Integrating eco-innovation principles into regional strategy and policy: case of Päijät-Häme region, Finland

This study focuses on the integration of eco-innovation principles into strategy and policy at the regional level. The importance of regions as a level for integrating eco-innovative programs and activities served as the point of interest for this study. Eco-innovative activities and technologies are seen as means to meet sustainable development objective of improving regions’ quality of life. This study is conducted to get an in-depth understanding and learning about eco-innovation at regional level, and to know the basic concepts that are important in integrating eco-innovation principles into regional policy. Other specific objectives of this study are to know how eco-innovation are developed and practiced in the regions of the EU, and to analyze the main characteristic features of an eco-innovation model that is specifically developed at Päijät-Häme Region in Finland. Paijät-Häme Region is noted for its successful eco-innovation strategies and programs, hence, taken as casework in this study. Both primary (interviews) and secondary data (publicly available documents) are utilized in this study. The study shows that eco-innovation plays an important role in regional strategy as reviewed based on the experience of other regions in the EU. This is because of its localized nature which makes it easier to facilitate in a regional setting. Since regional authorities and policy-makers are normally focused on solving its localized environmental problems, eco-innovation principles can easily be integrated into regional strategy. The case study highlights Päijät-Häme Region’s eco-innovation strategies and projects which are characterized by strong connection of knowledge-producing institutions. Policy instruments supporting eco-innovation (e.g. environmental technologies) are very much focused on clean technologies, hence, justifying the formation of cleantech clusters and business parks in Päijät-Häme Region. A newly conceptualized SAMPO model of eco-innovation has been developed in Päijät-Häme Region to better capture the region’s characteristics and to eventually replace the current model employed by the Päijät-Häme Regional Authority. The SAMPO model is still under construction, however, review of its principles points to some of its three important spearheads – practice-based innovation, design (eco-design) and clean technology or environmental technology (environment).

Process approach in quality management - Case: ECO process development in EMS industry

The aim of the thesis was to study quality management with process approach and to find out how to utilize process management to improve quality. The operating environment of organizations has changed. Organizations are focusing on their core competences and networking with suppliers and customers to ensure more effective and efficient value creation for the end customer. Quality management is moving from inspection of the output to prevention of problems from occurring in the first place and management thinking is changing from functional approach to process approach. In the theoretical part of the thesis, it is studied how to define quality, how to achieve good quality, how to improve quality, and how to make sure the improvement goes on as never ending cycle. A selection of quality tools is introduced. Process approach to quality management is described and compared to functional approach, which is the traditional way to manage operations and quality. The customer focus is also studied, and it is presented, that to ensure long term customer commitment, organization needs to react to changing customer requirements and wishes by constantly improving the processes. In the experimental part the theories are tested in a process improvement business case. It is shown how to execute a process improvement project starting from defining the customer requirements, continuing to defining the process ownership, roles and responsibilities, boundaries, interfaces and the actual process activities. The control points and measures are determined for the process, as well as the feedback and corrective action process, to ensure continual improvement can be achieved and to enable verification that customer requirements are fulfilled.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

The Design and Implementation of a Power Management Feature to a Configuration Management System

Green IT is a term that covers various tasks and concepts that are related to reducing the environmental impact of IT. At enterprise level, Green IT has significant potential to generate sustainable cost savings: the total amount of devices is growing and electricity prices are rising. The lifecycle of a computer can be made more environmentally sustainable using Green IT, e.g. by using energy efficient components and by implementing device power management. The challenge using power management at enterprise level is how to measure and follow-up the impact of power management policies? During the thesis a power management feature was developed to a configuration management system. The feature can be used to automatically power down and power on PCs using a pre-defined schedule and to estimate the total power usage of devices. Measurements indicate that using the feature the device power consumption can be monitored quite precisely and the power consumption can be reduced, which generates electricity cost savings and reduces the environmental impact of IT.

Avaliação da medida do comprimento do colo e da ausência do eco glandular endocervical para predição do parto pré-termo

