Predictors and Correlates of Substance Use among Young Men. The Longitudinal “From a Boy to a Man” Birth Cohort Study.

Substance use is one of our most important public health problems. Studying risk factors in a longitudinal study setting helps to identify subgroups of young people at greater risk for substance-use-related problems, and to facilitate targeted prevention efforts. The aim of this thesis was to study childhood predictors and correlates of substance-use-related outcomes among young men in a longitudinal, nationwide birth cohort study. The study population included 10% of all Finnish-speaking boys born in Finland in 1981 (n=2946, 97% of the target population). In 1989, at age eight, valid measures of psychiatric symptoms (Rutter questionnaires and Children’s Depression Inventory) were obtained from parents, teachers and the boys themselves. In 1999, at age 18, boys were reached at their obligatory military call-up (n=2348, 80% of the boys attending the study in 1989). Self-reports of substance use, psychopathology, adaptive functioning (Young Adult Self-Report), and mental health service use were obtained through questionnaires. Information about psychiatric diagnoses from the Military Register (age 18-23 years) and information about offending from the National Police Register (age 16-20 years) were collected in early adulthood (92% of the 1989sample). Boys with childhood conduct, hyperactive, and comorbid conduct-emotional problems had elevated rates of substance use and substance-use-related crime in early adulthood. Depressive symptoms predicted daily smoking, especially among boys of low-educated fathers. Emotional problems predicted lower occurrence of drunkenness-related alcohol use and smoking. Teacher reports on boys’ problem behaviour had the best predictive power for later substance use. At age 18, frequent drunkenness associated with delinquency, smoking and illicit drug use, and having friends. Occasional drunkenness associated with better psychosocial functioning in general compared to boys with frequent drunkenness or without drunkenness-related alcohol use. Illicit drug use without drug offending was not predicted by childhood psychiatric symptoms, but 22% of boys with illicit drug use had a psychiatric diagnosis in early adulthood. Drug offenders, in turn, had psychiatric problems both in childhood and in adulthood. Psychiatric disorders were common among young men with substance-use-related crime. Recidivist crime associated strongly with having a substance use disorder diagnosis according to the Military Register. At age 18, frequent drunkenness was common among boys entering mental health services, but entering substance use treatment was non-existent. According to the findings of this thesis, substance-use-related outcomes accumulate in boys having psychiatric problems both in childhood and in early adulthood. Targeted early interventions in school health care systems, particularly for boys with childhood hyperactive, conduct, and comorbid conduct-emotional problems are recommended. Psychiatric problems and risky behaviours, such as delinquency should always be assessed alongside substance use. Specialized and multidisciplinary care are required for young men who have multiple or complex needs, for instance, for young men with drug offending and recidivist crime. Integrating a substance use treatment perspective with other services where young men are encountered is emphasized.

CFD simulation of complex phenomena containing suspensions and flow throughporous media

There is an increasing reliance on computers to solve complex engineering problems. This is because computers, in addition to supporting the development and implementation of adequate and clear models, can especially minimize the financial support required. The ability of computers to perform complex calculations at high speed has enabled the creation of highly complex systems to model real-world phenomena. The complexity of the fluid dynamics problem makes it difficult or impossible to solve equations of an object in a flow exactly. Approximate solutions can be obtained by construction and measurement of prototypes placed in a flow, or by use of a numerical simulation. Since usage of prototypes can be prohibitively time-consuming and expensive, many have turned to simulations to provide insight during the engineering process. In this case the simulation setup and parameters can be altered much more easily than one could with a real-world experiment. The objective of this research work is to develop numerical models for different suspensions (fiber suspensions, blood flow through microvessels and branching geometries, and magnetic fluids), and also fluid flow through porous media. The models will have merit as a scientific tool and will also have practical application in industries. Most of the numerical simulations were done by the commercial software, Fluent, and user defined functions were added to apply a multiscale method and magnetic field. The results from simulation of fiber suspension can elucidate the physics behind the break up of a fiber floc, opening the possibility for developing a meaningful numerical model of the fiber flow. The simulation of blood movement from an arteriole through a venule via a capillary showed that the model based on VOF can successfully predict the deformation and flow of RBCs in an arteriole. Furthermore, the result corresponds to the experimental observation illustrates that the RBC is deformed during the movement. The concluding remarks presented, provide a correct methodology and a mathematical and numerical framework for the simulation of blood flows in branching. Analysis of ferrofluids simulations indicate that the magnetic Soret effect can be even higher than the conventional one and its strength depends on the strength of magnetic field, confirmed experimentally by Völker and Odenbach. It was also shown that when a magnetic field is perpendicular to the temperature gradient, there will be additional increase in the heat transfer compared to the cases where the magnetic field is parallel to the temperature gradient. In addition, the statistical evaluation (Taguchi technique) on magnetic fluids showed that the temperature and initial concentration of the magnetic phase exert the maximum and minimum contribution to the thermodiffusion, respectively. In the simulation of flow through porous media, dimensionless pressure drop was studied at different Reynolds numbers, based on pore permeability and interstitial fluid velocity. The obtained results agreed well with the correlation of Macdonald et al. (1979) for the range of actual flow Reynolds studied. Furthermore, calculated results for the dispersion coefficients in the cylinder geometry were found to be in agreement with those of Seymour and Callaghan.

