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Amorphization is an attractive formulation technique for drugs suffering from poor aqueous solubility as a result of their high lattice energy. Computational models that can predict the material properties associated with amorphization, such as glass-forming ability (GFA) and crystallization behavior in the dry state, would be a time-saving, cost-effective, and material-sparing approach compared to traditional experimental procedures. This article presents predictive models of these properties developed using support vector machine (SVM) algorithm. The GFA and crystallization tendency were investigated by melt-quenching 131 drug molecules in situ using differential scanning calorimetry. The SVM algorithm was used to develop computational models based on calculated molecular descriptors. The analyses confirmed the previously suggested cutoff molecular weight (MW) of 300 for glass-formers, and also clarified the extent to which MW can be used to predict the GFA of compounds with MW < 300. The topological equivalent of Grav3_3D, which is related to molecular size and shape, was a better descriptor than MW for GFA; it was able to accurately predict 86% of the data set regardless of MW. The potential for crystallization was predicted using molecular descriptors reflecting Hückel pi atomic charges and the number of hydrogen bond acceptors. The models developed could be used in the early drug development stage to indicate whether amorphization would be a suitable formulation strategy for improving the dissolution and/or apparent solubility of poorly soluble compounds.

Glass transition temperature (T-g) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between T-g and melting temperature (T-m) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of T-g were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on T-m predicted T-g with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict T-g of drug-like molecules with high accuracy were developed. If T-m is available, a simple linear regression can be used to predict T-g. However, the results also suggest that support vector regression and calculated molecular descriptors can predict T-g with equal accuracy, already before compound synthesis.

Improving product quality is always of industrial interest. Product availability, a function of product maintainability and reliability, is an example of a measurement that can be used to evaluate product quality. Product availability and cost are two units which are especially important to manage in the context of the manufacturing industry, especially where industry is interested in selling or buying offers with increased service content. Industry in general uses different strategies for increasing equipment availability; these include: corrective (immediate or delayed) and preventive strategies. Preventive strategies may be further subdivided into scheduled and predictive (condition-based) maintenance strategies. In turn, predictive maintenance may also be subdivided into scheduled inspection and continuously monitored. The predictive approach can be achieved by early fault detection. Fault detection and diagnosis methods can be classified into three categories: data-driven, analytically based, and knowledge-based methods. In this thesis, the focus is mainly on fault detection and on data-driven models.Furthermore, industry is generating an ever-increasing amount of data, which may eventually become impractical to store and search, and when the data rate is increasing, eventually impossible to store. The ever-increasing amount of data has prompted both industry and researchers to find systems and tools which can control the data on the fly, as close to real-time as possible, without the need to store the data itself. Approaches and tools such as Data Stream Mining (DSM) and Data Stream Management Systems (DSMS) become important. For the work reported in this thesis, DSMS and DSM have been used to control, manage and search data streams, with the purpose of supporting increased availability of industrial products.Bosch Rexroth Mellansel AB (formerly Hägglunds Drives AB) has been the industrial partner company during the course of the work reported in this thesis., Godkänd; 2013; 20130423 (ahmalz); Tillkännagivande disputation 2013-05-24 Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Ahmad Alzghoul Ämne: Datorstödd maskinkonstruktion/Computer Aided Design Avhandling: Mining Data Streams to Increase Industrial Product Availability Opponent: Professor Patrik Eklund, Institutionen för datavetenskap, Umeå universitet Ordförande: Professor Lennart Karlsson, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet Tid: Måndag den 17 juni 2013, kl 09.00 Plats: E231, Luleå tekniska universitet

The objective of this work is to identify the main parameters of the printing press, the printing process, and the paper affecting the occurrence of web breaks in a pressroom. Two approaches are explored. The first one treats the problem as a task of data classification into "break" and "non-break" classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The second approach, targeted for data visualization and also based on genetic search, combines procedures of input variable selection and data mapping into a two-dimensional space. The genetic search-based analysis has shown that the web tension parameters are amongst the most important ones. It was also found that the group of paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the group of traditional parameters recorded off-line at a paper lab. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of web break cases was equal to 76.7%. (C) 2010 Elsevier B. V. All rights reserved.

This paper is concerned with data mining techniques for identifying the main parameters of the printing press, the printing process and paper affecting the occurrence of paper web breaks in a pressroom.Two approaches are explored. The first one treats the problem as a task of data classification into “break” and “non break” classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The search process results in a set of input variables providing the lowest average loss incurred in taking decisions. The second approach, also based on genetic search, combines procedures of input variable selection and data mapping into a low dimensional space. The tests have shown that the web tension parameters are amongst the most important ones. It was also found that, provided the basic off-line paper parameters are in an acceptable range, the paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the off-line ones. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of the break cases was equal to 76.7%.