Open source software is designed, developed and maintained by means of electronic media. These media include discussions on a variety of issues reflecting the evolution of a software system, such as reports on bugs and their fixes, new feature requests, design change, refactoring tasks, test plans, etc. Often this valuable information is simply buried as plain text in the mailing archives. We believe that email interactions collected prior to a product release are related to its source code modifications, or if they do not immediately correlate to change events of the current release, they might affect changes happening in future revisions. In this work, we propose a method to reason about the nature of software changes by mining and correlating electronic mailing list archives. Our approach is based on the assumption that developers use meaningful names and their domain knowledge in defining source code identifiers, such as classes and methods. We employ natural language processing techniques to find similarity between source code change history and history of public interactions surrounding these changes. Exact string matching is applied to find a set of common concepts between discussion vocabulary and changed code vocabulary. We apply our correlation method on two software systems, LSEdit and Apache Ant. The results of these exploratory case studies demonstrate the evidence of similarity between the content of free-form text emails among developers and the actual modifications in the code. We identify a set of correlation patterns between discussion and changed code vocabularies and discover that some releases referred to as minor should instead fall under the major category. These patterns can be used to give estimations about the type of a change and time needed to implement it.

The large-scale production of monoclonal antibodies (MAb) by mammalian cells in batch and fed-batch culture systems is limited by the unwanted decline in cell viability and reduced productivity that may result from changes in culture conditions. Therefore, it becomes imperative to gain an in-depth knowledge of the factors affecting cell growth and cell viability that in turn determine the antibody production. An attempt has been made to obtain an overall model that predicts the behaviour of both batch and fed-batch systems as a function of the extra-cellular nutrient/metabolite concentrations. Such model formulation will aid in identifying and eventually controlling the dominant factors in play to optimize monoclonal antibody (MAb) production in the future. Murine hybridoma 130-8F producing anti-F-glycoprotein monoclonal antibody was grown in D-MEM medium (Gibco 12100) with 2% FBS. A systematic approach based on Metabolic Flux Analysis (MFA) was applied for the calculation of intracellular fluxes for metabolites from available extracellular concentration values. Based on the set of identified significant fluxes (from MFA), the original metabolic network was reduced to a set of significant reactions. The reactions in the reduced metabolic network were then combined to yield a set of macro-reactions obeying Monod kinetics. Half saturation constants were fixed empirically to avoid computational difficulties that parameter estimation for an over-parameterized system of equations would cause. Using Quadratic Programming, the proposed Dynamic Model was calibrated and model prediction was carried out individually for batch and fed-batch runs. Flux distribution for batch and fedbatch modes were compared to determine whether the same model structure could be applied to both the feeding profiles. Correlation analysis was performed to formulate a Biomass Model for predicting cell concentration and viability as a function of the extracellular metabolite concentrations in batch and fed-batch experiments. Quadratic Programming was applied once again for estimation of growth and death coefficients in the equations for viable and dead cell predictions. The prediction accuracy of these model equations was tested by using experimental data from additional runs. Further, the Dynamic Model was integrated with the Biomass Model to get an Integrated Model capable of predicting concentration values for substrates, extracellular metabolites, and viable and dead cell concentration by utilizing only starting concentrations as input. It was found that even though the set of significant fluxes was the same for batch and fedbatch operations, the order of these fluxes was different between the two systems. There was a gradual metabolic shift in the fed-batch system with time indicating that under conditions of nutrient limitation, the available energy is channeled towards maintenance rather than growth. Also, available literature with regard to cell kinetics during fed-batch operation suggests that under nutrient limited conditions, the cells move from a viable, non-apoptotic state to a viable apoptotic state. This is believed to lead to variations in antibody production rates and might explain inaccurate predictions for MAb obtained from the model proposed in the current work. As a result more detailed analysis of the system and in particular, the switch from non-apoptotic to apoptotic state is required. As a continuation of efforts to study the system in-depth, fluorescence imaging is currently being applied as a tool to capture the changes in cell morphology along the course of experimental batch and fed-batch runs. These experiments maybe able to elucidate the transition from non-apoptotic to apoptotic cells and this information maybe used in the future to improve the accuracy of the existing mathematical model.

