Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

CBS: an open platform that integrates predictive methods and epigenetics information to characterize conserved regulatory features in multiple Drosophila genomes.

Background: Information about the composition of regulatory regions is of great value for designing experiments to functionally characterize gene expression. The multiplicity of available applications to predict transcription factor binding sites in a particular locus contrasts with the substantial computational expertise that is demanded to manipulate them, which may constitute a potential barrier for the experimental community. Results: CBS (Conserved regulatory Binding Sites, http://compfly.bio.ub.es/CBS) is a public platform of evolutionarily conserved binding sites and enhancers predicted in multiple Drosophila genomes that is furnished with published chromatin signatures associated to transcriptionally active regions and other experimental sources of information. The rapid access to this novel body of knowledge through a user-friendly web interface enables non-expert users to identify the binding sequences available for any particular gene, transcription factor, or genome region. Conclusions: The CBS platform is a powerful resource that provides tools for data mining individual sequences and groups of co-expressed genes with epigenomics information to conduct regulatory screenings in Drosophila.

Varaosien tunnistuspalvelut sähköisessä liiketoimintaympäristössä

Työn tarkoituksena on ollut luoda varaosien tunnistukseen kehitysmalli, jota voidaan käyttää pohjana palveluiden kehityksessä. Saatavilla olevia tunnistusmenetelmiä parantamalla on mahdollista kehittää liiketoimintaprosesseja. Yrityksiin kohdistuu yhä enemmän vaatimuksia, jotka täyttääkseen on kyettävä luomaan kustomoitavissa olevia innovatiivisia palvelukokonaisuuksia. Yritysten tulisi kerätä yhteen kaikki saatavilla oleva tieto asiakkaiden hankkimista tuotteista ja palveluista tarjoten näiden kautta toimintamahdollisuuksia lähemmäs asiakasrajapintaan. Sähköisten tunnistuspalveluiden kehitys ja integrointi yhdeksi kokonaisuudeksi on keskeisessä asemassa yrityksen palvelutarjontaa kehitettäessä. Palveluihin voidaan antaa pääsy SOA (Service Oriented Architecture) -malliin perustuvien portaaleiden kautta. Uudet varaosien tunnistusmenetelmät tulee liittää web-palveluina portaaliin integrointimenetelmiä käyttäen. Varaosien tunnistuksen kannalta on keskeistä linkittää taustajärjestelmien tieto tukemaan tunnistusprosessia tuotteiden koko elinkaaren ajan. Case tapauksessa integroitiin varaosien tunnistuspalveluina IMB WebSphere Portal:n PDM (Product Data Management) -järjestelmän varaosadokumentaatio. Lisäksi luotiin varaosien tunnistusjärjestelmän vaatima datamalli, joka mahdollistaa PDMjärjestelmässä olevan as-built – varaosarakenteen hyväksikäytön portaaliin liitettävän graafisen varaosien tunnistusjärjestelmän kanssa. Tulevaisuuden kehitysmahdollisuuksina nähdään huoltojärjestelmien tiedon integrointi osaksi tunnistusmenetelmiä parantavaa kokonaisuutta, huoltotarpeen automaattinen seuranta ja analysointi, RFID:n käyttömahdollisuudet, mobiili-sovellusten kehitys, varaosien tunnistukseen perustuvien liiketoimintaprosessien automatisointi sekä jatkuva tiedonjaon kehittäminen.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

Reuse of information for selection of methods and operating conditions for drying of pharmaceutical materials

Drying is a major step in the manufacturing process in pharmaceutical industries, and the selection of dryer and operating conditions are sometimes a bottleneck. In spite of difficulties, the bottlenecks are taken care of with utmost care due to good manufacturing practices (GMP) and industries' image in the global market. The purpose of this work is to research the use of existing knowledge for the selection of dryer and its operating conditions for drying of pharmaceutical materials with the help of methods like case-based reasoning and decision trees to reduce time and expenditure for research. The work consisted of two major parts as follows: Literature survey on the theories of spray dying, case-based reasoning and decision trees; working part includes data acquisition and testing of the models based on existing and upgraded data. Testing resulted in a combination of two models, case-based reasoning and decision trees, leading to more specific results when compared to conventional methods.

Implementing Agile Software Development Methods in a Small Organization

Currently there is a vogue for Agile Software Development methods and many software development organizations have already implemented or they are planning to implement agile methods. Objective of this thesis is to define how agile software development methods are implemented in a small organization. Agile methods covered in this thesis are Scrum and XP. From both methods the key practices are analysed and compared to waterfall method. This thesis also defines implementation strategy and actions how agile methods are implemented in a small organization. In practice organization must prepare well and all needed meters are defined before the implementation starts. In this work three different sample projects are introduced where agile methods were implemented. Experiences from these projects were encouraging although sample set of projects were too small to get trustworthy results.

Probabilistic age classification with Bayesian networks: a study on the ossification status of the medial clavicular epiphysis

In the past few decades, the rise of criminal, civil and asylum cases involving young people lacking valid identification documents has generated an increase in the demand of age estimation. The chronological age or the probability that an individual is older or younger than a given age threshold are generally estimated by means of some statistical methods based on observations performed on specific physical attributes. Among these statistical methods, those developed in the Bayesian framework allow users to provide coherent and transparent assignments which fulfill forensic and medico-legal purposes. The application of the Bayesian approach is facilitated by using probabilistic graphical tools, such as Bayesian networks. The aim of this work is to test the performances of the Bayesian network for age estimation recently presented in scientific literature in classifying individuals as older or younger than 18 years of age. For these exploratory analyses, a sample related to the ossification status of the medial clavicular epiphysis available in scientific literature was used. Results obtained in the classification are promising: in the criminal context, the Bayesian network achieved, on the average, a rate of correct classifications of approximatively 97%, whilst in the civil context, the rate is, on the average, close to the 88%. These results encourage the continuation of the development and the testing of the method in order to support its practical application in casework.

