Kysynnän ennustaminen leipomotuotteille ja ennusteen tarkkuuden arviointi

Tässä diplomityössä tutkittiin kysynnän ennustamista Vaasan & Vaasan Oy:n tuotteille. Ensin työssä perehdyttiin ennustamiseen ja sen tarjoamiin mahdollisuuksiin yrityksessä. Erityisesti kysynnän ennustamisesta saatavat hyödyt käytiin läpi. Kysynnän ennustamisesta haettiin ratkaisua erityisesti ongelmiin työvuorosuunnittelussa.Työssä perehdyttiin ennustemenetelmiin liittyvään kirjallisuuteen, jonka oppien perusteella tehtiin koe-ennustuksia yrityksen kysynnän historiadatan avulla. Koe-ennustuksia tehtiin kuudelle eri Turun leipomon koe-tuotteelle. Ennustettavana aikavälinä oli kahden viikon päiväkohtainen kysyntä. Tämän aikavälin erityisesti peruskysynnälle etsittiin ennustetarkkuudeltaan parasta kvantitatiivista ennustemenetelmää. Koe-ennustuksia tehtiin liukuvilla keskiarvoilla, klassisella aikasarja-analyysillä, eksponentiaalisen tasoituksen menetelmällä, Holtin lineaarisella eksponenttitasoituksen menetelmällä, Wintersin kausittaisella eksponentiaalisella tasoituksella, autoregressiivisillä malleilla, Box-Jenkinsin menetelmällä ja regressioanalyysillä. Myös neuroverkon opettamista historiadatalla ja käyttämistä ongelman ratkaisun apuna kokeiltiin.Koe-ennustuksien tulosten perusteella ennustemenetelmien toimintaa analysoitiin jatkokehitystä varten. Ennustetarkkuuden lisäksi arvioitiin mallin yksinkertaisuutta, helppokäyttöisyyttä ja sopivuutta yrityksen monien tuotteiden ennustamiseen. Myös kausivaihteluihin, trendeihin ja erikoispäiviin kiinnitettiin huomiota. Ennustetarkkuuden huomattiin parantuvan selvästi peruskysyntää ennustettaessa, jos ensin historiadata esikäsittelemällä puhdistettiin erikoispäivistä ja –viikoista.

Biomarkers of Host Response Predict Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study.

BACKGROUND: Diagnosing pediatric pneumonia is challenging in low-resource settings. The World Health Organization (WHO) has defined primary end-point radiological pneumonia for use in epidemiological and vaccine studies. However, radiography requires expertise and is often inaccessible. We hypothesized that plasma biomarkers of inflammation and endothelial activation may be useful surrogates for end-point pneumonia, and may provide insight into its biological significance. METHODS: We studied children with WHO-defined clinical pneumonia (n = 155) within a prospective cohort of 1,005 consecutive febrile children presenting to Tanzanian outpatient clinics. Based on x-ray findings, participants were categorized as primary end-point pneumonia (n = 30), other infiltrates (n = 31), or normal chest x-ray (n = 94). Plasma levels of 7 host response biomarkers at presentation were measured by ELISA. Associations between biomarker levels and radiological findings were assessed by Kruskal-Wallis test and multivariable logistic regression. Biomarker ability to predict radiological findings was evaluated using receiver operating characteristic curve analysis and Classification and Regression Tree analysis. RESULTS: Compared to children with normal x-ray, children with end-point pneumonia had significantly higher C-reactive protein, procalcitonin and Chitinase 3-like-1, while those with other infiltrates had elevated procalcitonin and von Willebrand Factor and decreased soluble Tie-2 and endoglin. Clinical variables were not predictive of radiological findings. Classification and Regression Tree analysis generated multi-marker models with improved performance over single markers for discriminating between groups. A model based on C-reactive protein and Chitinase 3-like-1 discriminated between end-point pneumonia and non-end-point pneumonia with 93.3% sensitivity (95% confidence interval 76.5-98.8), 80.8% specificity (72.6-87.1), positive likelihood ratio 4.9 (3.4-7.1), negative likelihood ratio 0.083 (0.022-0.32), and misclassification rate 0.20 (standard error 0.038). CONCLUSIONS: In Tanzanian children with WHO-defined clinical pneumonia, combinations of host biomarkers distinguished between end-point pneumonia, other infiltrates, and normal chest x-ray, whereas clinical variables did not. These findings generate pathophysiological hypotheses and may have potential research and clinical utility.

