Characterisation and processing of amorphous binary mixtures with low glass transition temperature

The number of drug substances in formulation development in the pharmaceutical industry is increasing. Some of these are amorphous drugs and have glass transition below ambient temperature, and thus they are usually difficult to formulate and handle. One reason for this is the reduced viscosity, related to the stickiness of the drug, that makes them complicated to handle in unit operations. Thus, the aim in this thesis was to develop a new processing method for a sticky amorphous model material. Furthermore, model materials were characterised before and after formulation, using several characterisation methods, to understand more precisely the prerequisites for physical stability of amorphous state against crystallisation. The model materials used were monoclinic paracetamol and citric acid anhydrate. Amorphous materials were prepared by melt quenching or by ethanol evaporation methods. The melt blends were found to have slightly higher viscosity than the ethanol evaporated materials. However, melt produced materials crystallised more easily upon consecutive shearing than ethanol evaporated materials. The only material that did not crystallise during shearing was a 50/50 (w/w, %) blend regardless of the preparation method and it was physically stable at least two years in dry conditions. Shearing at varying temperatures was established to measure the physical stability of amorphous materials in processing and storage conditions. The actual physical stability of the blends was better than the pure amorphous materials at ambient temperature. Molecular mobility was not related to the physical stability of the amorphous blends, observed as crystallisation. Molecular mobility of the 50/50 blend derived from a spectral linewidth as a function of temperature using solid state NMR correlated better with the molecular mobility derived from a rheometer than that of differential scanning calorimetry data. Based on the results obtained, the effect of molecular interactions, thermodynamic driving force and miscibility of the blends are discussed as the key factors to stabilise the blends. The stickiness was found to be affected glass transition and viscosity. Ultrasound extrusion and cutting were successfully tested to increase the processability of sticky material. Furthermore, it was found to be possible to process the physically stable 50/50 blend in a supercooled liquid state instead of a glassy state. The method was not found to accelerate the crystallisation. This may open up new possibilities to process amorphous materials that are otherwise impossible to manufacture into solid dosage forms.

Ti-Ti Nallesta Hevisaurukseen : muutokset suomalaisessa lastenmusiikkikulttuurissa ja lastenmusiikin asemassa

Pro gradu -tutkielmani käsittelee levytettyä lastenmusiikkia Suomessa, lastenmusiikintekijöitä ja lastenmusiikin asemaa levy-yhtiöissä. Lastenmusiikin kentällä on viime vuosina ilmennyt uusi aalto, joka on lisännyt kiinnostusta lastenmusiikin laatuun. Työssäni tarkoitan lastenmusiikilla nimenomaan levytettyä kaupallista lastenmusiikkia, joka on tuotettu varta vasten lapsille. Lastenmusiikkikulttuuri ei asetu jäännöksettömästi mihinkään musiikinkategoriaan. Lastenmusiikki voi sisältää elementtejä yhtä hyvin populaari-, kansan-, maailman- ja taidemusiikista. Lastenmusiikkiäänitteitä on julkaistu lähes tulkoon läpi koko suomalaisen äänilevyteollisuuden historian. Pro graduni yhtenä tarkoituksena on selventää, miten eri toimijat lastenmusiikin kentällä ovat sijoittuneet ja mikä rooli lastenmusiikilla on osana isompaa kokonaisuutta, suomalaista levyteollisuutta. Suomessa lastenkulttuurin asemaa, sisältöjä ja tekijöitä on tutkittu erittäin vähän. Lastenmusiikin historiaa ei myöskään ole tutkittu laajasti, ja lastenmusiikin kentän nykytilan tutkimus on ollut vähäistä. Pro graduni esimerkkiyhtyeet ovat pitkänlinjan lastenmusiikkiyhtyeitä ja kasvatustieteen ammattilaisia. Musiikkikasvatuksella on iso rooli suuressa osassa kotimaista lastenmusiikkia. Lapsille suunnattujen musiikkituotteiden määrä on kasvanut viime vuosina, ja lastenmusiikin kentälle tulee jatkuvasti uusia tekijöitä. Lastenkulttuurin tuotteistaminen on tänä päivänä tehokasta. Työssäni pyrin löytämään syitä siihen, miksi lastenmusiikki ei kuitenkaan aina löydä kuulijoitaan. On oleellista tietää, minkä kanssa lastenmusiikkiprojektit kilpailevat esimerkiksi rahoituksesta ja mediahuomiosta. Pro graduni tarkoitus on vastata lastenmusiikin tutkimustarpeeseen ja esitellä lastenmusiikin nykykentän toimijoita ja lastenmusiikin monimuotoisuutta. Päähuomioni kiinnittyy lastenmusiikin asemaan sekä mahdollisuuksiin, joiden avulla lastenmusiikin asemaa voitaisiin kohentaa.

Atomic Layer Deposition of Multicomponent Oxide Materials

Atomic layer deposition (ALD) is a method for thin film deposition which has been extensively studied for binary oxide thin film growth. Studies on multicomponent oxide growth by ALD remain relatively few owing to the increased number of factors that come into play when more than one metal is employed. More metal precursors are required, and the surface may change significantly during successive stages of the growth. Multicomponent oxide thin films can be prepared in a well-controlled way as long as the same principle that makes binary oxide ALD work so well is followed for each constituent element: in short, the film growth has to be self-limiting. ALD of various multicomponent oxides was studied. SrTiO3, BaTiO3, Ba(1-x)SrxTiO3 (BST), SrTa2O6, Bi4Ti3O12, BiTaO4 and SrBi2Ta2O9 (SBT) thin films were prepared, many of them for the first time by ALD. Chemistries of the binary oxides are shown to influence the processing of their multicomponent counterparts. The compatibility of precursor volatilities, thermal stabilities and reactivities is essential for multicomponent oxide ALD, but it should be noted that the main reactive species, the growing film itself, must also be compatible with self-limiting growth chemistry. In the cases of BaO and Bi2O3 the growth of the binary oxide was very difficult, but the presence of Ti or Ta in the growing film made self-limiting growth possible. The application of the deposited films as dielectric and ferroelectric materials was studied. Post-deposition annealing treatments in different atmospheres were used to achieve the desired crystalline phase or, more generally, to improve electrical properties. Electrode materials strongly influenced the leakage current densities in the prepared metal insulator metal (MIM) capacitors. Film permittivities above 100 and leakage current densities below 110-7 A/cm2 were achieved with several of the materials.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.