72 resultados para Intelligent Agents


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Three new complexes of Cu(I) have been synthesized using ancillary ligands like thiopyrimidine (tp) a modified nucleobase, and nicotinamide (nie) or vitamin B3, and characterized by spectroscopy and X-ray crystallography. In vitro cytotoxicity studies of the complexes on various human cancer cell lines such as Colo295, H226, HOP62, K562, MCF7 and T24 show that Cu(PPh3)(2)(tp)Cl] and Cu(PPh3)(2)(tp)ClO4 (2) have in vitro cytotoxicity comparable to cisplatin. Complex Cu(nic)(3)PPh3]ClO4 (3) is non-toxic and increases the life span by about 55 % in spontaneous breast tumor model. DNA binding and cleavage studies show that complex (3) binds to calf thymus DNA with an apparent binding constant of 5.9 x 10(5)M and completely cleaves super-coiled DNA at a concentration of 400 mu M, whereas complexes (1) and (2) do not bind DNA and do not show any cleavage even at 1200 mu M. Thus, complex (3) may exhibit cytotoxicity Via DNA cleavage whereas the mechanism of cytotoxicity of (1) and (2) probably involves a different pathway.

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An intelligent computer aided defect analysis (ICADA) system, based on artificial intelligence techniques, has been developed to identify design, process or material parameters which could be responsible for the occurrence of defective castings in a manufacturing campaign. The data on defective castings for a particular time frame, which is an input to the ICADA system, has been analysed. It was observed that a large proportion, i.e. 50-80% of all the defective castings produced in a foundry, have two, three or four types of defects occurring above a threshold proportion, say 10%. Also, a large number of defect types are either not found at all or found in a very small proportion, with a threshold value below 2%. An important feature of the ICADA system is the recognition of this pattern in the analysis. Thirty casting defect types and a large number of causes numbering between 50 and 70 for each, as identified in the AFS analysis of casting defects-the standard reference source for a casting process-constituted the foundation for building the knowledge base. Scientific rationale underlying the formation of a defect during the casting process was identified and 38 metacauses were coded. Process, material and design parameters which contribute to the metacauses were systematically examined and 112 were identified as rootcauses. The interconnections between defects, metacauses and rootcauses were represented as a three tier structured graph and the handling of uncertainty in the occurrence of events such as defects, metacauses and rootcauses was achieved by Bayesian analysis. The hill climbing search technique, associated with forward reasoning, was employed to recognize one or several root causes.

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Levamisole, the imidazo2,1-b]thiazole derivative has been reported as a potential antitumor agent. In the present study, we synthesized, characterized and evaluated biological activity of its novel analogues with substitution in the aralkyl group and on imidazothiadiazole molecules with same chemical backbone but different side chains namely 2-aralkyl-6-(4'-fluorophenyl)-imidazo2,1-b]1,3,4]thiadiazoles (SCR1), 2-aralkyl-5-bromo-6-(4'-fluorophenyl)-imidazo2,1-b]1,3,4]-thiadiaz oles (SCR2), 2-aralkyl-5-formyl-6-(4'-fluorophenyl)-imidazo2,1-b]1,3,4]-thiadia zoles (SCR3) and 2-aralkyl-5-thiocyanato-6-(4'-fluorophenyl)-imidazo2,1-b]1,3,4]-th iadiazoles (SCR4) on leukemia cells. The cytotoxic studies showed that 3a, 4a, and 4c exhibited strong cytotoxicity while others had moderate cytotoxicity. Among these we chose 4a (IC50, 8 mu M) for understanding its mechanism of cytotoxicity. FACS analysis in conjunction with mitochondrial membrane potential and DNA fragmentation studies indicated that 4a induced apoptosis without cell cycle arrest suggesting that it could be used as a potential chemotherapeutic agent. (C) 2011 Elsevier Masson SAS. All rights reserved.

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Reduction of carbon emissions is of paramount importance in the context of global warming. Countries and global companies are now engaged in understanding systematic ways of achieving well defined emission targets. In fact, carbon credits have become significant and strategic instruments of finance for countries and global companies. In this paper, we formulate and suggest a solution to the carbon allocation problem, which involves determining a cost minimizing allocation of carbon credits among different emitting agents. We address this challenge in the context of a global company which is faced with the challenge of determining an allocation of carbon credit caps among its divisions in a cost effective way. The problem is formulated as a reverse auction problem where the company plays the role of a buyer or carbon planning authority and the different divisions within the company are the emitting agents that specify cost curves for carbon credit reductions. Two natural variants of the problem: (a) with unlimited budget and (b) with limited budget are considered. Suitable assumptions are made on the cost curves and in each of the two cases we show that the resulting problem formulation is a knapsack problem that can be solved optimally using a greedy heuristic. The solution of the allocation problem provides critical decision support to global companies engaged seriously in green programs.

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A fuzzy logic intelligent system is developed for gas-turbine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. These four measurements are also called the cockpit parameters and are typically found in almost all older and newer jet engines. The fuzzy logic system uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. It automates the reasoning process of an experienced powerplant engineer. Tests with simulated data show that the fuzzy system isolates faults with an accuracy of 89% with only the four cockpit measurements. However, if additional pressure and temperature probes between the compressors and before the burner, which are often found in newer jet engines, are considered, the fault isolation accuracy rises to as high as 98%. In addition, the additional sensors are useful in keeping the fault isolation system robust as quality of the measured data deteriorates.