Magnetic sputtering of complex oxides

HTSC materials are relevant in modern microelectronics, because of their transformation from the normal state to the superconducting. That is why the idea of producing HTSC in industrial amounts is actual nowadays. To decrease cost of their production it is important to use magnetron sputtering systems which give the best results for essential parameters. Modeling is the simplest and the fastest way to determine optimum sputtering condition. This thesis concentrates on determination the phases of the whole sputtering process and to find out basic factors of each phase using the modeling. It was find out, that the main factors which influence on the mode of occurrence of the initial stages are the current density of the magnetron discharge and the pressure of sputtering gas. With the modeling also velocity dependences were obtained for YBCO and SmFeAsO. These were compared and difference between them was examined. To support represented model comparison was made with experimental results. This showed that the model gives good results, very similar to the experimental ones. The results of this work were published in annual conference of the finnish physical society.

Assessing the Environmental Value of Water Treatment Solutions in the Mining Industry

Environmental accountability has become a major source of competitive advantage for industrial companies, because customers consider it as relevant buying criterion. However, in order to leverage their environmental responsibility, industrial suppliers have to be able to demonstrate the environmental value of their products and services, which is also the aim of Kemira, a global water chemistry company considered in this study. The aim of this thesis is to develop a tool which Kemira can use to assess the environmental value of their solutions for the customer companies in mining industry. This study answers to questions on what kinds of methods to assess environmental impacts exist, and what kind of tool could be used to assess the environmental value of Kemira’s water treatment solutions. The environmental impacts of mining activities vary greatly between different mines. Generally the major impacts include the water related issues and wastes. Energy consumption is also a significant environmental aspect. Water related issues include water consumption and impacts in water quality. There are several methods to assess environmental impacts, for example life cycle assessment, eco-efficiency tools, footprint calculations and process simulation. In addition the corresponding financial value may be estimated utilizing monetary assessment methods. Some of the industrial companies considered in the analysis of industry best practices use environmental and sustainability assessments. Based on the theoretical research and conducted interviews, an Excel based tool utilizing reference data on previous customer cases and customer specific test results was considered to be most suitable to assess the environmental value of Kemira’s solutions. The tool can be used to demonstrate the functionality of Kemira’s solutions in customers’ processes, their impacts in other process parameters and their environmental and financial aspects. In the future, the tool may be applied to fit also Kemira’s other segments, not only mining industry.