OBJETIVO: verificar a prevalência do sinal eco glandular endocervical (EGE) e o comprimento cervical menor ou igual a 20 mm em gestantes entre a 21ª e a 24ª semana e comparar estes sinais ecográficos como fatores indicadores de parto pré-termo espontâneo. MÉTODOS: estudo prospectivo transversal no qual foram incluídas 361 gestantes da população geral, para realização de exame ultra-sonografico em idade gestacional entre a 21ª a 24ª semana. Os critérios de exclusão do estudo foram malformações müllerianas, gestações múltiplas, malformações fetais, óbito fetal, alterações da quantidade de líquido amniótico, placenta com inserção segmentar, antecedentes de cirurgia no colo uterino (conização, amputação, cerclagem) e procedimentos cirúrgicos durante a gestação. Após a realização do exame ultra-sonográfico obstétrico morfológico efetuado por via abdominal, seguiu-se o exame ecográfico por via vaginal para observação de uma faixa hipoecóica ou hiperecóica adjacente ao canal endocervical correpondente às glândulas do epitélio endocervical (EGE) e mensuração do comprimento cervical. As variáveis qualitativas são representadas por freqüência absoluta e relativa, ao passo que as variáveis quantitativas, por média, desvio-padrão, mediana e valores mínimo e máximo. A associação entre as variáveis qualitativas foi avaliada pelo teste c² ou teste exato de Fisher. Para cada variável estudada, foi calculado o risco relativo seguido do intervalo com 95% de confiança. A técnica de análise de regressão logística univariada foi utilizada para verificar, entre as variáveis estudadas, quais foram indicativas de parto pré-termo espontâneo. O nível de significância adotado foi de 95% (alfa = 5%) e descritivos (p) iguais ou inferiores a 0,05 foram considerados significantes. RESULTADOS: a incidência do parto pré-termo espontâneo foi de 5,0%. O comprimento do colo uterino revelou-se igual ou inferior a 20 mm em 3,3% da população estudada e em 27,8% das pacientes que apresentaram parto pré-termo espontâneo. A ausência do EGE foi detectada em 2,8% das pacientes estudadas e em 44,4% das pacientes que evoluíram para parto pré-termo espontâneo. A associação entre ausência do EGE e presença do colo curto revelou-se estatisticamente significante (p<0,001). A ausência do EGE teve forte associação com parto pré-termo espontâneo e risco relativo de 28,57, com intervalo de confiança (IC 95%) 14,40-56,68. A medida do comprimento cervical inferior a 20 mm também apresentou associação com parto pré-termo espontâneo (p<0,001), com risco relativo de 11,27 e IC 95% de 4,79-26,53. CONCLUSÃO: a não visualização do EGE endocervical constitui parâmetro morfológico ultra-sonográfico novo e útil na predição do parto pré-termo espontâneo nas gestações únicas da população geral. Os resultados deste trabalho indicam uma tendência clara da marcante importância da ausência do EGE como indicador do risco para parto pré-termo espontâneo, a ser confirmada em pesquisas multicêntricas futuras.

Scaling up stepwise feature introduction to construction of large software systems

Developing software is a difficult and error-prone activity. Furthermore, the complexity of modern computer applications is significant. Hence,an organised approach to software construction is crucial. Stepwise Feature Introduction – created by R.-J. Back – is a development paradigm, in which software is constructed by adding functionality in small increments. The resulting code has an organised, layered structure and can be easily reused. Moreover, the interaction with the users of the software and the correctness concerns are essential elements of the development process, contributing to high quality and functionality of the final product. The paradigm of Stepwise Feature Introduction has been successfully applied in an academic environment, to a number of small-scale developments. The thesis examines the paradigm and its suitability to construction of large and complex software systems by focusing on the development of two software systems of significant complexity. Throughout the thesis we propose a number of improvements and modifications that should be applied to the paradigm when developing or reengineering large and complex software systems. The discussion in the thesis covers various aspects of software development that relate to Stepwise Feature Introduction. More specifically, we evaluate the paradigm based on the common practices of object-oriented programming and design and agile development methodologies. We also outline the strategy to testing systems built with the paradigm of Stepwise Feature Introduction.

A feature-based object-oriented expert system to model and support product design

In this paper a computer program to model and support product design is presented. The product is represented through a hierarchical structure that allows the user to navigate across the product’s components, and it aims at facilitating each step of the detail design process. A graphical interface was also developed, which shows visually to the user the contents of the product structure. Features are used as building blocks for the parts that compose the product, and object-oriented methodology was used as a means to implement the product structure. Finally, an expert system was also implemented, whose knowledge base rules help the user design a product that meets design and manufacturing requirements.

Developing of Supportive Context for Eco-Industrial Networks in Finland

The aim of the present thesis was to explore possible promotional actions to support the emergence of eco-industrial business networks in Finland. The main objectives were to investigate what kind of factors affect in the development of eco-industrial networks and further make suggestions in what kinds of actions this could be supported. In addition, since the active facilitation was discovered as one potential promoting activity, further investigation about facilitation process in Finnish context was conducted and also main characteristics of nationwide facilitation programme were identified. This thesis contains literature review of network orchestration and eco-industrial networks. The latter consists of green supply chain management and industrial symbiosis, although the main focus of the study leans on the concept of industrial symbiosis. The empirical data of the study was obtained from semi-structured expert interviews. These interviews were analyzed using qualitative content analysis. The study identified four main promotional activities for eco-industrial networks: 1) building awareness, 2) incentives, 3) dismantling of legislative barriers and 4) active facilitation. In addition, a framework for facilitation activities in Finnish context was built and main characteristics of nationwide facilitation programme were identified.

