Embedding objects into Matlab/Simulink for process supervision

This paper introduces how artificial intelligence technologies can be integrated into a known computer aided control system design (CACSD) framework, Matlab/Simulink, using an object oriented approach. The aim is to build a framework to aid supervisory systems analysis, design and implementation. The idea is to take advantage of an existing CACSD framework, Matlab/Simulink, so that engineers can proceed: first to design a control system, and then to design a straightforward supervisory system of the control system in the same framework. Thus, expert systems and qualitative reasoning tools are incorporated into this popular CACSD framework to develop a computer aided supervisory system design (CASSD) framework. Object-variables an introduced into Matlab/Simulink for sharing information between tools

A Monte Carlo-based procedure for independent monitor unit calculation in IMRT treatment plans.

Intensity-modulated radiotherapy (IMRT) treatment plan verification by comparison with measured data requires having access to the linear accelerator and is time consuming. In this paper, we propose a method for monitor unit (MU) calculation and plan comparison for step and shoot IMRT based on the Monte Carlo code EGSnrc/BEAMnrc. The beamlets of an IMRT treatment plan are individually simulated using Monte Carlo and converted into absorbed dose to water per MU. The dose of the whole treatment can be expressed through a linear matrix equation of the MU and dose per MU of every beamlet. Due to the positivity of the absorbed dose and MU values, this equation is solved for the MU values using a non-negative least-squares fit optimization algorithm (NNLS). The Monte Carlo plan is formed by multiplying the Monte Carlo absorbed dose to water per MU with the Monte Carlo/NNLS MU. Several treatment plan localizations calculated with a commercial treatment planning system (TPS) are compared with the proposed method for validation. The Monte Carlo/NNLS MUs are close to the ones calculated by the TPS and lead to a treatment dose distribution which is clinically equivalent to the one calculated by the TPS. This procedure can be used as an IMRT QA and further development could allow this technique to be used for other radiotherapy techniques like tomotherapy or volumetric modulated arc therapy.

Methods for Estimating Annual Exceedance-Probability Discharges for Streams in Iowa, Based on Data through Water Year 2010, TR-519, 2013

A statewide study was performed to develop regional regression equations for estimating selected annual exceedance- probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedanceprobability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage. Regional regression analysis, using generalized leastsquares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions. Average standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized leastsquares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations. These regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.

Market efficiency and calendar anomalies in the Russian stock market

Tämän tutkimuksen tarkoituksena on tarkastella esiintyykö Venäjän osakemarkkinoilla kalenterianomalioita. Tutkimus keskittyy Halloween-, kuukausi-, kuunvaihde-, viikonpäivä- ja juhlapäiväanomalioiden tarkasteluun. Tutkimusaineistona käytetään RTS (Russian Trading System) indeksiä. Tarkasteluaika alkaa 1. syyskuuta 1995 ja loppuu 31. joulukuuta 2005. Havaintojen kokonaismäärä on 2584. Tutkimusmenetelmänä käytetään pienimmän neliösumman menetelmää (OLS). Tutkimustulokset osoittavat, että Venäjän osakemarkkinoilla esiintyy Halloween-, kuunvaihde- ja viikonpäiväanomalioita. Sen sijaan kuukausi- ja juhlapäiväanomalioita ei tulosten mukaanesiinny Venäjän osakemarkkinoilla. Tulokset osoittavat lisäksi, että suurin osaanomalioista on merkittävämpiä nykyään kuin Venäjän osakemarkkinoiden ensimmäisinä vuosina. Näiden tulosten perusteella voidaan todeta, että Venäjän osakemarkkinat eivät ole vielä tehokkaat.

3-D density structure and geological evolution of Stromboli volcano (Aeolian Islands, Italy) inferred from land-based and sea-surface gravity data

We present the first density model of Stromboli volcano (Aeolian Islands, Italy) obtained by simultaneously inverting land-based (543) and sea-surface (327) relative gravity data. Modern positioning technology, a 1 x 1 m digital elevation model, and a 15 x 15 m bathymetric model made it possible to obtain a detailed 3-D density model through an iteratively reweighted smoothness-constrained least-squares inversion that explained the land-based gravity data to 0.09 mGal and the sea-surface data to 5 mGal. Our inverse formulation avoids introducing any assumptions about density magnitudes. At 125 m depth from the land surface, the inferred mean density of the island is 2380 kg m(-3), with corresponding 2.5 and 97.5 percentiles of 2200 and 2530 kg m-3. This density range covers the rock densities of new and previously published samples of Paleostromboli I, Vancori, Neostromboli and San Bartolo lava flows. High-density anomalies in the central and southern part of the island can be related to two main degassing faults crossing the island (N41 and NM) that are interpreted as preferential regions of dyke intrusions. In addition, two low-density anomalies are found in the northeastern part and in the summit area of the island. These anomalies seem to be geographically related with past paroxysmal explosive phreato-magmatic events that have played important roles in the evolution of Stromboli Island by forming the Scari caldera and the Neostromboli crater, respectively. (C) 2014 Elsevier B.V. All rights reserved.

