Electric Motor and Frequency Converter Markets in Latin America and Especially in the Mining Industry of Chile

Latinalaisen Amerikan osuus maailmantaloudesta on pieni verrattuna sen maantieteelliseen kokoon, väkilukuun ja luonnonvaroihin. Aluetta pidetään kuitenkin yhtenä tulevaisuuden merkittävistä kasvumarkkinoista. Useissa Latinalaisen Amerikan maissa on teollisuutta, joka hyödyntää luonnonvaroja ja tuottaa raaka-aineita sekä kotimaan että ulkomaiden markkinoille. Tällaisia tyypillisiä teollisuudenaloja Latinalaisessa Amerikassa ovat kaivos- ja metsäteollisuus sekä öljyn ja maakaasun tuotanto. Näiden teollisuudenalojen tuotantolaitteiden ja koneiden valmistusta ei Latinalaisessa Amerikassa juurikaan ole. Ne tuodaan yleensä Pohjois-Amerikasta ja Euroopasta. Tässä diplomityössä tutkitaan sähkömoottorien ja taajuusmuuttajien markkinapotentiaalia Latinalaisessa Amerikassa. Tutkimuksessa perehdytään Latinalaisen Amerikan maiden kansantalouksien tilaan sekä arvioidaan sähkömoottorien ja taajuusmuuttajien markkinoiden kokoa tullitilastojen avulla. Chilen kaivosteollisuudessa arvioidaan olevan erityistä potentiaalia. Diplomityössä selvitetään ostoprosessin kulkua Chilen kaivosteollisuudessa ja eri asiakastyyppien roolia siinä sekä tärkeimpiä päätöskriteerejä toimittaja- ja teknologiavalinnoissa.

Analysis of corporate social responsibility (CSR) performance and financial performance of selected mining companies

The global concern about sustainability has been growing and the mining industry is questioned about its environmental and social performance. Corporate social responsibility (CSR) is an important issue for the extractive industries. The main objective of this study was to investigate the relationship between CSR performance and financial performance of selected mining companies. The study was conducted by identifying and comparing a selection of available CSR performance indicators with financial performance indicators. Based on the result of the study, the relationship between CSR performance and financial performance is unclear for the selected group of companies. The result is mixed and no industry specific realistic way to measure CSR performance uniformly is available. The result as a whole is contradictory and varies at company level as well as based on the selected indicators. The result of this study confirms that the relationship between CSR performance and financial performance is complicated and difficult to determine. As an outcome, evaluation of benefits of CSR in the mining sector could better be analyzed based on different attributes.

Electrical Machines' Market in Russia

Venäjän valtion osuus maailmantaloudesta on pieni verrattuna sen maantieteelliseen kokoon, väkilukuun ja luonnonvaroihin. Sitä pidetään kuitenkin yhtenä tulevaisuuden merkittävistä kasvumarkkinoista. Venäjällä on tyypillisesti teollisuutta, joka hyödyntää luonnonvaroja ja tuottaa raaka-aineita sekä kotimaan että ulkomaiden markkinoille. Tällaisia tyypillisiä teollisuudenaloja Venäjällä ovat kaivos- ja metsäteollisuus sekä kemikaalien- kaasun- ja öljyntuotanto. Myös näiden teollisuusalojen tarvitsemien tuotantolaitteiden ja koneiden valmistusta on Venäjällä. Näitä koneita viedään Venäjältä entisiin neuvostovaltioihin ja päinvastoin. Tässä diplomityössä tutkitaan sähkömoottorien markkinapotentiaalia ja kilpailutilannetta Venäjällä. Venäjän osalta perehdytään sen kansantalouden tilaan ja tutkitaan sähkökonemarkkinoiden kokoa segmenteittäin monien erilähteiden avulla. Venäjän arvioidaan olevan erittäin potentiaalinen ja kasvava markkina-alue. Diplomityössä selvitetään ostoprosessia Venäjällä ja sähkökonemarkkinoiden ominaisuuksia kyseisellä alueella.

Resource-based approach to new product development at the intersection of industries. Two case studies in telecommunications

Työn tavoitteena on selvittää mitkä ovat tärkeimmät aineettomat resurssit, joita tarvitaan teollisuuksien risteyskohdassa tapahtuvassa tuotekehityksessä. Teollisuuksien risteyskohdissa syntyvät tuotteet ovat usein radikaaleja, mikä tekee tuotteista mielenkiintoisia, paljon liiketoimintapotentiaalia tarjoavia. Tämä tutkimus lähestyy tuotekehitystä resurssipohjaisesta näkökulmasta. Myös tietämyspohjaista ja suhdepohjaista näkemystä hyödynnetään korostamaan keskittymistä aineettomiin resursseihin. Tutkimuksessa rakennetaan viitekehys, jossa tutkitaan eri resurssikategorioita. Valitut kategoriat ovat teknologiset, markkinointi-, johtamiseen ja hallinnointiin liittyvät ja suhdepohjaiset resurssit. Empiirisessä osassa tutkitaan kahta uutta tuotekonseptia, jotka ovat syntyneet teollisuuksien risteyskohdissa. Empiirisen osan tavoitteena on määritellä tutkimuksen kohteena olevia alustavia tuotekonsepteja tarkemmin ja selvittää millaisia resursseja näiden toteuttamiseen tarvitaan. Myös tarvittavien resurssien nykytila selvitetään ja pohditaan tulisiko puuttuvia resursseja kehittää yrityksen sisällä vai hankkia ne ulkopuolelta. Tutkimus toteutettiin asiantuntijahaastatteluin. Kahden tapaustutkimuksen perusteella näyttäisi siltä, että suhdepohjaiset resurssit ovat erittäin tärkeitä teollisuuksien risteyskohdissa tapahtuvassa tuotekehityksessä. Myös teknologiset resurssit ovat tärkeitä. Markkinointiresurssien tärkeys riippuu lopullisesta tuotekonseptista, kun taas johtamiseen ja kehittämiseen liittyvät resurssit ovat tärkeitänäiden konseptien luomisessa.