Soil quality assessment through a multi-approach analysis in soils of abandoned terraced land in NE Spain

The abandonment of agricultural land in mountainous areas has been an outstanding problem along the last century and has captured the attention of scientists, technicians and administrations, for the dramatic consequences sometimes occurred due to soil instability, steep slopes, rainfall regimes and wildfires. Hidromorfological and pedological alterations causing exceptional floods and accelerated erosion processes has therefore been studied, identifying the cause in the loss of landscape heterogeneity. Through the disappearance of agricultural works and drainage maintenance, slope stability has resulted severely affected. The mechanization of agriculture has caused the displacement of vines, olives and corks trees cultivation in terraced areas along the Mediterranean catchment towards more economically suitable areas. On the one hand, land use and management changes have implicated sociological changes as well, transforming areas inhabited by agricultural communities into deserted areas where the colonization of disorganized spontaneous vegetation has buried a valuable rural patrimony. On the other hand, lacking of planning and management of the abandoned areas has produced badlands and infertile soils due to wildfire and high erosion rates strongly degrading the whole ecosystems. In other cases, after land abandonment a process of soil regeneration has been recorded. Investigations have been conducted in a part of NE Spain where extended areas of terraced soils previously cultivated have been abandoned in the last century. The selected environments were semi-abandoned vineyards, semi-abandoned olive groves, abandoned stands of cork trees, abandoned stands of pine trees, scrubland of Cistaceaea, scrubland of Ericaceaea, and pasture. The research work was focused on the study of most relevant physical, chemical and biological soil properties, as well as runoff and erosion under soils with different plant cover to establish the abandonment effect on soil quality, due to the peculiarity and vulnerability of these soils with a much reduced depth. The period of observation was carried out from autumn 2009 to autumn 2010. The sediment concentration of soil erosion under vines was recorded as 34.52 g/l while under pasture it was 4.66 g/l. In addition, the soil under vines showed the least amount of organic matter, which was 12 times lower than all other soil environments. The carbon dioxide (CO2) and total glomalin (TG) ratio to soil organic carbon (SOC) in this soil was 0.11 and 0.31 respectively. However, the soil under pasture contained a higher amount of organic matter and showed that the CO2 and TG ratio to SOC was 0.02 and 0.11 respectively indicating that the soil under pasture better preserves the soil carbon pool. A similar trend was found in the intermediate soils in the sequence of land use change and abandonment. Soil structural stability increased in the two soil fractions investigated (0.25-2.00 mm, 2.0-5.6 mm) especially in those soils that did not undergo periodical perturbations like wildfires. Soil quality indexes were obtained by using relevant physical and chemical soil parameters. Factor analysis carried out to study the relationship between all soil parameters allowed to related variables and environments and identify those areas that better contribute to soil quality towards others that may need more attention to avoid further degradation processes

Psychomotor Development of 18-Months Children With Orofacial Clefts

Objective - To describe the global and language development of children with cleft palate or cleft lip and palate at the age of 18 months, and to evaluate whether the type of cleft has an impact on psychomotor development. Study Design - Prospective cohort study. Settings - Tertiary care hospital Patients - All children born between December 2002 and November 2009 with an orofacial cleft, operated and seen at the developmental unit (UD) of the same hospital at the age of 18 months. Outcome Measures - Developmental quotients of the Griffiths Mental Development Scale and the French Communicative Development Inventory (IFDC) were used to assess the overall and language development of the children. Statistics- The population characteristics were described with means for continuous variables, and frequencies for binary or categorical variables. Chi-squared and regression analysis were used to analyse the results. Results - 69 children with clefts were examined at the age of 18 months with the IFDC and the Griffith test. The results showed that there was no significant difference in the test results of language development and global psychomotor development between the children with different types of clefts, and all were within the normal range. Conclusion - Psychomotor development is not affected by orofacial clefts, and there is no difference between children with cleft palate or cleft lip and palate.

Combining Advanced Forecasting Methods with System Dynamics - the Case of Finnish Seaports

Seaports play an important part in the wellbeing of a nation. Many nations are highly dependent on foreign trade and most trade is done using sea vessels. This study is part of a larger research project, where a simulation model is required in order to create further analyses on Finnish macro logistical networks. The objective of this study is to create a system dynamic simulation model, which gives an accurate forecast for the development of demand of Finnish seaports up to 2030. The emphasis on this study is to show how it is possible to create a detailed harbor demand System Dynamic model with the help of statistical methods. The used forecasting methods were ARIMA (autoregressive integrated moving average) and regression models. The created simulation model gives a forecast with confidence intervals and allows studying different scenarios. The building process was found to be a useful one and the built model can be expanded to be more detailed. Required capacity for other parts of the Finnish logistical system could easily be included in the model.

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.

Analysis of laboratory intercomparison data: a matter of independence

When laboratory intercomparison exercises are conducted, there is no a priori dependence of the concentration of a certain compound determined in one laboratory to that determined by another(s). The same applies when comparing different methodologies. A existing data set of total mercury readings in fish muscle samples involved in a Brazilian intercomparison exercise was used to show that correlation analysis is the most effective statistical tool in this kind of experiments. Problems associated with alternative analytical tools such as mean or paired 't'-test comparison and regression analysis are discussed.