The demand for global student talent: Capitalizing on the value of university-industry collaboration

The university sector in Europe has invested money and effort into the internationalization of higher education. The benefits of internationalizing higher education are fuelled by changing global values, choices and practices. However, arguments that serve the internationalization of higher education tend to stress either local organizational or individual interests; seldom do they emphasize the societal benefits. This dissertation investigates how collaboration between university and industry facilitates a shift in thinking about attracting and retaining global student talent, in terms of co-creating solutions to benefit the development of our knowledge society. The macro-structures of the higher education sector have the tendency to overemphasize quantitative goals to improve performance verifiability. Recruitment of international student talent is thereby turned into a mere supply issue. A mind shift is needed to rethink the efficacy of the higher education sector with regard to retaining foreign student talent as a means of contributing to society’s stock of knowledge and through that to economic growth. This thesis argues that academic as well as industrial understanding of the value of university-industry collaboration might then move beyond the current narrow expectations and perceptions of the university’s contribution to society’s innovation systems. This mind shift is needed to encourage and generate creative opportunities for university-industry partnerships to develop sustainable solutions for successful recruitment of foreign student talent, and thereby to maximize the wealth-creating potential of global student talent recruitment. This thesis demonstrates through the use of interpretive and participatory methods, how it is possible to reveal new and important insights into university-industry partnering for enhancing attraction and retention of global student talent. It accomplishes this by expressly pointing out the central role of human collaborative experiencing and learning. The narratives presented take the reader into a Finnish and Dutch universityindustry partnering environment to reflect on the relationship between the local universities of technology and their operational surroundings, a relationship that is set in a context of local and global entanglements and challenges.

Libyan breast cancer: Health services and biology. Diagnosis delay and prognostic value of DNA pploidy, S-phase fraction, and Ki-67 and Bcl-2 immunohistochemistry

The aim of this study was to investigate the diagnosis delay and its impact on the stage of disease. The study also evaluated a nuclear DNA content, immunohistochemical expression of Ki-67 and bcl-2, and the correlation of these biological features with the clinicopathological features and patient outcome. 200 Libyan women, diagnosed during 2008–2009 were interviewed about the period from the first symptoms to the final histological diagnosis of breast cancer. Also retrospective preclinical and clinical data were collected from medical records on a form (questionnaire) in association with the interview. Tumor material of the patients was collected and nuclear DNA content analysed using DNA image cytometry. The expression of Ki-67 and bcl-2 were assessed using immunohistochemistry (IHC). The studies described in this thesis show that the median of diagnosis time for women with breast cancer was 7.5 months and 56% of patients were diagnosed within a period longer than 6 months. Inappropriate reassurance that the lump was benign was an important reason for prolongation of the diagnosis time. Diagnosis delay was also associated with initial breast symptom(s) that did not include a lump, old age, illiteracy, and history of benign fibrocystic disease. The patients who showed diagnosis delay had bigger tumour size (p<0.0001), positive lymph nodes (p<0.0001), and high incidence of late clinical stages (p<0.0001). Biologically, 82.7% of tumors were aneuploid and 17.3% were diploid. The median SPF of tumors was 11% while the median positivity of Ki-67 was 27.5%. High Ki-67 expression was found in 76% of patients, and high SPF values in 56% of patients. Positive bcl-2 expression was found in 62.4% of tumors. 72.2% of the bcl-2 positive samples were ER-positive. Patients who had tumor with DNA aneuploidy, high proliferative activity and negative bcl-2 expression were associated with a high grade of malignancy and short survival. The SPF value is useful cell proliferation marker in assessing prognosis, and the decision cut point of 11% for SPF in the Libyan material was clearly significant (p<0.0001). Bcl-2 is a powerful prognosticator and an independent predictor of breast cancer outcome in the Libyan material (p<0.0001). Libyan breast cancer was investigated in these studies from two different aspects: health services and biology. The results show that diagnosis delay is a very serious problem in Libya and is associated with complex interactions between many factors leading to advanced stages, and potentially to high mortality. Cytometric DNA variables, proliferative markers (Ki-67 and SPF), and oncoprotein bcl-2 negativity reflect the aggressive behavior of Libyan breast cancer and could be used with traditional factors to predict the outcome of individual patients, and to select appropriate therapy.