Kaivosteollisuudessa syntyvän kipsin hyödyntäminen

Diplomityön tarkoituksena oli tutkia ja kehittää käyttökohde kaivosteollisuudessa syntyvälle märälle kipsisivuvirralle, joka sisältää metalliepäpuhtauksina alumiinia, rautaa ja mangaania ja jonka määrä on noin 1 000 000 t/a. Kirjallisuuden pohjalta tutkittiin aluksi mahdollisuutta hyödyntää kipsiaines asfaltti- ja sementtiteollisuuden raaka-aineena. Sementin joukkoon lisätään tavallisesti noin 5 p-% kipsiä, mutta harvinaisimpiin sementtilaatuihin sitä voidaan lisätä jopa 30 p-%. Tästä huolimatta vain pieni osa tutkimuksen kohteessa syntyvästä kipsisivuvirrasta voitaisiin hyödyntää tässä sovelluksessa. Lisäksi kipsisivuvirran sisältämät epäpuhtaudet täytyisi poistaa tai saattaa inaktiiviseen muotoon. Myöskään sen kosteuspitoisuus ei saisi olla suuri. Näin ollen tämän kipsisivuvirran hyödyntäminen asfaltti- ja sementtiteollisuuden lisäaineena ei ole mahdollista Seuraavaksi harkittiin kipsin kierrättämistä, jolloin yhtenä vaihtoehtona oli hajottaa kipsi termisesti rikkioksideiksi ja valmistaa niistä rikkihappoa. Taloudellisista syistä hajoamistuotteen on oltava rikkitrioksidia, josta voitaisiin veteen imeyttämällä valmistaa rikkihappoa. Kipsin hajottaminen termovaa´alla osoitti, että kipsi vaatii noin 1400 ºC:n lämpötilan ja haihtuvat komponentit ovat H2O, SO ja SO2, muttei SO3. Alempien oksidien muuttaminen rikkihapoksi vaatisi katalyyttisen hapetuksen, mikä olisi käytännössä liian kallista. Toisena vaihtoehtona kipsin kierrättämiseksi tutkittiin sen biologista pelkistämistä rikkivedyksi ja kalsiumhydroksidilietteeksi. Laboratoriossa Ca(OH)2-lietteestä valmistettiin hiilidioksidin avulla kalsiumkarbonaattia, jolloin päästiin 90 %:n kalsiumhydroksidin konversiossa. Lisäksi alumiinihydroksidi saatiin erotettua kipsilietteestä kokeellisesti hydrosyklonin avulla. Diplomityössä päädyttiin siihen, että sulfaatin biologinen pelkistäminen ja alumiinihydroksidin mekaaninen erotus jatkuvatoimisesti on varteenotettava vaihtoehto kipsisivuvirran hyödyntämiseksi.

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.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.

Opinion Mining

In this thesis we study the field of opinion mining by giving a comprehensive review of the available research that has been done in this topic. Also using this available knowledge we present a case study of a multilevel opinion mining system for a student organization's sales management system. We describe the field of opinion mining by discussing its historical roots, its motivations and applications as well as the different scientific approaches that have been used to solve this challenging problem of mining opinions. To deal with this huge subfield of natural language processing, we first give an abstraction of the problem of opinion mining and describe the theoretical frameworks that are available for dealing with appraisal language. Then we discuss the relation between opinion mining and computational linguistics which is a crucial pre-processing step for the accuracy of the subsequent steps of opinion mining. The second part of our thesis deals with the semantics of opinions where we describe the different ways used to collect lists of opinion words as well as the methods and techniques available for extracting knowledge from opinions present in unstructured textual data. In the part about collecting lists of opinion words we describe manual, semi manual and automatic ways to do so and give a review of the available lists that are used as gold standards in opinion mining research. For the methods and techniques of opinion mining we divide the task into three levels that are the document, sentence and feature level. The techniques that are presented in the document and sentence level are divided into supervised and unsupervised approaches that are used to determine the subjectivity and polarity of texts and sentences at these levels of analysis. At the feature level we give a description of the techniques available for finding the opinion targets, the polarity of the opinions about these opinion targets and the opinion holders. Also at the feature level we discuss the various ways to summarize and visualize the results of this level of analysis. In the third part of our thesis we present a case study of a sales management system that uses free form text and that can benefit from an opinion mining system. Using the knowledge gathered in the review of this field we provide a theoretical multi level opinion mining system (MLOM) that can perform most of the tasks needed from an opinion mining system. Based on the previous research we give some hints that many of the laborious market research tasks that are done by the sales force, which uses this sales management system, can improve their insight about their partners and by that increase the quality of their sales services and their overall results.

