Computer-assisted cartography using topographic properties: precision and accuracy of local soil maps in central Mexico

Map units directly related to properties of soil-landscape are generated by local soil classes. Therefore to take into consideration the knowledge of farmers is essential to automate the procedure. The aim of this study was to map local soil classes by computer-assisted cartography (CAC), using several combinations of topographic properties produced by GIS (digital elevation model, aspect, slope, and profile curvature). A decision tree was used to find the number of topographic properties required for digital cartography of the local soil classes. The maps produced were evaluated based on the attributes of map quality defined as precision and accuracy of the CAC-based maps. The evaluation was carried out in Central Mexico using three maps of local soil classes with contrasting landscape and climatic conditions (desert, temperate, and tropical). In the three areas the precision (56 %) of the CAC maps based on elevation as topographical feature was higher than when based on slope, aspect and profile curvature. The accuracy of the maps (boundary locations) was however low (33 %), in other words, further research is required to improve this indicator.

Digital soil mapping: strategy for data pre-processing

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

Structural assessment of single amino acid mutations: application to TP53 function.

Single amino acid substitution is the type of protein alteration most related to human diseases. Current studies seek primarily to distinguish neutral mutations from harmful ones. Very few methods offer an explanation of the final prediction result in terms of the probable structural or functional effect on the protein. In this study, we describe the use of three novel parameters to identify experimentally-verified critical residues of the TP53 protein (p53). The first two parameters make use of a surface clustering method to calculate the protein surface area of highly conserved regions or regions with high nonlocal atomic interaction energy (ANOLEA) score. These parameters help identify important functional regions on the surface of a protein. The last parameter involves the use of a new method for pseudobinding free-energy estimation to specifically probe the importance of residue side-chains to the stability of protein fold. A decision tree was designed to optimally combine these three parameters. The result was compared to the functional data stored in the International Agency for Research on Cancer (IARC) TP53 mutation database. The final prediction achieved a prediction accuracy of 70% and a Matthews correlation coefficient of 0.45. It also showed a high specificity of 91.8%. Mutations in the 85 correctly identified important residues represented 81.7% of the total mutations recorded in the database. In addition, the method was able to correctly assign a probable functional or structural role to the residues. Such information could be critical for the interpretation and prediction of the effect of missense mutations, as it not only provided the fundamental explanation of the observed effect, but also helped design the most appropriate laboratory experiment to verify the prediction results.

IFN Stimulated Gene Expression in the Liver is a Better Predictor of Treatment Response in Chronic Hepatitis C than the IL28B (IFN lambda 3) Genotype

BACKGROUND: Therapy of chronic hepatitis C (CHC) with pegIFNα/ribavirin achieves a sustained virologic response (SVR) in ∼55%. Pre-activation of the endogenous interferon system in the liver is associated with non-response (NR). Recently, genome-wide association studies described associations of allelic variants near the IL28B (IFNλ3) gene with treatment response and with spontaneous clearance of the virus. We investigated if the IL28B genotype determines the constitutive expression of IFN stimulated genes (ISGs) in the liver of patients with CHC. METHODS: We genotyped 93 patients with CHC for 3 IL28B single nucleotide polymorphisms (SNPs, rs12979860, rs8099917, rs12980275), extracted RNA from their liver biopsies and quantified the expression of IL28B and of 8 previously identified classifier genes which discriminate between SVR and NR (IFI44L, RSAD2, ISG15, IFI22, LAMP3, OAS3, LGALS3BP and HTATIP2). Decision tree ensembles in the form of a random forest classifier were used to calculate the relative predictive power of these different variables in a multivariate analysis. RESULTS: The minor IL28B allele (bad risk for treatment response) was significantly associated with increased expression of ISGs, and, unexpectedly, with decreased expression of IL28B. Stratification of the patients into SVR and NR revealed that ISG expression was conditionally independent from the IL28B genotype, i.e. there was an increased expression of ISGs in NR compared to SVR irrespective of the IL28B genotype. The random forest feature score (RFFS) identified IFI27 (RFFS = 2.93), RSAD2 (1.88) and HTATIP2 (1.50) expression and the HCV genotype (1.62) as the strongest predictors of treatment response. ROC curves of the IL28B SNPs showed an AUC of 0.66 with an error rate (ERR) of 0.38. A classifier with the 3 best classifying genes showed an excellent test performance with an AUC of 0.94 and ERR of 0.15. The addition of IL28B genotype information did not improve the predictive power of the 3-gene classifier. CONCLUSIONS: IL28B genotype and hepatic ISG expression are conditionally independent predictors of treatment response in CHC. There is no direct link between altered IFNλ3 expression and pre-activation of the endogenous system in the liver. Hepatic ISG expression is by far the better predictor for treatment response than IL28B genotype.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

Coarse Global Visual Localization of a Mobile Robot

Localization, which is the ability of a mobile robot to estimate its position within its environment, is a key capability for autonomous operation of any mobile robot. This thesis presents a system for indoor coarse and global localization of a mobile robot based on visual information. The system is based on image matching and uses SIFT features as natural landmarks. Features extracted from training images arestored in a database for use in localization later. During localization an image of the scene is captured using the on-board camera of the robot, features are extracted from the image and the best match is searched from the database. Feature matching is done using the k-d tree algorithm. Experimental results showed that localization accuracy increases with the number of training features used in the training database, while, on the other hand, increasing number of features tended to have a negative impact on the computational time. For some parts of the environment the error rate was relatively high due to a strong correlation of features taken from those places across the environment.

