Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Statistical and Information-Theoretic Methods for Data-Analysis

In this Thesis, we develop theory and methods for computational data analysis. The problems in data analysis are approached from three perspectives: statistical learning theory, the Bayesian framework, and the information-theoretic minimum description length (MDL) principle. Contributions in statistical learning theory address the possibility of generalization to unseen cases, and regression analysis with partially observed data with an application to mobile device positioning. In the second part of the Thesis, we discuss so called Bayesian network classifiers, and show that they are closely related to logistic regression models. In the final part, we apply the MDL principle to tracing the history of old manuscripts, and to noise reduction in digital signals.

Geant4 Hadronic Cascade Models and CMS Data Analysis : Computational Challenges in the LHC era

This work belongs to the field of computational high-energy physics (HEP). The key methods used in this thesis work to meet the challenges raised by the Large Hadron Collider (LHC) era experiments are object-orientation with software engineering, Monte Carlo simulation, the computer technology of clusters, and artificial neural networks. The first aspect discussed is the development of hadronic cascade models, used for the accurate simulation of medium-energy hadron-nucleus reactions, up to 10 GeV. These models are typically needed in hadronic calorimeter studies and in the estimation of radiation backgrounds. Various applications outside HEP include the medical field (such as hadron treatment simulations), space science (satellite shielding), and nuclear physics (spallation studies). Validation results are presented for several significant improvements released in Geant4 simulation tool, and the significance of the new models for computing in the Large Hadron Collider era is estimated. In particular, we estimate the ability of the Bertini cascade to simulate Compact Muon Solenoid (CMS) hadron calorimeter HCAL. LHC test beam activity has a tightly coupled cycle of simulation-to-data analysis. Typically, a Geant4 computer experiment is used to understand test beam measurements. Thus an another aspect of this thesis is a description of studies related to developing new CMS H2 test beam data analysis tools and performing data analysis on the basis of CMS Monte Carlo events. These events have been simulated in detail using Geant4 physics models, full CMS detector description, and event reconstruction. Using the ROOT data analysis framework we have developed an offline ANN-based approach to tag b-jets associated with heavy neutral Higgs particles, and we show that this kind of NN methodology can be successfully used to separate the Higgs signal from the background in the CMS experiment.

Accelerator Mass Spectrometry and Bayesian Data Analysis

Accelerator mass spectrometry (AMS) is an ultrasensitive technique for measuring the concentration of a single isotope. The electric and magnetic fields of an electrostatic accelerator system are used to filter out other isotopes from the ion beam. The high velocity means that molecules can be destroyed and removed from the measurement background. As a result, concentrations down to one atom in 10^16 atoms are measurable. This thesis describes the construction of the new AMS system in the Accelerator Laboratory of the University of Helsinki. The system is described in detail along with the relevant ion optics. System performance and some of the 14C measurements done with the system are described. In a second part of the thesis, a novel statistical model for the analysis of AMS data is presented. Bayesian methods are used in order to make the best use of the available information. In the new model, instrumental drift is modelled with a continuous first-order autoregressive process. This enables rigorous normalization to standards measured at different times. The Poisson statistical nature of a 14C measurement is also taken into account properly, so that uncertainty estimates are much more stable. It is shown that, overall, the new model improves both the accuracy and the precision of AMS measurements. In particular, the results can be improved for samples with very low 14C concentrations or measured only a few times.

Residual noise covariance for Planck low-resolution data analysis

Aims: Develop and validate tools to estimate residual noise covariance in Planck frequency maps. Quantify signal error effects and compare different techniques to produce low-resolution maps. Methods: We derive analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of map-making approaches. We test these analytical predictions using Monte Carlo simulations and their impact on angular power spectrum estimation. We use simulations to quantify the level of signal errors incurred in different resolution downgrading schemes considered in this work. Results: We find an excellent agreement between the optimal residual noise covariance matrices and Monte Carlo noise maps. For destriping map-makers, the extent of agreement is dictated by the knee frequency of the correlated noise component and the chosen baseline offset length. The significance of signal striping is shown to be insignificant when properly dealt with. In map resolution downgrading, we find that a carefully selected window function is required to reduce aliasing to the sub-percent level at multipoles, ell > 2Nside, where Nside is the HEALPix resolution parameter. We show that sufficient characterization of the residual noise is unavoidable if one is to draw reliable contraints on large scale anisotropy. Conclusions: We have described how to compute the low-resolution maps, with a controlled sky signal level, and a reliable estimate of covariance of the residual noise. We have also presented a method to smooth the residual noise covariance matrices to describe the noise correlations in smoothed, bandwidth limited maps.