Venäläisten yritysten kansainvälistyminen

Tämän kandidaatintyön tavoitteena on selvittää ja analysoida venäläisten yritysten kansainvälistymistä investointien näkökulmasta. Aihetta tutkittiin Venäjältä ulospäin suuntautuneiden investointien valossa. Kansainvälistymistä analysoitiin konkreettisten yritysesimerkkien kautta. Työssä käytettiin lähteinä aikaisempia aiheesta tehtyjä tutkimuksia sekä esimerkkeinä olevien yritysten internetsivuja. Venäläisten yritysten kansainvälistyminen alkoi toden teolla vasta Neuvostoliiton hajoamisen jälkeen. Käytännössä yritysten kansainvälistyminen on lähtenyt kasvuun vasta 2000-luvulla. Viime vuosien aikana Venäjä on ollut yksi suurimmista ulospäin investoijista nousevien markkinatalouksien joukossa. Suurimmat Venäjältä ulospäin investoijat toimivat öljy- ja kaasuteollisuudessa sekä metalli- ja kaivosteollisuudessa. Kyseiset teollisuudenalat ovat riippuvaisia raaka-aineiden maailmanmarkkinahinnoista. Vuoden 2008 lopulla alkanut talouskriisi on alentanut raaka-aineiden hintoja ja näin vaikuttanut yritysten toimintaan. Yritykset ovat joutuneet talousvaikeuksiin, joka on vaikuttanut myös niiden investointeihin ulkomaille. Tällä hetkellä Venäjä on riippuvainen luonnonvaroihin perustuvista teollisuudenaloista, mutta tulevaisuudessa uusilla yrityksillä on mahdollisuuksia nousta kansainvälisien yritysten joukkoon. Raaka-aineiden hintojen kääntyessä jälleen nousuun myös luonnonvaroihin perustuvat yritykset tulevat nousemaan ahdingosta ja jatkamaan kansainvälistymistä.

Engendering Group Support Based Foresight for Capital Intensive Manufacturing Industries - Case Paper and Steel Industry Scenarios by 2018

In recent times of global turmoil, the need for uncertainty management has become ever momentous. The need for enhanced foresight especially concerns capital-intensive industries, which need to commit their resources and assets with long-term planning horizons. Scenario planning has been acknowledged to have many virtues - and limitations - concerning the mapping of the future and illustrating the alternative development paths. The present study has been initiated to address both the need of improved foresight in two capital-intensive industries, i.e. the paper and steel industries and the imperfections in the current scenario practice. The research problem has been approached by engendering a problem-solving vehicle, which combines, e.g. elements of generic scenario process, face-to-face group support methods, deductive scenario reasoning and causal mapping into a fully integrated scenario process. The process, called the SAGES scenario framework, has been empirically tested by creating alternative futures for two capital-intensive industries, i.e. the paper and steel industries. Three scenarios for each industry have been engendered together with the identification of the key megatrends, the most important foreign investment determinants, key future drivers and leading indicators for the materialisation of the scenarios. The empirical results revealed a two-fold outlook for the paper industry, while the steel industry future was seen as much more positive. The research found support for utilising group support systems in scenario and strategic planning context with some limitations. Key perceived benefits include high time-efficiency, productivity and lower resource-intensiveness. Group support also seems to enhance participant satisfaction, encourage innovative thinking and provide the users with personalised qualitative scenarios.

Analysis of patterns in mining time series using Qlucore

Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

Corporate Social Responsibility in Global Mining Industry

Corporate Social Responsibility is company’s interest and actions towards its environment and the society that the company takes from its free will, to give back to the community and environment. Corporate Social Responsibility is current topic as companies are challenged to take responsibility for their action, due to the constant tightening environmental legislations and raising pressure for transparency from the public. The objective of this Master’s Thesis research is to study if Corporate Social Responsibility affects suppliers’ brand image and mining companies’ buying decisions within global mining industry. The research method is qualitative and the research is conducted with secondary and primary research methods. The research aims to find out what are the implications of the research for the case company Larox. The objective is to answer to the question; how should case company Larox start to develop Corporate Social Responsibility (CSR) program of its own, and how the case company could benefit from CSR as a competitive advantage and what actions could be taken in the company marketing. Conclusions are drawn based on both the secondary and primary research results. Both of the researches imply that CSR is well present in the global mining industry, and that suppliers’ CSR policy has positive effect on company image, which positively affects company’s brand, and furthermore brand has a positive effect on mining companies buying decision. It can be concluded that indirectly CSR has an effect on buying decisions, and case company should consider developing a CSR program of its own.