P(Ph)C¢H4} {P(C4H3S)3}] 5.19 whereas a similar reaction of 5.3 with P(C4H30)3 furnished three new compounds [Os3(u-H)(CO)s3 {Ph2PCH2P(Ph)C¢H4}P(C4H30)3] 5.19, [Os3(CO)o(u-dppm) {P(C4H30)3}] 5.20 and [Os3(CO)s(u-dppm){P(C4H30)3}2] 5.21. Compound 5.19 contains a unique example of an electronically unsaturated triosmium cluster in which the phenyl ring of the orthometalated diphosphine ligand is bound to the cluster via a three-center two electron bond. The interaction of small organic molecules with transition metal clusters has received a great deal of attention over the last few decades. The reasons for this are manifold, but are largely derived from the observation that the structure and reactivity of organic fragments coordinated to clusters differ from those coordinated to mononuclear complexes and approach the properties observed chemisorbed on a metal surface. Clearly, if an organic moiety can adopt a multicentre bonding site on a metal surface it cannot be accurately modeled by a single metal atom, whereas even the smallest cluster may act as a reasonable structural model. When accurate surface structures are obtained and compared with crystallographically determined cluster complexes it has been found that the ligand-cluster, adsorbate-surface interactions are remarkably similar.

2008-02-01T00:00:00Z Alam, Md. Ashad http://103.7.193.12:8080/xmlui/handle/123456789/762 2022-04-24T06:36:51Z 2014-09-01T00:00:00Z

Kernel Choice for Unsupervised Kernel Methods Alam, Md. Ashad In kernel methods, choosing a suitable kernel is indispensable for favorable results. While cross-validation is a useful method of the kernel and parameter choice for supervised learning such as the support vector machines, there are no well-founded methods, have been established in general for unsupervised learning. We focus on kernel principal component analysis (kernel PCA) and kernel canonical correlation analysis (kernel CCA), which are the nonlinear extension of principal component analysis (PCA) and canonical correlation analysis (CCA), respectively. Both of these methods have been used effectively for extracting nonlinear features and reducing dimensionality. As a kernel method, kernel PCA and kernel CCA also suffer from the problem of kernel choice. Although cross-validation is a popular method of choosing hyperparameters, it is not applicable straightforwardly to choose a kernel and the number of components in kernel PCA and kernel CCA. It is important, thus, to develop a well-founded method for choosing hyperparameters of the unsupervised methods. In kernel PCA, it is not possible to use cross-validation for choosing hyperparameters because of the incomparable norms given by different kernels. The first goal of the dissertation is to propose a method for choosing hyperparameters in kernel PCA (the kernel and the number of components) based on cross-validation for the comparable reconstruction errors of pre-images in the original space. The experimental results of synthesized and real-world datasets demonstrate that the proposed method successfully selects an appropriate kernel and the number of components in kernel PCA in terms of visualization and classification errors on the principal components. The results imply that the proposed method enables the automatic design of hyperparameters in kernel PCA. XIV In recent years, the influence function of kernel PCA and a robust kernel PCA has been theoretically derived. One observation of their analysis is that kernel PCA with a bounded kernel such as Gaussian is robust in that sense the influence function does not diverged, while for kernel PCA with unbounded kernels for example polynomial the influence function goes to infinity. This can be understood by the boundedness of the transformed data onto the feature space by a bounded kernel. While this is not a result of kernel CCA but for kernel PCA, it is reasonable to expect that kernel CCA with a bounded kernel is also robust. This consideration motivates us to do some empirical studies on the robustness of kernel CCA. It is essential to know how kernel CCA is effected by outliers and to develop measures of accuracy. Therefore, we do intend to study a number of conventional robust estimates and kernel CCA with different functions but fixed parameter of kernel. The second goal of the dissertation is to discuss five canonical correlation coefficients and investigate their performances (robustness) by influence function, sensitivity curve, qualitative robustness index and breakdown point using different type of simulated datasets. The final goal of the dissertation is to extract the limitations of cross-validation for the kernel CCA, and to propose a new regularization approach to overcome the limitations of kernel CCA. As we demonstrate for Gaussian kernels, the cross-validation errors for kernel CCA tend to decrease as the bandwidth parameter of the kernel decreases, which provides inappropriate features with all the data concentrated in a few points. This is caused by the ill-posedness of the kernel CCA with the cross-validation. To solve this problem, we propose to use constraints on the 4th order moments of canonical variables in addition to the variances. Experiments on synthesized and real world datasets including human action recognition for a robot demonstrate that the proposed higher-order regularized kernel CCA can be applied effectively with the cross-validation to find appropriate kernel and regularization parameters. Methods using positive definite kernel (PDK), kernel methods play an increasingly prominent role to solve various problems in statistical machining learning such as, web design, pattern recognition, human action recognition for a robot, computational protein function perdition, remote sensing data analysis and in many other research fields. Due to the kernel trick and reproducing property, we can use linear techniques in feature spaces without knowing explicit forms of either the feature map or feature spaces. It offers versatile tools to process, analyze, and compare many types of data and offers state-of-the-art performance. Nowadays, PDK has become a popular tool for the most branches of statistical machine learning e.g., supervised learning, unsupervised learning, reinforcement learning, non-parametric inference and so on. Many methods have been proposed to kernel methods, which include support vector machine (SVM, Boser et al., 1992), kernel ridge regression (KRR, Saunders et al., 1998), kernel principal component analysis (kernel PCA, Schélkopf et al., 1998), kernel canonical correlation analysis (kernel CCA, Akaho, 2001, Bach and Jordan, 2002), Bayesian inference with positive definite kernels (kernel Bayes’ rule, Fukumizu et al., 2013), gradient-based kernel dimension reduction for regression (gKDR, Fukumizu and Leng, 2014), kernel two-sample test (Gretton, 2012) and so on.