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Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are, in general, estimated by fitting the theoretical models to a field monitoring or laboratory experimental data. Double-reservoir diffusion (Transient Through-Diffusion) experiments are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These design parameters are estimated by manual parameter adjusting techniques (also called eye-fitting) like Pollute. In this work an automated inverse model is developed to estimate the mass transport parameters from transient through-diffusion experimental data. The proposed inverse model uses particle swarm optimization (PSO) algorithm which is based on the social behaviour of animals for finding their food sources. Finite difference numerical solution of the transient through-diffusion mathematical model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation.The working principle of the new solver is demonstrated by estimating mass transport parameters from the published transient through-diffusion experimental data. The estimated values are compared with the values obtained by existing procedure. The present technique is robust and efficient. The mass transport parameters are obtained with a very good precision in less time

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In this paper we address the problem of forming procurement networks for items with value adding stages that are linearly arranged. Formation of such procurement networks involves a bottom-up assembly of complex production, assembly, and exchange relationships through supplier selection and contracting decisions. Research in supply chain management has emphasized that such decisions need to take into account the fact that suppliers and buyers are intelligent and rational agents who act strategically. In this paper, we view the problem of procurement network formation (PNF) for multiple units of a single item as a cooperative game where agents cooperate to form a surplus maximizing procurement network and then share the surplus in a fair manner. We study the implications of using the Shapley value as a solution concept for forming such procurement networks. We also present a protocol, based on the extensive form game realization of the Shapley value, for forming these networks.

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This paper presents an intelligent procurement marketplace for finding the best mix of web services to dynamically compose the business process desired by a web service requester. We develop a combinatorial auction approach that leads to an integer programming formulation for the web services composition problem. The model takes into account the Quality of Service (QoS) and Service Level Agreements (SLA) for differentiating among multiple service providers who are capable of fulfilling a functionality. An important feature of the model is interface aware composition.

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Three new series of 4-hydroxy-8-trifluoromethyl-quinoline derivatives were synthesized through multi step reactions. All the newly synthesized compounds were characterized by spectral and elemental analyses. The structure of 5j was evidenced by X-ray crystallographic study. The newly synthesized title compounds were evaluated for their antimicrobial activities including antimycobacterial activity. Amongst the tested compounds, 5b, 5e, 5h, 5j, 6c and 7c displayed promising antimicrobial activity. The mode of action of these active compounds was carried out by docking of receptor enoyl-ACP reductase with newly synthesized candidate ligands, 5b, 5e, 5h, 5j and 6c. (C) 2011 Elsevier Masson SAS. All rights reserved.

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A major myonecrotic zinc containing metalloprotease `malabarin' with thrombin like activity was purified by the combination of gel permeation and anion exchange chromatography from T. malabaricus snake venom. MALDI-TOF analysis of malabarin indicated a molecular mass of 45.76 kDa and its N-terminal sequence was found to be Ile-Ile-Leu-Pro(Leu)-Ile-Gly-Val-Ile-Leu(Glu)-Thr-Thr. Atomic absorption spectral analysis of malabarin raveled the association of zinc metal ion. Malabarin is not lethal when injected i.p. or i.m. but causes extensive hemorrhage and degradation of muscle tissue within 24 hours. Sections of muscle tissue under light microscope revealed hemorrhage and congestion of blood vessel during initial stage followed by extensive muscle fiber necrosis with elevated levels of serum creatine kinase and lactate dehydrogenase activity. Malabarin also exhibited strong procoagulant action and its procoagulant action is due to thrombin like activity; it hydrolyzes fibrinogen to form fibrin clot. The enzyme preferentially hydrolyzes A alpha followed by B beta subunits of fibrinogen from the N-terminal region and the released products were identified as fibrinopeptide A and fibrinopeptide B by MALDI. The myonecrotic, fibrinogenolytic and subsequent procoagulant activities of malabarin was neutralized by specific metalloprotease inhibitors such as EDTA, EGTA and 1, 10-phenanthroline but not by PMSF a specific serine protease inhibitor. Since there is no antivenom available to neutralize local toxicity caused by T. malabaricus snakebite, EDTA chelation therapy may have more clinical relevance over conventional treatment.

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The present article demonstrates how the stiffness, hardness as well as the cellular response of bioinert high-density polyethylene (HDPE) can be significantly improved with combined addition of both bioinert and bioactive ceramic fillers. For this purpose, different amounts of hydroxyapatite and alumina, limited to a total of 40 wt %, have been incorporated in HDPE matrix. An important step in composite fabrication was to select appropriate solvent and optimal addition of coupling agent (CA). In case of chemically coupled composites, 2% Titanium IV, 2-propanolato, tris iso-octadecanoato-O was used as a CA. All the hybrid composites, except monolithic HDPE, were fabricated under optimized compression molding condition (140 degrees C, 0.75 h, 10 MPa pressure). The compression molded composites were characterized, using X-ray diffraction, Fourier transformed infrared spectroscopy, and scanning electron microscopy. Importantly, in vitro cell culture and cell viability study (MTT) using L929 fibroblast and SaOS2 osteoblast-like cells confirmed good cytocompatibility properties of the developed hybrid composites. (C) 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012

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This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.