Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Potential Acquisition Candidates for a Supplier of Wood Processing Machinery

Diplomityön tavoitteena oli sopivimman yritysostokohteen valitseminen useiden kilpailijoiden joukosta puunkäsittelykoneiden toimittajalle. Ensin esiteltiin Suomen metsäteollisuus sekä sen osaamistarpeista noussut metsäklusteri pääosin kohdeyrityksen näkökulmasta. Seuraavaksi annettiin kuva yrityksen tuotteista, kilpailijoista ja asiakkaista. Yritysostoprosessi kuvattiin sekä esille tuotiin yleiset motiivit ja kriittiset menestystekijät. Lisäksi kuvattiin kilpailijoiden ja liiketoimintaympäristön analysointi yrityksen menestyksen edellytyksenä. Puuntyöstökoneiden markkinat segmentoitiin ja analysoitiin vuodesta 1990 aina tähän päivään asti, jotta löydettäisiin kehityskelpoiset osa-alueet eli alueet, joissa yrityksen markkinaosuutta voitaisiin kasvattaa. Kandidaattien ominaisuuksia verrattiin yritysoston motiiveihin. Yritysten tuotteet sekä maantieteellinen sijainti pisteytettiin, jotta sopivimmat yritykset nousisivat esille. Kolme yritystä valittiin syvällisempään tarkasteluun. Yritysten tuotteita, taloudellista asemaa ja globaalia verkostoa vertailtiin keskenään muiden tekijöiden, kuten maailmantalouden ohessa. Taloudellisesti vakaa ja teknisesti monipuolinen yritys kohtasi yritysoston motiivit parhaiten. Kohteen positiivisia puolia olivat sijainti, tuotteet ja palvelut. Lisäksi, yritys sopii ostajan strategiaan sekä auttaa kohtaamaan asiakkaiden nykyiset ja tulevat tarpeet.

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.

Business Models of Packaging Machinery Manufacturers – Case Study

The goal of the study was to find a proper frame to understand business models and study business models of the chosen companies in packaging machinery manufacturing. Good practices and tips are searched from business models which have helped companies to success. Packaging industry’s future is also examined in front of different kinds of changes and the influence which they have on machinery manufacturer’s business models. In the theory part business models’ history and the best frame suitable for this study are presented. The chosen case companies have been discussed according to the frame, and they have been compared to each other to point out the differences. The good practices noticed in companies and according to information from other sources, new business model has been constructed including things that should be noticed while constructing a new business model. The information sources of this study where interviews, annual reports, companies presentations and web pages. The type of study was an interpretative case study.

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.

Construction of an index of information from clinical practice in Radiology and Imaging Diagnosis based on text mining and thesaurus

Objective To construct a Portuguese language index of information on the practice of diagnostic radiology in order to improve the standardization of the medical language and terminology. Materials and Methods A total of 61,461 definitive reports were collected from the database of the Radiology Information System at Hospital das Clínicas – Faculdade de Medicina de Ribeirão Preto (RIS/HCFMRP) as follows: 30,000 chest x-ray reports; 27,000 mammography reports; and 4,461 thyroid ultrasonography reports. The text mining technique was applied for the selection of terms, and the ANSI/NISO Z39.19-2005 standard was utilized to construct the index based on a thesaurus structure. The system was created in *html. Results The text mining resulted in a set of 358,236 (n = 100%) words. Out of this total, 76,347 (n = 21%) terms were selected to form the index. Such terms refer to anatomical pathology description, imaging techniques, equipment, type of study and some other composite terms. The index system was developed with 78,538 *html web pages. Conclusion The utilization of text mining on a radiological reports database has allowed the construction of a lexical system in Portuguese language consistent with the clinical practice in Radiology.

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.

Educational Data Mining: cerrando el círculo del proceso de aprendizaje en entornos virtuales

Development of methods to explore data from educational settings, to understand better the learning process.

Data mining and mall users profile

Marketing scholars have suggested a need for more empirical research on consumer response to malls, in order to have a better understanding of the variables that explain the behavior of the consumers. The segmentation methodology CHAID (Chi-square automatic interaction detection) was used in order to identify the profiles of consumers with regard to their activities at malls, on the basis of socio-demographic variables and behavioral variables (how and with whom they go to the malls). A sample of 790 subjects answered an online questionnaire. The CHAID analysis of the results was used to identify the profiles of consumers with regard to their activities at malls. In the set of variables analyzed the transport used in order to go shopping and the frequency of visits to centers are the main predictors of behavior in malls. The results provide guidelines for the development of effective strategies to attract consumers to malls and retain them there.

Mercury in soils and sediments from gold mining liabilities in Southern Amazonia

The 1980-1990 Amazonian gold rush left an enormous liability that increasingly has been substituted by developing fish aquaculture. This work aimed at the identification of the mercury levels in the environment, associated with fish farms located in the North of Mato Grosso State, Southern Amazon. Sediment and soil samples were analyzed for total organic carbon and total mercury. Results indicate that the chemical characteristics of the sediment largely depend on the management procedures of the fish pond (liming, fish food used and fish population). The soils presented relatively low concentrations when compared with other data from the literature.

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.

Mining in Developing Countries: A Case Study of Sustainability Challenges

Mining has severe impacts on its surrounding. Particularly in the developing countries it has degraded the environment and signigicantly altered the socio-economical dynamics of the hosts. Especially relocation disrupts people from their homes, livelihoods, cultures and social activities. Mining industry has failed to develop the local host and streghten its governance structures; instead it has further degraded the development of mineral rich third world countries, which are among the world poorest ones. Cash flows derived from mining companies have not benefitted the crass-root level that however, bears most of the detrimental impacts. Especially if the governance structure of the host is weak, the sudden wealth is likely to accelerate disparities, corruption and even fuel wars. Environmental degradation, miscommunication, mistrust and disputes over land use have created conflicts between the communities and a mining company in Obuasi, Ghana; a case study of this thesis. The disputes are deeply rooted and further fuelled by unrealistic expectations and broken promises. The relations with artisanal and illegal miners have been especially troublesome. Illegal activities, mainly encroachment of the land and assets of the mine, such as vandalising tailings pipes have resulted in profits losses, environmental degradation and security hazards. All challenges mentioned above have to be addressed locally with site-specific solutions. It is vital to increase two-way communication, initiate collaboration and build capacity of the stakeholders such as local communities, NGOs and governance authorities. The locals must be engaged to create livelihood opportunities that are designed with and for them. Capacity can also be strengthened through education and skills training, such as women’s literacy programs. In order to diminish the overdependence of locals to the mine, the activities have to be self -sufficient and able to survive without external financial and managerial inputs. Additionally adequate and fair compensation practises and dispute resolution methods that are understood and accepted by all parties have to be agreed on as early as possible.