Amplification of 9q34 in childhood adrenocortical tumors: a specific feature unrelated to ethnic origin or living conditions

Adrenocortical tumors (ACT) in children under 15 years of age exhibit some clinical and biological features distinct from ACT in adults. Cell proliferation, hypertrophy and cell death in adrenal cortex during the last months of gestation and the immediate postnatal period seem to be critical for the origin of ACT in children. Studies with large numbers of patients with childhood ACT have indicated a median age at diagnosis of about 4 years. In our institution, the median age was 3 years and 5 months, while the median age for first signs and symptoms was 2 years and 5 months (N = 72). Using the comparative genomic hybridization technique, we have reported a high frequency of 9q34 amplification in adenomas and carcinomas. This finding has been confirmed more recently by investigators in England. The lower socioeconomic status, the distinctive ethnic groups and all the regional differences in Southern Brazil in relation to patients in England indicate that these differences are not important to determine 9q34 amplification. Candidate amplified genes mapped to this locus are currently being investigated and Southern blot results obtained so far have discarded amplification of the abl oncogene. Amplification of 9q34 has not been found to be related to tumor size, staging, or malignant histopathological features, nor does it seem to be responsible for the higher incidence of ACT observed in Southern Brazil, but could be related to an ACT from embryonic origin.

Operant serial feature-positive conditional discrimination with composite features and different topography responses

In a serial feature-positive conditional discrimination procedure the properties of a target stimulus A are defined by the presence or not of a feature stimulus X preceding it. In the present experiment, composite features preceded targets associated with two different topography operant responses (right and left bar pressing); matching and non-matching-to-sample arrangements were also used. Five water-deprived Wistar rats were trained in 6 different trials: X-R®Ar and X-L®Al, in which X and A were same modality visual stimuli and the reinforcement was contingent to pressing either the right (r) or left (l) bar that had the light on during the feature (matching-to-sample); Y-R®Bl and Y-L®Br, in which Y and B were same modality auditory stimuli and the reinforcement was contingent to pressing the bar that had the light off during the feature (non-matching-to-sample); A- and B- alone. After 100 training sessions, the animals were submitted to transfer tests with the targets used plus a new one (auditory click). Average percentages of stimuli with a response were measured. Acquisition occurred completely only for Y-L®Br+; however, complex associations were established along training. Transfer was not complete during the tests since concurrent effects of extinction and response generalization also occurred. Results suggest the use of both simple conditioning and configurational strategies, favoring the most recent theories of conditional discrimination learning. The implications of the use of complex arrangements for discussing these theories are considered.

De parallelismo sententiarum hebraicarum non vitioso 2

Invokaatio: I.N.J.

De parallelismo sententiarum hebraicarum non vitioso 1

Invokaatio: I.N.J.

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.

Partikkelikokojakaumamenetelmän testaus INCA Feature

Tässä työssä testattiin partikkelikokojakaumien analysoinnissa käytettävää kuvankäsittelyohjelmaa INCA Feature. Partikkelikokojakaumat määritettiin elektronimikroskooppikuvista INCA Feature ohjelmaa käyttäen partikkeleiden projektiokuvista päällystyspigmenttinä käytettävälle talkille ja kahdelle eri karbonaattilaadulle. Lisäksi määritettiin partikkelikokojakaumat suodatuksessa ja puhdistuksessa apuaineina käytettäville piidioksidi- ja alumiinioksidihiukkasille. Kuvankäsittelyohjelmalla määritettyjä partikkelikokojakaumia verrattiin partikkelin laskeutumisnopeuteen eli sedimentaatioon perustuvalla SediGraph 5100 analysaattorilla ja laserdiffraktioon perustuvalla Coulter LS 230 menetelmällä analysoituihin partikkelikokojakaumiin. SediGraph 5100 ja kuva-analyysiohjelma antoivat talkkipartikkelien kokojakaumalle hyvin samankaltaisen keskiarvon. Sen sijaan Coulter LS 230 laitteen antama kokojakauman keskiarvo poikkesi edellisistä. Kaikki vertailussa olleet partikkelikokojakaumamenetelmät asettivat eri näytteiden partikkelit samaan kokojärjestykseen. Kuitenkaan menetelmien tuloksia ei voida numeerisesti verrata toisiinsa, sillä kaikissa käytetyissä analyysimenetelmissä partikkelikoon mittaus perustuu partikkelin eri ominaisuuteen. Työn perusteella kaikki testatut analyysimenetelmät soveltuvat paperipigmenttien partikkelikokojakaumien määrittämiseen. Tässä työssä selvitettiin myös kuva-analyysiin tarvittava partikkelien lukumäärä, jolla analyysitulos on luotettava. Työssä todettiin, että analysoitavien partikkelien lukumäärän tulee olla vähintään 300 partikkelia. Liian suuri näytemäärä lisää kokojakauman hajontaa ja pidentää analyysiin käytettyä aikaa useaan tuntiin. Näytteenkäsittely vaatii vielä lisää tutkimuksia, sillä se on tärkein ja kriittisin vaihe SEM ja kuva-analyysiohjelmalla tehtävää partikkelikokoanalyysiä. Automaattisten mikroskooppien yleistyminen helpottaa ja nopeuttaa analyysien tekoa, jolloin menetelmän suosio tulee kasvamaan myös paperipigmenttien tutkimuksessa. Laitteiden korkea hinta ja käyttäjältä vaadittava eritysosaaminen tulevat rajaamaan käytön ainakin toistaiseksi tutkimuslaitoksiin.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.