Teacher Education, School Effectiveness and Improvement : A Study of Academic and Professional Qualification on Teachers' Job Effectiveness in Nigerian Secondary Schools

This academic work begins with a compact presentation of the general background to the study, which also includes an autobiography for the interest in this research. The presentation provides readers who know little of the topic of this research and of the structure of the educational system as well as of the value given to education in Nigeria. It further concentrates on the dynamic interplay of the effect of academic and professional qualification and teachers' job effectiveness in secondary schools in Nigeria in particular, and in Africa in general. The aim of this study is to produce a systematic analysis and rich theoretical and empirical description of teachers' teaching competencies. The theoretical part comprises a comprehensive literature review that focuses on research conducted in the areas of academic and professional qualification and teachers' job effectiveness, teaching competencies, and the role of teacher education with particular emphasis on school effectiveness and improvement. This research benefits greatly from the functionalist conception of education, which is built upon two emphases: the application of the scientific method to the objective social world, and the use of an analogy between the individual 'organism' and 'society'. To this end, it offers us an opportunity to define terms systematically and to view problems as always being interrelated with other components of society. The empirical part involves describing and interpreting what educational objectives can be achieved with the help of teachers' teaching competencies in close connection to educational planning, teacher training and development, and achieving them without waste. The data used in this study were collected between 2002 and 2003 from teachers, principals, supervisors of education from the Ministry of Education and Post Primary Schools Board in the Rivers State of Nigeria (N=300). The data were collected from interviews, documents, observation, and questionnaires and were analyzed using both qualitative and quantitative methods to strengthen the validity of the findings. The data collected were analyzed to answer the specific research questions and hypotheses posited in this study. The data analysis involved the use of multiple statistical procedures: Percentages Mean Point Value, T-test of Significance, One-Way Analysis of Variance (ANOVA), and Cross Tabulation. The results obtained from the data analysis show that teachers require professional knowledge and professional teaching skills, as well as a broad base of general knowledge (e.g., morality, service, cultural capital, institutional survey). Above all, in order to carry out instructional processes effectively, teachers should be both academically and professionally trained. This study revealed that teachers are not however expected to have an extraordinary memory, but rather looked upon as persons capable of thinking in the right direction. This study may provide a solution to the problem of teacher education and school effectiveness in Nigeria. For this reason, I offer this treatise to anyone seriously committed in improving schools in developing countries in general and in Nigeria in particular to improve the lives of all its citizens. In particular, I write this to encourage educational planners, education policy makers, curriculum developers, principals, teachers, and students of education interested in empirical information and methods to conceptualize the issue this study has raised and to provide them with useful suggestions to help them improve secondary schooling in Nigeria. Though, multiple audiences exist for any text. For this reason, I trust that the academic community will find this piece of work a useful addition to the existing literature on school effectiveness and school improvement. Through integrating concepts from a number of disciplines, I aim to describe as holistic a representation as space could allow of the components of school effectiveness and quality improvement. A new perspective on teachers' professional competencies, which not only take into consideration the unique characteristics of the variables used in this study, but also recommend their environmental and cultural derivation. In addition, researchers should focus their attention on the ways in which both professional and non-professional teachers construct and apply their methodological competencies, such as their grouping procedures and behaviors to the schooling of students. Keywords: Professional Training, Academic Training, Professionally Qualified, Academically Qualified, Professional Qualification, Academic Qualification, Job Effectiveness, Job Efficiency, Educational Planning, Teacher Training and Development, Nigeria.