Toimittaja-arvioinnin ja toimitusvalvonnan kehittäminen metsäteollisuuden kunnossapitoyrityksessä

Tutkimuksen tavoitteena oli kehittää toimitusvalvontaa ja toimittaja-arviointia metsäteollisuuden kunnossapitoyrityksen toiminnanohjausjärjestelmän sekä toimintatapojen osalta. Tutkimus toteutettiin haastattelemalla yrityksen eri yksiköiden hankintaorganisaatioita sekä valittuja toimittajia ja sidosryhmiä. Tutkimuksessa myös tutustuttiin kohdeyrityksen SAP R/3 -järjestelmän nykyisiin hyödynnettyihin ja hyödyntämättömiin ominaisuuksiin. Työn teoreettinen viitekehys perustui hankintatoiminnan ja kunnossapidon kehityssuuntien sekä soveltuvien teknologisten ratkaisujen tarkasteluun kunnossapitoyrityksen toimintaympäristössä. Tutkimuksessa havaittuja kehitystarpeita arvioitiin järjestelmäasiantuntijoiden ja muodostetun laatukustannusten laskentamallin avulla. Toimittaja-arviointi rajattiin tutkimuksessa koskemaan toimitusvarmuuden ja laatupoikkeamien jatkuvaa, keskitettyä raportoinnin työkalujen hyödyntämistä. Toimitusvalvonnan parantamiseksi arvioitiin kunnossapitotoimintaan ja kunnossapidon hankintoihin soveltuvaa ratkaisua. Kehitysehdotuksia ja suosituksia ilmeni useita ja ne ryhmiteltiin kohdeyrityksen kannalta helposti toteutettaviin tai toimintamalleista johtuviin, kohtalaisia muutoksia vaativiin ja suuria muutoksia vaativiin hankkeisiin.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Simulação de espectros de ressonância paramagnética eletrônica (RPE) através do programa NLSL

EPR users often face the problem of extracting information from frequently low-resolution and complex EPR spectra. Simulation programs that provide a series of parameters, characteristic of the investigated system, have been used to achieve this goal. This work describes the general aspects of one of those programs, the NLSL program, used to fit EPR spectra applying a nonlinear least squares method. Several motion regimes of the probes are included in this computational tool, covering a broad range of spectral changes. The meanings of the different parameters and rotational diffusion models are discussed. The anisotropic case is also treated by including an orienting potential and order parameters. Some examples are presented in order to show its applicability in different systems.

Looking beneath Dalí's paint: non-destructive canvas analysis

A new analytical method was developed to non-destructively determine pH and degree of polymerisation (DP) of cellulose in fibres in 19th 20th century painting canvases, and to identify the fibre type: cotton, linen, hemp, ramie or jute. The method is based on NIR spectroscopy and multivariate data analysis, while for calibration and validation a reference collection of 199 historical canvas samples was used. The reference collection was analysed destructively using microscopy and chemical analytical methods. Partial least squares regression was used to build quantitative methods to determine pH and DP, and linear discriminant analysis was used to determine the fibre type. To interpret the obtained chemical information, an expert assessment panel developed a categorisation system to discriminate between canvases that may not be fit to withstand excessive mechanical stress, e.g. transportation. The limiting DP for this category was found to be 600. With the new method and categorisation system, canvases of 12 Dalí paintings from the Fundació Gala-Salvador Dalí (Figueres, Spain) were non-destructively analysed for pH, DP and fibre type, and their fitness determined, which informs conservation recommendations. The study demonstrates that collection-wide canvas condition surveys can be performed efficiently and non-destructively, which could significantly improve collection management.

QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR

QSAR modeling is a novel computer program developed to generate and validate QSAR or QSPR (quantitative structure- activity or property relationships) models. With QSAR modeling, users can build partial least squares (PLS) regression models, perform variable selection with the ordered predictors selection (OPS) algorithm, and validate models by using y-randomization and leave-N-out cross validation. An additional new feature is outlier detection carried out by simultaneous comparison of sample leverage with the respective Studentized residuals. The program was developed using Java version 6, and runs on any operating system that supports Java Runtime Environment version 6. The use of the program is illustrated. This program is available for download at lqta.iqm.unicamp.br.