T&K-yhteistyöprojektinvalintakriteerit ja - päätösprosessi: Case VR Cargo

Tutkimuksen tavoitteena oli löytää yritysten välisten tutkimus- ja kehityshankkeiden valintaan liittyviä kriittisiä tekijöitä ja luoda T&K-verkostojen analysointiin soveltuva malli, jonka avulla tutkimuksen caseyritys VR Cargo voi tehdä päätöksiä panostuksistaan eri hankkeisiin ja ylipäätänsä siitä, missä kannattaa olla mukana sekä kuinka löytää strategisia kehityskumppaneita. Työn teoriaosuus tehtiin kirjallisuuskatsauksena aiempaan T&K-tutkimukseen ja empiria osuus suoritettiin tekemällä teemahaastatteluja VR Cargossa sekä kahdessa vertailuyrityksessä. Tutkimuksessa havaittiin useita erilaisia kriittisiä tekijöitä T&K-yhteistyöhankkeisiin liittyen. Yrityksen on mm. huomioitava T&K-hankkeen strateginen sopivuus ja lisäarvo, määriteltävä motiivit ja riskit, yrityskohtaisten tekijöiden ovat oltava kunnossa, on tiedettävä millainen on yritykselle hyvä ja sopiva partneri sekä yhteistyötason tekijät on huomioitava. Tutkimuksessa rakennettua hankkeiden valintaan liittyvää päätöspuumallia, ei ole tarkoitettu tekemään lopullista valintapäätöstä, vaan sen tarkoituksena on helpottaa ja tukea valintaprosessia ja alentaa näin todennäköisyyttä unohtaa jokin tärkeä seikka valintaprosessissa.

Clinical features for diagnosis of pneumonia in children younger than 5 years: a systematic review and meta-analysis.

BACKGROUND: Pneumonia is the biggest cause of deaths in young children in developing countries, but early diagnosis and intervention can effectively reduce mortality. We aimed to assess the diagnostic value of clinical signs and symptoms to identify radiological pneumonia in children younger than 5 years and to review the accuracy of WHO criteria for diagnosis of clinical pneumonia. METHODS: We searched Medline (PubMed), Embase (Ovid), the Cochrane Database of Systematic Reviews, and reference lists of relevant studies, without date restrictions, to identify articles assessing clinical predictors of radiological pneumonia in children. Selection was based on: design (diagnostic accuracy studies), target disease (pneumonia), participants (children aged <5 years), setting (ambulatory or hospital care), index test (clinical features), and reference standard (chest radiography). Quality assessment was based on the 2011 Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) criteria. For each index test, we calculated sensitivity and specificity and, when the tests were assessed in four or more studies, calculated pooled estimates with use of bivariate model and hierarchical summary receiver operation characteristics plots for meta-analysis. FINDINGS: We included 18 articles in our analysis. WHO-approved signs age-related fast breathing (six studies; pooled sensitivity 0·62, 95% CI 0·26-0·89; specificity 0·59, 0·29-0·84) and lower chest wall indrawing (four studies; 0·48, 0·16-0·82; 0·72, 0·47-0·89) showed poor diagnostic performance in the meta-analysis. Features with the highest pooled positive likelihood ratios were respiratory rate higher than 50 breaths per min (1·90, 1·45-2·48), grunting (1·78, 1·10-2·88), chest indrawing (1·76, 0·86-3·58), and nasal flaring (1·75, 1·20-2·56). Features with the lowest pooled negative likelihood ratio were cough (0·30, 0·09-0·96), history of fever (0·53, 0·41-0·69), and respiratory rate higher than 40 breaths per min (0·43, 0·23-0·83). INTERPRETATION: Not one clinical feature was sufficient to diagnose pneumonia definitively. Combination of clinical features in a decision tree might improve diagnostic performance, but the addition of new point-of-care tests for diagnosis of bacterial pneumonia would help to attain an acceptable level of accuracy. FUNDING: Swiss National Science Foundation.