Chemical mass closure and source-specific composition of atmospheric particles

It has been known for decades that particles can cause adverse health effects as they are deposited within the respiratory system. Atmospheric aerosol particles influence climate by scattering solar radiation but aerosol particles act also as the nuclei around which cloud droplets form. The principal objectives of this thesis were to investigate the chemical composition and the sources of fine particles in different environments (traffic, urban background, remote) as well as during some specific air pollution situations. Quantifying the climate and health effects of atmospheric aerosols is not possible without detailed information of the aerosol chemical composition. Aerosol measurements were carried out at nine sites in six countries (Finland, Germany, Czech, Netherlands, Greece and Italy). Several different instruments were used in order to measure both the particulate matter (PM) mass and its chemical composition. In the off-line measurements the samples were collected first on a substrate or filter and gravimetric and chemical analysis were conducted in the laboratory. In the on-line measurements the sampling and analysis were either a combined procedure or performed successively within the same instrument. Results from the impactor samples were analyzed by the statistical methods. This thesis comprises also a work where a method for the determination carbonaceous matter size distribution by using a multistage impactor was developed. It was found that the chemistry of PM has usually strong spatial, temporal and size-dependent variability. In the Finnish sites most of the fine PM consisted of organic matter. However, in Greece sulfate dominated the fine PM and in Italy nitrate made the largest contribution to the fine PM. Regarding the size-dependent chemical composition, organic components were likely to be enriched in smaller particles than inorganic ions. Data analysis showed that organic carbon (OC) had four major sources in Helsinki. Secondary production was the major source in Helsinki during spring, summer and fall, whereas in winter biomass combustion dominated OC. The significant impact of biomass combustion on OC concentrations was also observed in the measurements performed in Central Europe. In this thesis aerosol samples were collected mainly by the conventional filter and impactor methods which suffered from the long integration time. However, by filter and impactor measurements chemical mass closure was achieved accurately, and a simple filter sampling was found to be useful in order to explain the sources of PM on the seasonal basis. The online instruments gave additional information related to the temporal variations of the sources and the atmospheric mixing conditions.

FGFR1 regulated gene-expression, cell proliferation and differentiation in the developing midbrain and hindbrain

The neuroectodermal tissue close to the midbrain hindbrain boundary (MHB) is an important secondary organizer in the developing neural tube. This so-called isthmic organizer (IsO) regulates cellular survival, patterning and proliferation in the midbrain (Mb) and rhombomere 1 (R1) of the hindbrain. Signaling molecules of the IsO, such as fibroblast growth factor 8 (FGF8) and WNT1 are expressed in distinct bands of cells around the MHB. It has been previously shown that FGF-receptor 1 (FGFR1) is required for the normal development of this brain region in the mouse embryo. In the present study, we have compared the gene expression profiles of wild-type and Fgfr1 mutant embryos. We show that the loss of Fgfr1 results in the downregulation of several genes expressed close to the MHB and in the disappearance of gene expression gradients in the midbrain and R1. Our microarray screen identified several previously uncharacterized genes which may participate in the development of midbrain R1 region. Our results also show altered neurogenesis in the midbrain and R1 of the Fgfr1 mutants. Interestingly, the neuronal progenitors in midbrain and R1 show different responses to the loss of signaling through FGFR1. As Wnt1 expression at the MHB region requires the FGF signaling pathway, WNT target genes, including Drapc1, were also identified in our screen. The microarray data analysis also suggested that the cells next to the midbrain hindbrain boundary express distinct cell cycle regulators. We showed that the cells close to the border appeared to have unique features. These cells proliferate less rapidly than the surrounding cells. Unlike the cells further away from the boundary, these cells express Fgfr1 but not the other FGF receptors. The slowly proliferating boundary cells are necessary for development of the characteristic isthmic constriction. They may also contribute to compartmentalization of this brain region.

Data fusion and matching by maximizing statistical dependencies

The core aim of machine learning is to make a computer program learn from the experience. Learning from data is usually defined as a task of learning regularities or patterns in data in order to extract useful information, or to learn the underlying concept. An important sub-field of machine learning is called multi-view learning where the task is to learn from multiple data sets or views describing the same underlying concept. A typical example of such scenario would be to study a biological concept using several biological measurements like gene expression, protein expression and metabolic profiles, or to classify web pages based on their content and the contents of their hyperlinks. In this thesis, novel problem formulations and methods for multi-view learning are presented. The contributions include a linear data fusion approach during exploratory data analysis, a new measure to evaluate different kinds of representations for textual data, and an extension of multi-view learning for novel scenarios where the correspondence of samples in the different views or data sets is not known in advance. In order to infer the one-to-one correspondence of samples between two views, a novel concept of multi-view matching is proposed. The matching algorithm is completely data-driven and is demonstrated in several applications such as matching of metabolites between humans and mice, and matching of sentences between documents in two languages.