Tilastollinen tutkimus ohjelmiston laatuun vaikuttavista tekijöistä

Tämä työ on tehty osana MASTO-tutkimushanketta, jonka tarkoituksena on kehittää ohjelmistotestauksen adaptiivinen referenssimalli. Työ toteutettiin tilastollisena tutkimuksena käyttäen survey-menetelmää. Tutkimuksessa haastateltiin 31 organisaatioyksikköä eri puolelta suomea, jotka tekevät keskikriittisiä sovelluksia. Tutkimuksen hypoteeseina oli laadun riippuvuus ohjelmistokehitysmenetelmästä, asiakkaan osallistumisesta, standardin toteutumisesta, asiakassuhteesta, liiketoimintasuuntautuneisuudesta, kriittisyydestä, luottamuksesta ja testauksen tasosta. Hypoteeseista etsittiin korrelaatiota laadun kanssa tekemällä korrelaatio ja regressioanalyysi. Lisäksi tutkimuksessa kartoitettiin minkälaisia ohjelmistokehitykseen liittyviä käytäntöjä, menetelmiä ja työkaluja organisaatioyksiköissä käytettiin, ongelmia ja parannusehdotuksia liittyen ohjelmistotestaukseen, merkittävimpiä tapoja asiakkaan vaikuttamiseksi ohjelmiston laatuun sekä suurimpia hyötyjä ja haittoja ohjelmistokehityksen tai testauksen ulkoistamisessa. Tutkimuksessa havaittiin, että laatu korreloi positiivisesti ja tilastollisesti merkitsevästi testauksen tason, standardin toteutumisen, asiakasosallistumisen suunnitteluvaiheessa sekä asiakasosallistumisen ohjaukseen kanssa, luottamuksen ja yhden asiakassuhteeseen liittyvän osakysymyksen kanssa. Regressioanalyysin perusteella muodostettiin regressioyhtälö, jossa laadun todettiin positiivisesti riippuvan standardin toteutumisesta, asiakasosallistumisesta suunnitteluvaiheessa sekä luottamuksesta.

Desenvolvimento e validação de método de análise de glifosato em grãos de soja

The main objective of this work is to develop an efficient procedure to determine glyphosate in soybean grains. The cleanup of the aqueous extracts was done in two steps, beginning with liquid-liquid partitioning and then solid-phase extraction with anion exchange resin. After derivatization with a mixture of trifluoroacetic anhydride (TFAA) and trifluoroethanol (TFE), quantification was done by gas chromatography coupled to mass spectrometry. The mean recovery and RSD of the spiked samples were, respectively, 80.5% and 3.1% at level 0.200 mg kg-1, 93.3% and 18.7% at level 0.500 mg kg-1 and 92% and 3.5% at level 1.000 mg kg-1. The method was linear in the working range (correlation coefficient = 0.9965).

Ratiomuuttujat ja näennäinen korrelaatio

Eri tieteenalojen tutkijat ovat kiistelleet jo yli vuosisadan ajan ratiomuodossa olevien muuttujien käytön vaikutuksista korrelaatio- ja regressioanalyysien tuloksiin ja niiden oikeaan tulkintaan. Strategiatutkimuksen piirissä aiheeseen ei ole kuitenkaan kiinnitetty suuresti huomiota. Tämä on yllättävää, sillä ratiomuuttujat ovat hyvin yleisesti käytettyjä empiirisen strategiatutkimuksen piirissä. Tässä työssä luodaan katsaus ratiomuuttujien ympärillä käytyyn debattiin. Lisäksi selvitetään artikkelikatsauksen avulla niiden käytön yleisyyttä nykypäivän strategiatutkimuksessa. Työssä tutkitaan Monte Carlo –simulaatioiden avulla ratiomuuttujien ominaisuuksien vaikutuksia korrelaatio- ja regressioanalyysin tuloksiin erityisesti yhteisen nimittäjän tapauksissa.

Previsão das propriedades físicas do papel kraft por espectroscopia no infravermelho próximo (NIR) e regressão por mínimos quadrados parciais (PLS)

The main objective of the present work is represented by the characterization of the physical properties of industrial kraft paper (i.e. transversal and longitudinal tear resistance, transversal traction resistance, bursting or crack resistance, longitudinal and transversal compression resistance (SCT (Compressive Strength Tester) and compression resistance (RCT-Ring Crush Test)) by near infrared spectroscopy associated to partial least squares regression. Several multivariate models were developed, many of them with high prevision capacity. In general, low prevision errors were observed and regression coefficients that are comparable with those provided by conventional standard methodologies.