In pursuit of negatively associated measures, this thesis focuses on certain negative correlation properties in matroids. In particular, the results presented contribute to the search for matroids which satisfy $$P(\{X:e,f\in X\}) \leq P(\{X:e\in X\})P(\{X:f\in X\})$$ for certain measures, $P$, on the ground set. Let $\mathcal M$ be a matroid. Let $(y_g:g\in E)$ be a weighting of the ground set and let $${Z = \sum_{X}\left( \prod_{x\in X} y_x\right) }$$ be the polynomial which generates Z-sets, were Z $\in \{$ B,I,S $\}$. For each of these, the sum is over bases, independent sets and spanning sets, respectively. Let $e$ and $f$ be distinct elements of $E$ and let $Z_e$ indicate partial derivative. Then $\mathcal M$ is Z-Rayleigh if $Z_eZ_f-ZZ_{ef}\geq 0$ for every positive evaluation of the $y_g$s. The known elementary results for the B, I and S-Rayleigh properties and two special cases called negative correlation and balance are proved. Furthermore, several new results are discussed. In particular, if a matroid is binary on at most nine elements or paving or rank three, then it is I-Rayleigh if it is B-Rayleigh. Sparse paving matroids are B-Rayleigh. The I-Rayleigh difference for graphs on at most seven vertices is a sum of monomials times squares of polynomials and this same special form holds for all series parallel graphs.

When a cyclist is cycling on a suburban road, it’s a problem to notice fast rear approaching vehicles in some cases. Looking back frequently is not a good idea. Finding some technical way to help cyclist perceiving rear approaching vehicles is quite necessary. This project aims to find some proper sensor to detect rear approaching vehicles. It’s separated into three steps. First, choose the suitable sensor and capture data. Then, find proper analyzing tool to analyze the capture data. Last but not least, draw a conclusion after analyzing contrast. Microphone is chosen as the sensor to recording the sounds form rear approaching vehicles. ”iRig Recorder FREE” is the program to transfer audio format. And the analyzing tool is to be Matlab. Matlab audio analysis makes good frequency spectrum for each piece of audio data. According to the frequency spectrum, the unique amplitude change around 1000 Hz is found when there is a rear approaching vehicle. This change is always distinct with or without noise. After getting the spectrum of different audio sources, the cross-correlation coefficient between 800 Hz and 1200 Hz is computed to see the correlation level. Then according to cross-correlation coefficient between new captured data and knowledge data, we can determine if there is a rear approaching vehicle in the new data or not. So, this project proves that the cross-correlation coefficient of frequency spectrum can determine if there is rear approaching vehicles or not. The future work would be automatic computer detect depending on this method.

During the 2nd generation Global System for Mobile Communications (GSM) mobile communication, the focus of the mobile positioning was mostly on call setup and messaging. But the evolution of the 3rd generation Wideband Code Division Multiple Access (WCDMA) has changed the focus of mobile positioning. With the increase use of smart phones the mobile positioning is now extensively used for location based services (LBS’s). Mobile positioning becomes extremely important when the user requests any particular LBS, because it directly affects the communication and resource handling between the network and the mobile user MU. In order to reduce cost of messages exchange between the network and the MU it is really important that network should know the location of MU with minimum error. There are many positioning methods that are used today for MU location estimation. In this thesis database correlation method (DCM) is used as a positioning technique to estimate the MU location in the Universal Mobile Telecommunication System UMTS network. The thesis will also explain different penalty techniques for different scenarios that could be used to improve the MU location accuracy in the urban environment. By applying different penalty techniques the best positioning accuracy achieved for 67% of the measurements varies is 88m and for 95% it is 153m. Other penalty techniques results will be compared at the end in order to find the best penalty techniques that offer much improved location accuracy for MU.

It is known that a highly ordered medium allows certain wave functions to move unhindered throughout and in this manner achieve delocalization. It is also known that if one introduces disorder into a medium, wave packets will not be able to move as freely and will instead be trapped or localized. In this thesis, I have simulated a medium in which the amount of disorder can be modified and using this I have shown that the shape of the localization can be altered.