2014-09-01T00:00:00Z Kumer, Ashim http://103.7.193.12:8080/xmlui/handle/123456789/684 2022-04-23T09:16:20Z 2015-12-01T00:00:00Z

STATISTICAL ANALYSIS OF FOOD EXPENDITURE BEHAVIOR OF AN NGO SUPPORTED FARMERS’ FAMILIES IN DINAJPUR: AN APPLICATION OF LA/AIDS MODEL Kumer, Ashim This study analyzed aggregate food expenditure data of marginal and small farmers` families’ collected from the Dinajpur District in the north-western Bangladesh. The Linear Approximate Almost Ideal Demand System (LA/AIDS) method is used to estimate food expenditure and demand function for aggregating the seven food categories. In order to observe the impact of per capita monthly food expenditure, prices of different commodities, household size, dependency ratio, sex, age, food security status and occupation of the household head on the budget share. The study was based on among the NGO beneficiaries program LRP-45 (ActionAid Bangladesh), Ghorgahat and Katabari union in dinajpur District. The food demand and expenditure behavior analysed by sample of size of 165 household was drawn from the enumerated household of 4936 employing simple random sampling method. The AIDS model fits better for all the items as the adjusted R2 values under consideration the regression through-the -origin model as a solution to the problem of Heteroscedasticity. The results revealed that, the allocation of household total monthly expenditure on food items. The mean budget share for Cereals, Roots and Pulses, Vegetables, Rich foods, Milk & Sugar, Oil & Spices and Drugs & Other Luxuries was (52%, 9.5%, 14.6%, 3.0%, 6.8%, 5.8%, & 7.7%) respectively. The empirical findings of the estimated seven expenditure equations are summarized. The expenditure elasticities for food groups are elastic, except cereals, vegetables, and oil & spices. The implication is that food groups of cereals, vegetables, and spices are necessities in the Bangladeshi diet. Roots & pulses, rich foods, milk & sugar, and luxuries foods are luxury goods. Marshallian and Hicksian elasticity calculated from the model were between 1 and -1 making the products less responsive to price changes. The uncompensated own-price elasticties for the food items for cereals (-0.43), vegetables (-1.07), milk & sugar (-0.67), oil & spices (0.83) and luxuries (-1.04) were inelastic, showing that consumers were not sensitive to the price in adjusting their consumption of corresponding items. However, for vegetables, roots & pulses, own-price elasticity of demand were close to one (0.68) implying that quantity demanded for this item changes by almost the same percentage with the price change. That is, if the prices of these food items decreased, then the demand for those food increased. For example, if price of rich foods falls by 10 percent, then demand for rich foods would increase by 19.6 percent. Compensated own and cross-price elasriceties of demand for oil & spices, and rich foods in this case were substitutes. Consumer’s demand behavior is fundamental to understand the demand side of the market. Demand analysts are continuously finding for specifications and functional forms of demand equations which are initials concerned with finding out how the demand for a product will alter as certain specified variables change. The most consistent patterns of consumer demand is the Engel’s Law (1857) which states that as income rises, budget’s share spent on food tends to decline. The estimation of Engel’s models and hence Engel’s curves has a long tradition in empirical economic research. Moreover, in working with Engel’s curve, one important assumption is that consumer’s purchase is mostly influenced by his or her level of income, total expenditure and does not depend on the

2015-12-01T00:00:00Z