Value of Information Logistics Integration in Digital Business Ecosystem

Digital business ecosystems (DBE) are becoming an increasingly popular concept for modelling and building distributed systems in heterogeneous, decentralized and open environments. Information- and communication technology (ICT) enabled business solutions have created an opportunity for automated business relations and transactions. The deployment of ICT in business-to-business (B2B) integration seeks to improve competitiveness by establishing real-time information and offering better information visibility to business ecosystem actors. The products, components and raw material flows in supply chains are traditionally studied in logistics research. In this study, we expand the research to cover the processes parallel to the service and information flows as information logistics integration. In this thesis, we show how better integration and automation of information flows enhance the speed of processes and, thus, provide cost savings and other benefits for organizations. Investments in DBE are intended to add value through business automation and are key decisions in building up information logistics integration. Business solutions that build on automation are important sources of value in networks that promote and support business relations and transactions. Value is created through improved productivity and effectiveness when new, more efficient collaboration methods are discovered and integrated into DBE. Organizations, business networks and collaborations, even with competitors, form DBE in which information logistics integration has a significant role as a value driver. However, traditional economic and computing theories do not focus on digital business ecosystems as a separate form of organization, and they do not provide conceptual frameworks that can be used to explore digital business ecosystems as value drivers—combined internal management and external coordination mechanisms for information logistics integration are not the current practice of a company’s strategic process. In this thesis, we have developed and tested a framework to explore the digital business ecosystems developed and a coordination model for digital business ecosystem integration; moreover, we have analysed the value of information logistics integration. The research is based on a case study and on mixed methods, in which we use the Delphi method and Internetbased tools for idea generation and development. We conducted many interviews with key experts, which we recoded, transcribed and coded to find success factors. Qualitative analyses were based on a Monte Carlo simulation, which sought cost savings, and Real Option Valuation, which sought an optimal investment program for the ecosystem level. This study provides valuable knowledge regarding information logistics integration by utilizing a suitable business process information model for collaboration. An information model is based on the business process scenarios and on detailed transactions for the mapping and automation of product, service and information flows. The research results illustrate the current cap of understanding information logistics integration in a digital business ecosystem. Based on success factors, we were able to illustrate how specific coordination mechanisms related to network management and orchestration could be designed. We also pointed out the potential of information logistics integration in value creation. With the help of global standardization experts, we utilized the design of the core information model for B2B integration. We built this quantitative analysis by using the Monte Carlo-based simulation model and the Real Option Value model. This research covers relevant new research disciplines, such as information logistics integration and digital business ecosystems, in which the current literature needs to be improved. This research was executed by high-level experts and managers responsible for global business network B2B integration. However, the research was dominated by one industry domain, and therefore a more comprehensive exploration should be undertaken to cover a larger population of business sectors. Based on this research, the new quantitative survey could provide new possibilities to examine information logistics integration in digital business ecosystems. The value activities indicate that further studies should continue, especially with regard to the collaboration issues on integration, focusing on a user-centric approach. We should better understand how real-time information supports customer value creation by imbedding the information into the lifetime value of products and services. The aim of this research was to build competitive advantage through B2B integration to support a real-time economy. For practitioners, this research created several tools and concepts to improve value activities, information logistics integration design and management and orchestration models. Based on the results, the companies were able to better understand the formulation of the digital business ecosystem and the importance of joint efforts in collaboration. However, the challenge of incorporating this new knowledge into strategic processes in a multi-stakeholder environment remains. This challenge has been noted, and new projects have been established in pursuit of a real-time economy.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Effects of variety, soil type and nitrogen fertilizer supply on the nutritive value of barley for growing pigs

Selostus: Lajikkeen, typpilannoitustason ja maalajin vaikutus ohran ruokinnalliseen arvoon lihasioilla

Feeding value of grass ensiled with absorbents assessed in growing lambs

Selostus: Eri absorbenteilla valmistettu säilörehu karitsoiden ruokinnassa

Effects of expanding on the nutritive value of wheat bran in pig diets

Selostus: Ekspanderkäsittelyn vaikutus vehnänleseen rehuarvoon lihasian ruokinnassa

Effects of sodium sulphate and potassium chloride fertilizers on the nutritive value of timothy grown on different soils

Selostus: Natrium- ja kaliumlannoituksen vaikutus timotein ravintoarvoon

Timing of primary growth harvest affects the yield and nutritive value of timothy-red clover mixtures

Selostus: Ensimmäisen sadon korjuuaika vaikuttaa timotein ja puna-apilan seosnurmen satoon ja rehuarvoon