Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.

<p>In this thesis a novel method for controlling a manipulator with arbitrary number of Degrees of freedom is proposed, the proposed method has the main advantages of two common controllers, the simplicity of PID controller and the robustness and accuracy of adaptive controller. The controller architecture is based on an Artificial Neural Network (ANN) and a PID controller.</p><p>The controller has the ability of solving inverse dynamics and inverse kinematics of robot with two separate Artificial Neural Networks. Since the ANN is learning the system parameters by itself the structure of controller can easily be changed to</p><p>improve the performance of robot.</p><p>The proposed controller can be implemented on a FPGA board to control the robot in real-time or the response of the ANN can be calculated offline and be reconstructed by controller using a lookup table. Error between the desired trajectory path and the path of the robot converges to zero rapidly and as the robot performs its tasks the controller learns the robot parameters and generates better control signal. The performance of controller is tested in simulation and on a real manipulator with satisfactory results.</p>

<p>Erfarenheter från tidigare försök på Korsnäs AB visar att det är väldigt svårt att på matematisk väg förutsäga vad som händer under framställningen av pappersmassa i en kontinuerlig kokare.</p><p>Målet med detta examensarbete var att undersöka möjligheterna att med hjälp av neurala nätverk underlätta regleringen genom att prediktera ligninhalten hos pappersmassan tre och en halv timme innan den aktuella flisen är färdigkokt.</p><p>På grund av den, med produktionstakten, varierande tidsförskjutningen mellan olika givarsignaler löstes problemet med en enkel, lokal modell per produktionstakt. Alla ingående modeller minimeras med avseende på både antalet noder i det gömda lagret och antalet ingångar, vilket gav en slutlig lösning med fyra enkla modeller uppbyggda av framåtkopplade neurala nätverk, var och ett med ett gömt lager innehållandes tre noder.</p><p>Prediktionen av ligninhalten påvisade till slut goda egenskaper, med avseende på hur väl prediktionen följer den verkliga kappatalsanalysatorn.</p>

<p>The purpose of this dissertation is to chart the development of an ambivalent female identity in the Gothic genre, as exemplified by Ann Radcliffe&rsquo / s late eighteenth century fictions. The thesis examines the social and literary context of the emergence of the Gothic in English literature and argues that it is intimately tied up with changes in social, political and gender relations in the period.</p>

In this thesis a novel method for controlling a manipulator with arbitrary number of Degrees of freedom is proposed, the proposed method has the main advantages of two common controllers, the simplicity of PID controller and the robustness and accuracy of adaptive controller. The controller architecture is based on an Artificial Neural Network (ANN) and a PID controller. The controller has the ability of solving inverse dynamics and inverse kinematics of robot with two separate Artificial Neural Networks. Since the ANN is learning the system parameters by itself the structure of controller can easily be changed to improve the performance of robot. The proposed controller can be implemented on a FPGA board to control the robot in real-time or the response of the ANN can be calculated offline and be reconstructed by controller using a lookup table. Error between the desired trajectory path and the path of the robot converges to zero rapidly and as the robot performs its tasks the controller learns the robot parameters and generates better control signal. The performance of controller is tested in simulation and on a real manipulator with satisfactory results.

ABSTRACT Granite Butterfly is a novel about three women—grandmother, mother, and daughter—and the unusual attachments that break apart their family. Tuula Laine is a Rockport, Massachusetts, native of Finnish descent, whose parents moved to Cape Ann for work in the area’s granite quarries. Her life changes one afternoon when her son Henri, a brilliant surgeon who has never seriously dated anyone before, visits with his pregnant girlfriend, Coreen. Tuula immediately senses that Coreen not the right match for him in terms of age, education, or temperament, and as the couple separates and unites over the course of one summer, Tuula witnesses, for the first time, the pattern of desire and abandonment that will define their relationship. By the time Tuula’s granddaughter, Suvi, is fourteen years old, she, too, has established a destructive relationship pattern with Coreen: whenever Coreen and Henri separate, Suvi’s mother clings to her until they develop a bond closer to that of sisters than a mother and child. In the final movement of the novel, this bond, and the bond between Suvi’s parents, is finally put to the test. Granite is cut into precise blocks—dynamite is never used, lest it shatter the stone. In a few short weeks, the Laine family is pulled apart, but unlike with quarrying, there is no way to divide them in a careful manner, no way to detach them that isn’t violent and abrupt, no way to predict, or guide, where they will split.