Use of Statistical Methods in Demand Planning

In a very volatile industry of high technology it is of utmost importance to accurately forecast customers’ demand. However, statistical forecasting of sales, especially in heavily competitive electronics product business, has always been a challenging task due to very high variation in demand and very short product life cycles of products. The purpose of this thesis is to validate if statistical methods can be applied to forecasting sales of short life cycle electronics products and provide a feasible framework for implementing statistical forecasting in the environment of the case company. Two different approaches have been developed for forecasting on short and medium term and long term horizons. Both models are based on decomposition models, but differ in interpretation of the model residuals. For long term horizons residuals are assumed to represent white noise, whereas for short and medium term forecasting horizon residuals are modeled using statistical forecasting methods. Implementation of both approaches is performed in Matlab. Modeling results have shown that different markets exhibit different demand patterns and therefore different analytical approaches are appropriate for modeling demand in these markets. Moreover, the outcomes of modeling imply that statistical forecasting can not be handled separately from judgmental forecasting, but should be perceived only as a basis for judgmental forecasting activities. Based on modeling results recommendations for further deployment of statistical methods in sales forecasting of the case company are developed.

A comparison of three established age estimation methods on an adult Spanish sample.

Most current methods for adult skeletal age-at-death estimation are based on American samples comprising individuals of European and African ancestry. Our limited understanding of population variability hampers our efforts to apply these techniques to various skeletal populations around the world, especially in global forensic contexts. Further, documented skeletal samples are rare, limiting our ability to test our techniques. The objective of this paper is to test three pelvic macroscopic methods (1-Suchey-Brooks; 2- Lovejoy; 3- Buckberry and Chamberlain) on a documented modern Spanish sample. These methods were selected because they are popular among Spanish anthropologists and because they never have been tested in a Spanish sample. The study sample consists of 80 individuals (55 ♂ and 25 ♀) of known sex and age from the Valladolid collection. Results indicate that in all three methods, levels of bias and inaccuracy increase with age. The Lovejoy method performs poorly (27%) compared with Suchey-Brooks (71%) and Buckberry and Chamberlain (86%). However, the levels of correlation between phases and chronological ages are low and comparable in the three methods (< 0.395). The apparent accuracy of the Suchey-Brooks and Buckberry and Chamberlain methods is largely based on the broad width of the methods" estimated intervals. This study suggests that before systematic application of these three methodologies in Spanish populations, further statistical modeling and research into the co-variance of chronological age with morphological change is necessary. Future methods should be developed specific to various world populations, and should allow for both precision and flexibility in age estimation.

A test of Suchey-Brooks (pubic symphysis) and Buckberry-Chamberlain (auricular surface) methods on an identified Spanish sample: paleodemographic implications

Forensic Anthropology and Bioarchaeology studies depend critically on the accuracy and reliability of age-estimation techniques. In this study we have evaluated two age-estimation methods for adults based on the pubic symphysis (Suchey-Brooks) and the auricular surface (Buckberry-Chamberlain) in a current sample of 139 individuals (67 women and 72 men) from Madrid in order to verify the accuracy of both methods applied to a sample of innominate bones from the central Iberian Peninsula. Based on the overall results of this study, the Buckberry-Chamberlain method seems to be the method that provides better estimates in terms of accuracy (percentage of hits) and absolute difference to the chronological age taking into account the total sample. The percentage of hits and mean absolute difference of the Buckberry-Chamberlain and Suchey-Brooks methods are 97.3% and 11.24 years, and 85.7% and 14.38 years, respectively. However, this apparently greater applicability of the Buckberry-Chamberlain method is mainly due to the broad age ranges provided. Results indicated that Suchey-Brooks method is more appropriate for populations with a majority of young individuals, whereas Buckberry-Chamberlain method is recommended for populations with a higher percentage of individuals in the range 60-70 years. These different age estimation methodologies significantly influence the resulting demographic profile, consequently affecting the biological characteristics reconstruction of the samples in which they are applied.

Fast and economic immobilization methods described for non-commercial Pseudomonas lipases

There is an increasing interest to seek new enzyme preparations for the development of new products derived from bioprocesses to obtain alternative bio-based materials. In this context, four non-commercial lipases from Pseudomonas species were prepared, immobilized on different low-cost supports, and examined for potential biotechnological applications. Results: To reduce costs of eventual scaling-up, the new lipases were obtained directly from crude cell extracts or from growth culture supernatants, and immobilized by simple adsorption on Accurel EP100, Accurel MP1000 and Celite (R) 545. The enzymes evaluated were LipA and LipC from Pseudomonas sp. 42A2, a thermostable mutant of LipC, and LipI. 3 from Pseudomonas CR611, which were produced in either homologous or heterologous hosts. Best immobilization results were obtained on Accurel EP100 for LipA and on Accurel MP1000 for LipC and its thermostable variant. Lip I. 3, requiring a refolding step, was poorly immobilized on all supports tested ( best results for Accurel MP1000). To test the behavior of immobilized lipases, they were assayed in triolein transesterification, where the best results were observed for lipases immobilized on Accurel MP1000. Conclusions: The suggested protocol does not require protein purification and uses crude enzymes immobilized by a fast adsorption technique on low-cost supports, which makes the method suitable for an eventual scaling up aimed at biotechnological applications. Therefore, a fast, simple and economic method for lipase preparation and immobilization has been set up. The low price of the supports tested and the simplicity of the procedure, skipping the tedious and expensive purification steps, will contribute to cost reduction in biotechnological lipase-catalyzed processes.