Ostotapahtuman ennustemallin kehittäminen ajoneuvomarkkinoinnin tueksi

Perinteisesti ajoneuvojen markkinointikampanjoissa kohderyhmät muodostetaan yksinkertaisella kriteeristöllä koskien henkilön tai hänen ajoneuvonsa ominaisuuksia. Ennustavan analytiikan avulla voidaan tuottaa kohderyhmänmuodostukseen teknisesti kompleksisia mutta kuitenkin helppokäyttöisiä menetelmiä. Tässä työssä on sovellettu luokittelu- ja regressiomenetelmiä uuden auton ostajien joukkoon. Tämän työn menetelmiksi on rajattu tukivektorikone sekä Coxin regressiomalli. Coxin regression avulla on tutkittu elinaika-analyysien soveltuvuutta ostotapahtuman tapahtumahetken mallintamiseen. Luokittelu tukivektorikonetta käyttäen onnistuu tehtävässään noin 72% tapauksissa. Tukivektoriregressiolla mallinnetun hankintahetken virheen keskiarvo on noin neljä kuukautta. Työn tulosten perusteella myös elinaika-analyysin käyttö ostotapahtuman tapahtumahetken mallintamiseen on menetelmänä käyttökelpoinen.

Sensitivity reduction in Blumeria graminis f. sp. hordei to triadimenol fungicide applied as barley seed treatment

Experiments were carried out in a growth chamber with controlled temperature and photoperiod to test two populations of Blumeria graminis f. sp. hordei from Guarapuava, Paraná State, and Passo Fundo, Rio Grande do Sul State, Brazil. Treatments consisted in application of the fungicide triadimenol (Baytan 150 SC®) at three rates of its commercial formulation: 150, 250, 350 mL/100 Kg barley seeds. The experiments were conducted separately in a growth chamber for each population, adopting the same temperature and photoperiod. For inoculation, pots containing barley seedlings colonized by the fungus were placed among the plots. After emergence of the first symptoms, the disease severity was assessed at two-day intervals. The experiments were repeated twice for each fungus population. Data were expressed as area under the disease progress curve and as powdery mildew control by comparing the severity after the fungicide treatments to that of control. Data were subjected to analysis of variance and regression analysis; the area under the disease progress curve was also calculated. Comparing the data obtained in the present study with those reported in the literature and the control, the maximum value of 26.1% is considered insufficient to prevent the damages caused by the disease. The control response to the fungicide rate was significant. We can conclude that there was a reduction in the sensitivity of both B. graminis f.sp. hordei populations to the fungicide triadimenol, which explains the control failure observed in barley farms.

Stock Repurchase Authorizations - Finnish Companies

The aim of this study is to examine the abnormal market reaction caused by share repurchase authorizations. We study this abnormal reaction from five different angles. First four concentrate on average abnormal returns while the fifth concentrates on cumulative abnormal return. Data consists of 508 share repurchase authorization from Finnish stock market. Event study methodology is used to examine the stock price reaction and regression analysis is used to find correlation between actual buybacks and abnormal returns. The empirical results show that markets do usually react positively to share repurchase authorizations. There are some differences depending which of the five angles the abnormal returns are being examined. Statistically we can confirm that some authorization give positive reaction while others do not. Also we didn’t find a statistically significant positive correlation between actual buybacks and abnormal returns.

Fraud detection in the banking sector : a multi-agent approach

Fraud is an increasing phenomenon as shown in many surveys carried out by leading international consulting companies in the last years. Despite the evolution of electronic payments and hacking techniques there is still a strong human component in fraud schemes. Conflict of interest in particular is the main contributing factor to the success of internal fraud. In such cases anomaly detection tools are not always the best instruments, since the fraud schemes are based on faking documents in a context dominated by lack of controls, and the perpetrators are those ones who should control possible irregularities. In the banking sector audit team experts can count only on their experience, whistle blowing and the reports sent by their inspectors. The Fraud Interactive Decision Expert System (FIDES), which is the core of this research, is a multi-agent system built to support auditors in evaluating suspicious behaviours and to speed up the evaluation process in order to detect or prevent fraud schemes. The system combines Think-map, Delphi method and Attack trees and it has been built around audit team experts and their needs. The output of FIDES is an attack tree, a tree-based diagram to ”systematically categorize the different ways in which a system can be attacked”. Once the attack tree is built, auditors can choose the path they perceive as more suitable and decide whether or not to start the investigation. The system is meant for use in the future to retrieve old cases in order to match them with new ones and find similarities. The retrieving features of the system will be useful to simplify the risk management phase, since similar countermeasures adopted for past cases might be useful for present ones. Even though FIDES has been built with the banking sector in mind, it can be applied in all those organisations, like insurance companies or public organizations, where anti-fraud activity is based on a central anti-fraud unit and a reporting system.