MCR-ALS APLICADO NO MONITORAMENTO QUANTITATIVO DO PROCESSO DE ELETRODEGRADAÇÃO DA ATRAZINA USANDO ESPECTROS UV: RESULTADOS COMPARATIVOS COM HPLC-DAD COMO UM MÉTODO DE REFERÊNCIA

Electrodegradation of atrazine in water was performed using homemade (PA and PB) and purchased (PC) boron-doped diamond anodes. The degradation was monitored off-line by analyzing total organic carbon and high performance liquid chromatography with diode array detector (HPLC-DAD) and at-line by UV spectroscopy. The spectra were recorded every 2 min. The rank deficiency problem was resolved by assembling an augmented column-wise matrix. HPLC was employed to separate the original and byproducts degradation components. Aiming the same goal, multivariate curve resolution - alternating least squares (MCR-ALS) was applied to resolve the UV spectroscopic data. Comparison between HPLC and MCR-ALS separations is presented. By using MCR-ALS the spectra of atrazine and two byproducts were successfully resolved and the resulted concentration profiles properly represented the system studied. The ALS explained variance (R2) for PA, PB and PC was equal to 99.99% for all of them and the lack of fit for PA, PB and PC were 0.39, 0.34 and 0.54 respectively. The correlation (R) between the recovered and pure spectra were calculate for each electrodegradation, validating the MCR-ALS results. The average R was equal to 0.997. The spectral and concentration profiles described with this new approach are in agreement with HPLC-DAD results. The proposed method is an alternative to classical analyses for monitoring of the degradation process, mainly due to the simplicity, fast results and economy.

Sähköisten hankintamenettelyjen taloudelliset yritysvaikutukset

Pro gradu -tutkielmassa tutkitaan julkisissa hankinnoissa käyttöön otettavien uusien sähköisten menettelyjen, sähköisen huutokaupan ja dynaamisen hankintajärjestelmän, yritysvaikutuksia. Ydintutkimustehtävänä on tutkia, millaisia taloudellisia vaikutuksia sähköisillä menettelyillä tulee olemaan yrityksiin niiden tarjotessa palveluita tai tavaroita julkiselle sektorille ja mitkä ovat sähköisten menettelyjen käytön motiivit. Tutkielman teoriaosa on pohja empiiriselle tutkimukselle, jossa aineistona on käytetty suppeaa kyselytutkimusta, haastatteluja ja lisäksi materiaalia on tuotettu analysoimalla hallinnollisten kustannusten määrää sähköisissä hankintamenettelyissä. Yritysvaikutusten arvioinnissa tarkasteltiin sähköisten hankintamenettelyjen käytön motiiveja. Motiiveiksi tunnistettiin tehokkuuden tavoittelu, kustannussäästöt, ajankäytön ja henkilöresurssien käytön tehostaminen sekä avoimuus. Tutkimuksessa onnistuttiin todentamaan edellä mainittuja tekijöitä taloudellisilla malleilla. Määrälliset ja laadulliset tutkimustulokset osoittavat kokonaisuudessaan tuloksellisella tavalla sähköisten hankintamenettelyjen käytön motiiveita ja vaikutuksia yrityksiin. Sähköisillä hankintamenettelyillä voidaan perustellusti tehostaa hankintaprosessia ja saavuttaa kustannussäästöjä. Monia yleisiä yritysten julkisiin hankintoihin pääsyn esteinä pitämiä seikkoja voitaisiin vähentää tai poistaa käyttämällä sähköisiä hankintoja. Sähköistä huutokauppaa ja dynaamista hankintajärjestelmää koskevan lainsäädännön luodessa puitteet menettelyiden käytölle, tulee menettelyjen käyttöönoton motiivien ja kannustimien riittävyyttä seurata. Hallinnollisten kustannusten väheneminen ja muiden esteiden pieneneminen ei välttämättä synnytä pysyvää kannustinvaikutusta, vaan hankintaprosessia tulee pyrkiä sähköistämään kokonaisvaltaisesti myös muissa hankintalain mukaisissa hankintamenettelyissä ja kilpailuttamisen ulkopuolisissa hankinnan osa-alueissa kuten laskuttaminen, maksatus ja tilaus.