The aim of this paper is discussion on particular aspects of the extension of a classic example in the design of experiments under the presence of correlated errors. Such extension allows us to study the effect of the correlation range on the design. We discuss the dependence of the information gained by the D-optimum design on the covariance bandwidth and also we concentrate to some technical aspects that occurs in such settings. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

The aim of this paper is discussion on particular properties of D-optimal designs under isotropic and intrinsically stationary correlation structures. We show that design points can collapse under the presence of some covariance structures. This enables to include so called nugget effect by natural way. Some numerical examples are also included. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Open source software is designed, developed and maintained by means of electronic media. These media include discussions on a variety of issues reflecting the evolution of a software system, such as reports on bugs and their fixes, new feature requests, design change, refactoring tasks, test plans, etc. Often this valuable information is simply buried as plain text in the mailing archives. We believe that email interactions collected prior to a product release are related to its source code modifications, or if they do not immediately correlate to change events of the current release, they might affect changes happening in future revisions. In this work, we propose a method to reason about the nature of software changes by mining and correlating electronic mailing list archives. Our approach is based on the assumption that developers use meaningful names and their domain knowledge in defining source code identifiers, such as classes and methods. We employ natural language processing techniques to find similarity between source code change history and history of public interactions surrounding these changes. Exact string matching is applied to find a set of common concepts between discussion vocabulary and changed code vocabulary. We apply our correlation method on two software systems, LSEdit and Apache Ant. The results of these exploratory case studies demonstrate the evidence of similarity between the content of free-form text emails among developers and the actual modifications in the code. We identify a set of correlation patterns between discussion and changed code vocabularies and discover that some releases referred to as minor should instead fall under the major category. These patterns can be used to give estimations about the type of a change and time needed to implement it.

The large-scale production of monoclonal antibodies (MAb) by mammalian cells in batch and fed-batch culture systems is limited by the unwanted decline in cell viability and reduced productivity that may result from changes in culture conditions. Therefore, it becomes imperative to gain an in-depth knowledge of the factors affecting cell growth and cell viability that in turn determine the antibody production. An attempt has been made to obtain an overall model that predicts the behaviour of both batch and fed-batch systems as a function of the extra-cellular nutrient/metabolite concentrations. Such model formulation will aid in identifying and eventually controlling the dominant factors in play to optimize monoclonal antibody (MAb) production in the future. Murine hybridoma 130-8F producing anti-F-glycoprotein monoclonal antibody was grown in D-MEM medium (Gibco 12100) with 2% FBS. A systematic approach based on Metabolic Flux Analysis (MFA) was applied for the calculation of intracellular fluxes for metabolites from available extracellular concentration values. Based on the set of identified significant fluxes (from MFA), the original metabolic network was reduced to a set of significant reactions. The reactions in the reduced metabolic network were then combined to yield a set of macro-reactions obeying Monod kinetics. Half saturation constants were fixed empirically to avoid computational difficulties that parameter estimation for an over-parameterized system of equations would cause. Using Quadratic Programming, the proposed Dynamic Model was calibrated and model prediction was carried out individually for batch and fed-batch runs. Flux distribution for batch and fedbatch modes were compared to determine whether the same model structure could be applied to both the feeding profiles. Correlation analysis was performed to formulate a Biomass Model for predicting cell concentration and viability as a function of the extracellular metabolite concentrations in batch and fed-batch experiments. Quadratic Programming was applied once again for estimation of growth and death coefficients in the equations for viable and dead cell predictions. The prediction accuracy of these model equations was tested by using experimental data from additional runs. Further, the Dynamic Model was integrated with the Biomass Model to get an Integrated Model capable of predicting concentration values for substrates, extracellular metabolites, and viable and dead cell concentration by utilizing only starting concentrations as input. It was found that even though the set of significant fluxes was the same for batch and fedbatch operations, the order of these fluxes was different between the two systems. There was a gradual metabolic shift in the fed-batch system with time indicating that under conditions of nutrient limitation, the available energy is channeled towards maintenance rather than growth. Also, available literature with regard to cell kinetics during fed-batch operation suggests that under nutrient limited conditions, the cells move from a viable, non-apoptotic state to a viable apoptotic state. This is believed to lead to variations in antibody production rates and might explain inaccurate predictions for MAb obtained from the model proposed in the current work. As a result more detailed analysis of the system and in particular, the switch from non-apoptotic to apoptotic state is required. As a continuation of efforts to study the system in-depth, fluorescence imaging is currently being applied as a tool to capture the changes in cell morphology along the course of experimental batch and fed-batch runs. These experiments maybe able to elucidate the transition from non-apoptotic to apoptotic cells and this information maybe used in the future to improve the accuracy of the existing mathematical model.