Static and dynamic bimolecular fluorescence quenching of porphyrin dendrimers in solution

The fluorescence quenching kinetics of two porphyrin dendrimer series (GnTPPH(2) and GnPZn) by different type of quenchers is reported. The microenvironment surrounding the core in GnPZn was probing by core-quencher interactions using benzimidazole. The dependence of quencher binding constant (K(a) ) on generation indicates the presence of a weak interaction between branches and the core of the porphyrin dendrimer. The similar free volume in dendrimers of third and fourth generation suggests that structural collapse in high generations occurs by packing of the dendrimer peripheral layer. Dynamic fluorescence quenching of the porphyrin core by 1,3-dicyanomethylene-2-methyl-2-pentyl-indan (PDCMI) in GnTPPH(2) is a distance dependent electron transfer process with an exponential attenuation factor beta=0.33 angstrom(-1). The quenching by 1,2-dibromobenzene occurs by diffusion process of the quencher toward to the porphyrin core, and its rate constant is practically independent of dendrimer generation.

Fluorescent probes with malononitrile side group in methyl methacrylate copolymers

Fluorescent probes derivated from auramine, 1-aminopyrene, and 9-aminoacridine containing a malononitrile group are copolymerized with methyl methacrylate. These new fluorescent polymeric materials are studied in solution of different solvents by steady-state and time-resolved emission techniques. Their spectroscopic properties and excited state dynamics are driven by charge transfer from the aromatic group to the electron withdrawing CN groups, and this factor is responsible for the non-exponential emission decay behavior. (c) 2008 Elsevier B.V. All rights reserved.

Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing

This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.

Daily Calls Volume Forecasting

A massive amount has been written about forecasting but few articles are written about the development of time series models of call volumes for emergency services. In this study, we use different techniques for forecasting and make the comparison of the techniques for the call volume of the emergency service Rescue 1122 Lahore, Pakistan. For the purpose of this study data is taken from emergency calls of Rescue 1122 from 1st January 2008 to 31 December 2009 and 731 observations are used. Our goal is to develop a simple model that could be used for forecasting the daily call volume. Two different approaches are used for forecasting the daily call volume Box and Jenkins (ARIMA) methodology and Smoothing methodology. We generate the models for forecasting of call volume and present a comparison of the two different techniques.