Consumption in Rural Bangladesh : Households, Lifestyles, and Identities

Bangladesh, often better known to the outside world as a country of natural calamities, is one of the most densely populated countries in the world. Despite rapid urbanization, more than 75% of the people still live in rural areas. The density of the rural population is also one of the highest in the world. Being a poor and low-income country, its main challenge is to eradicate poverty through increasing equitable income. Since its independence in 1971, Bangladesh has experienced many ups and downs, but over the past three decades, its gross domestic product (GDP) has grown at an impressive rate. Consequently, the country s economy is developing and the country has outperformed many low-income countries in terms of several social indicators. Bangladesh has achieved the Millennium Development Goal (MDG) of eliminating gender disparity in primary and secondary school enrollment. A sharp decline in child and infant mortality rates, increased per capita income, and improved food security have placed Bangladesh on the track to achieving in the near future the status of a middle-income country. All these developments have influenced the consumption pattern of the country. This study explores the consumption scenario of rural Bangladesh, its changing consumption patterns, the relationship between technology and consumption in rural Bangladesh, cultural consumption in rural Bangladesh, and the myriad reasons why consumers nevertheless feel compelled to consume chemically treated foods. Data were collected in two phases in the summers of 2006 and 2008. In 2006, the empirical data were collected from the following three sources: interviews with consumers, producers/sellers, and doctors and pharmacists; observations of sellers/producers; and reviews of articles published in the national English and Bengali (the national language of Bangladesh) daily newspapers. A total of 110 consumers, 25 sellers/producers, 7 doctors, and 7 pharmacists were interviewed and observed. In 2008, data were collected through semi-structured in-depth qualitative interviews, ethnography, and unstructured conversations substantiated by secondary sources and photographs; the total number of persons interviewed was 22. -- Data were also collected on the consumption of food, clothing, housing, education, medical facilities, marriage and dowry, the division of labor, household decision making, different festivals such as Eid (for Muslims), the Bengali New Year, and Durga puja (for Hindus), and leisure. Qualitative methods were applied to the data analysis and were supported by secondary quantitative data. The findings of this study suggest that the consumption patterns of rural Bangladeshis are changing over time along with economic and social development, and that technology has rendered aspects of daily life more convenient. This study identified the perceptions and experiences of rural people regarding technologies in use and explored how culture is associated with consumption. This study identified the reasons behind the use of hazardous chemicals (e.g. calcium carbide, sodium cyclamate, cyanide and formalin, etc.) in foods as well as the extent to which food producers/sellers used such chemicals. In addition, this study assessed consumer perceptions of and attitudes toward these contaminated food items and explored how adulterated foods and food stuffs affect consumer health. This study also showed that consumers were aware that various foods and food stuffs contained hazardous chemicals, and that these adulterated foods and food stuffs were harmful to their health.

On formation, growth and concentrations of air ions

Aerosol particles have effect on climate, visibility, air quality and human health. However, the strength of which aerosol particles affect our everyday life is not well described or entirely understood. Therefore, investigations of different processes and phenomena including e.g. primary particle sources, initial steps of secondary particle formation and growth, significance of charged particles in particle formation, as well as redistribution mechanisms in the atmosphere are required. In this work sources, sinks and concentrations of air ions (charged molecules, cluster and particles) were investigated directly by measuring air molecule ionising components (i.e. radon activity concentrations and external radiation dose rates) and charged particle size distributions, as well as based on literature review. The obtained results gave comprehensive and valuable picture of the spatial and temporal variation of the air ion sources, sinks and concentrations to use as input parameters in local and global scale climate models. Newly developed air ion spectrometers (Airel Ltd.) offered a possibility to investigate atmospheric (charged) particle formation and growth at sub-3 nm sizes. Therefore, new visual classification schemes for charged particle formation events were developed, and a newly developed particle growth rate method was tested with over one year dataset. These data analysis methods have been widely utilised by other researchers since introducing them. This thesis resulted interesting characteristics of atmospheric particle formation and growth: e.g. particle growth may sometimes be suppressed before detection limit (~ 3 nm) of traditional aerosol instruments, particle formation may take place during daytime as well as in the evening, growth rates of sub-3 nm particles were quite constant throughout the year while growth rates of larger particles (3-20 nm in diameter) were higher during summer compared to winter. These observations were thought to be a consequence of availability of condensing vapours. The observations of this thesis offered new understanding of the particle formation in the atmosphere. However, the role of ions in particle formation, which is not well understood with current knowledge, requires further research in future.