Making Sense of Shared Leadership. A case study of leadership processes and practices without formal leadership structure in the team context

Leadership is essential for the effectiveness of the teams and organizations they are part of. The challenges facing organizations today require an exhaustive review of the strategic role of leadership. In this context, it is necessary to explore new types of leadership capable of providing an effective response to new needs. The presentday situations, characterized by complexity and ambiguity, make it difficult for an external leader to perform all leadership functions successfully. Likewise, knowledge-based work requires providing professional groups with sufficient autonomy to perform leadership functions. This study focuses on shared leadership in the team context. Shared leadership is seen as an emergent team property resulting from the distribution of leadership influence across multiple team members. Shared leadership entails sharing power and influence broadly among the team members rather than centralizing it in the hands of a single individual who acts in the clear role of a leader. By identifying the team itself as a key source of influence, this study points to the relational nature of leadership as a social construct where leadership is seen as social process of relating processes that are co-constructed by several team members. Based on recent theoretical developments concerned with relational, practice-based and constructionist approaches to the study of leadership processes, this thesis proposes the study of leadership interactions, working processes and practices to focus on the construction of direction, alignment and commitment. During the research process, critical events, activities, working processes and practices of a case team have been examined and analyzed with the grounded theory –approach in the terms of shared leadership. There are a variety of components to this complex process and a multitude of factors that may influence the development of shared leadership. The study suggests that the development process of shared leadership is a common sense -making process and consists of four overlapping dimensions (individual, social, structural, and developmental) to work with as a team. For shared leadership to emerge, the members of the team must offer leadership services, and the team as a whole must be willing to rely on leadership by multiple team members. For these individual and collective behaviors to occur, the team members must believe that offering influence to and accepting it from fellow team members are welcome and constructive actions. Leadership emerges when people with differing world views use dialogue and collaborative learning to create spaces where a shared common purpose can be achieved while a diversity of perspectives is preserved and valued. This study also suggests that this process can be supported by different kinds of meaning-making and process tools. Leadership, then, does not reside in a person or in a role, but in the social system. The built framework integrates the different dimensions of shared leadership and describes their relationships. This way, the findings of this study can be seen as a contribution to the understanding of what constitutes essential aspects of shared leadership in the team context that can be of theoretical value in terms of advancing the adoption and development process of shared leadership. In the real world, teams and organizations can create conditions to foster and facilitate the process. We should encourage leaders and team members to approach leadership as a collective effort that the team can be prepared for, so that the response is rapid and efficient.

Motivational Influences on Transfer: Dimensions and Boundary Conditions

The focus of this dissertation is the motivational influences on transfer in higher education and professional training contexts. To estimate these motivational influences, the dissertation includes seven individual studies that are structured in two parts. Part I, Dimensions, aims at identifying the dimensionality of motivation to transfer and its structural relations with training-related antecedents and outcomes. Part II, Boundary Conditions, aims at testing the predictive validity of motivation theories used in contemporary training research under different study conditions. Data in this dissertation was gathered from multi-item questionnaires, which were analyzed differently in Part I and Part II. Studies in Part I employed exploratory and confirmatory factor analysis, structural equation modeling, partial least squares (PLS) path modeling, and mediation analysis. Studies in Part II used artifact distribution meta-analysis, (nested) subgroup analysis, and weighted least squares (WLS) multiple regression. Results demonstrate that motivation to transfer can be conceptualized as a three-dimensional construct, including autonomous motivation to transfer, controlled motivation to transfer, and intention to transfer, given a theoretical framework informed by expectancy theory, self-determination theory, and the theory of planned behavior. Results also demonstrate that a range of boundary conditions moderates motivational influences on transfer. To test the predictive validity of expectancy theory, social cognitive theory, and the theory of goal orientations under different study settings, a total of 17 boundary conditions were meta-analyzed, including age; assessment criterion; assessment source; attendance policy; collaboration among trainees; computer support; instruction; instrument used to measure motivation; level of education; publication type; social training context; SS/SMC bias; study setting; survey modality; type of knowledge being trained; use of a control group; and work context. Together, the findings cumulated in this thesis support the basic premise that motivation is centrally important for transfer, but that motivational influences need to be understood from a more differentiated perspective than commonly found in the literature, in order to account for several dimensions and boundary conditions. The results of this dissertation across the seven individual studies are reflected in terms of their implications for theory development and their significance for training evaluation and the design of training environments. Limitations and directions to take in future research are discussed.

INSPIRE: contributions to Invenio from the leading High Energy Physics (HEP) platform

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014