Erfarenheter från tidigare försök på Korsnäs AB visar att det är väldigt svårt att på matematisk väg förutsäga vad som händer under framställningen av pappersmassa i en kontinuerlig kokare. Målet med detta examensarbete var att undersöka möjligheterna att med hjälp av neurala nätverk underlätta regleringen genom att prediktera ligninhalten hos pappersmassan tre och en halv timme innan den aktuella flisen är färdigkokt. På grund av den, med produktionstakten, varierande tidsförskjutningen mellan olika givarsignaler löstes problemet med en enkel, lokal modell per produktionstakt. Alla ingående modeller minimeras med avseende på både antalet noder i det gömda lagret och antalet ingångar, vilket gav en slutlig lösning med fyra enkla modeller uppbyggda av framåtkopplade neurala nätverk, var och ett med ett gömt lager innehållandes tre noder. Prediktionen av ligninhalten påvisade till slut goda egenskaper, med avseende på hur väl prediktionen följer den verkliga kappatalsanalysatorn.

The owners of a house in today’s society do not know in real-time how much electricity they use. It could be beneficial for any residential consumer to have more control and overview in real-time over the electricity consumption. This could be done possible with a system that monitors the consumptions, micro renewables and the electricity prices from the grid and then makes a decision to either use or sell electricity to reduce the monthly electricity cost for the household and living a "Greener" life to reduce carbon emissions. In this thesis, estimations are made based on artificial neural network (ANN). The predictions are made for air temperature, solar insolation and wind speed in order to know how much energy will be produced in the next 24 hours from the solar panel and from the wind turbine. The predictions are made for electricity consumption in order to know how much energy the house will consume. These predictions are then used as an input to the system. The system has 3 controls, one to control the amount of sell or buy the energy, one to control the amount of energy to charge or discharge the fixed battery and one to control the amount of energy to charge or discharge the electric vehicle (EV). The output from the system will be the decision for the next 10 minutes for each of the 3 controls. To study the reliability of the ANN estimations, the ANN estimations (SANN) are compared with the real data (Sreal ) and other estimation based on the mean values (Smean) of the previous week. The simulation during a day in January gave that the expenses are 0.6285 € if using SANN, 0.7788 € if using Smean and 0.5974 € if using Sreal. Further, 3 different cases are considered to calculate the savings based on the ANN estimations. The first case is to have the system connected with fixed storage device and EV (Scon;batt ). The second and third cases are to have the system disconnected (without fixed battery) using micro generation (Sdiscon;micro) and not using micro generation (Sdiscon) along with the EV. The savings are calculated as a difference between Scon;batt and Sdiscon, also between Sdiscon;micro and Sdiscon. The saving are 788.68 € during a year if Scon;batt is used and 593.90 € during a year if Sdiscon;micro is used. With the calculated savings and the cost for the equipment, the pay-back period is 15.3 years for Scon;batt and 4.5 years for Sdiscon;micro. It is profitable to only use micro generation, but then the owner of the household loses the opportunity to be part of helping the society to become "Greener".

No description available.

The Finite-Difference Time-Domain Method (FDTD) is a very powerful numerical method for the full wave analysis electromagnetic phenomena. Due to its flexibility, it can be used to solve numerous electromagnetic scattering problems on microwave circuits, dielectrics, and electromagnetic absorption in biological tissue at microwave frequencies. However, it needs so much computation time to simulate microwave integral circuits by applying the FDTD method. If the structure we simulated is complicated and we want to obtain accurate frequency domain scattering parameters, the simulation time will be so much longer that the efficiency of simulation will be bad as well. Therefore, in the thesis, we introduce an artificial neural networks (ANN) method called ¡§Infinite Impulse Response Neural Networks (IIRNN)¡¨ can speed up the FDTD simulation time. In order to boost the efficiency of the FDTD simulation time by stopping the simulation after a sufficient number of time steps and using FIRNN as a predictor to predict time series signal.