Päätöspuun käyttö tutkamaalien initialisoinnissa

Suomen ilmatilaa valvotaan reaaliaikaisesti, pääasiassa ilmavalvontatutkilla. Ilmatilassa on lentokoneiden lisäksi paljon muitakin kohteita, jotka tutka havaitsee. Tutka lähettää nämä tiedot edelleen ilmavalvontajärjestelmään. Ilmavalvontajärjestelmä käsittelee tiedot, sekä lähettää ne edelleen esitysjärjestelmään. Esitysjärjestelmässä tiedot esitetään synteettisinä merkkeinä, seurantoina joista käytetään nimitystä träkki. Näiden tietojen puitteissa sekä oman ammattitaitonsa perusteella ihmiset tekevät päätöksiä. Tämän työn tarkoituksena on tutkia tutkan havaintoja träkkien initialisointipisteessä siten, että voitaisiin määritellä tyypillinen rakenne sille mikä on oikea ja mikä väärä tai huono träkki. Tämän lisäksi tulisi ennustaa, mitkä Irakeista eivät aiheudu ilma- aluksista. Saadut tulokset voivat helpottaa työtä havaintojen tulkinnassa - jokainen lintuparvi ei ole ehdokas seurannaksi. Havaintojen luokittelu voidaan tehdä joko neurolaskennalla tai päätöspuulla. Neurolaskenta tehdään neuroverkoilla, jotka koostuvat neuroneista. Päätöspuu- luokittelijat ovat oppivia tietorakenteita kuten neuroverkotkin. Yleisin päätöpuu on binääripuu. Tämän työn tavoitteena on opettaa päätöspuuluokittelija havaintojen avulla siten, että se pystyy luokittelemaan väärät havainnot oikeista. Neurolaskennan mahdollisuuksia tässä työssä ei käsitellä kuin teoreettisesti. Työn tuloksena voi todeta, että päätöspuuluokittelijat ovat erittäin kykeneviä erottamaan oikeat havainnot vääristä. Vaikka tulokset olivat rohkaiseva, lisää tutkimusta tarvitaan määrittelemään luotettavammin tekijät, jotka parhaiten suorittavat luokittelun.

ICA as a preprocessing technique for classification

In this paper we propose the use of the independent component analysis (ICA) [1] technique for improving the classification rate of decision trees and multilayer perceptrons [2], [3]. The use of an ICA for the preprocessing stage, makes the structure of both classifiers simpler, and therefore improves the generalization properties. The hypothesis behind the proposed preprocessing is that an ICA analysis will transform the feature space into a space where the components are independent, and aligned to the axes and therefore will be more adapted to the way that a decision tree is constructed. Also the inference of the weights of a multilayer perceptron will be much easier because the gradient search in the weight space will follow independent trajectories. The result is that classifiers are less complex and on some databases the error rate is lower. This idea is also applicable to regression

Training system for conceptual design and evaluation for wastewater treatment

Rising population, rapid urbanisation and growing industrialisation have severely stressed water quality and its availability in Malawi. In addition, financial and institutional problems and the expanding agro industry have aggravated this problem. The situation is worsened by depleting water resources and pollution from untreated sewage and industrial effluent. The increasing scarcity of clean water calls for the need for appropriate management of available water resources. There is also demand for a training system for conceptual design and evaluation for wastewater treatment in order to build the capacity for technical service providers and environmental practitioners in the country. It is predicted that Malawi will face a water stress situation by 2025. In the city of Blantyre, this situation is aggravated by the serious pollution threat from the grossly inadequate sewage treatment capacity. This capacity is only 23.5% of the wastewater being generated presently. In addition, limited or non-existent industrial effluent treatment has contributed to the severe water quality degradation. This situation poses a threat to the ecologically fragile and sensitive receiving water courses within the city. This water is used for domestic purposes further downstream. This manuscript outlines the legal and policy framework for wastewater treatment in Malawi. The manuscript also evaluates the existing wastewater treatment systems in Blantyre. This evaluation aims at determining if the effluent levels at the municipal plants conform to existing standards and guidelines and other associated policy and regulatory frameworks. The raw material at all the three municipal plants is sewage. The typical wastewater parameters are Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), and Total Suspended Solids (TSS). The treatment target is BOD5, COD, and TSS reduction. Typical wastewater parameters at the wastewater treatment plant at MDW&S textile and garments factory are BOD5 and COD. The treatment target is to reduce BOD5 and COD. The manuscript further evaluates a design approach of the three municipal wastewater treatment plants in the city and the wastewater treatment plant at Mapeto David Whitehead & Sons (MDW&S) textile and garments factory. This evaluation utilises case-based design and case-based reasoning principles in the ED-WAVE tool to determine if there is potential for the tool in Blantyre. The manuscript finally evaluates the technology selection process for appropriate wastewater treatment systems for the city of Blantyre. The criteria for selection of appropriate wastewater treatment systems are discussed. Decision support tools and the decision tree making process for technology selection are also discussed. Based on the treatment targets and design criteria at the eight cases evaluated in this manuscript in reference to similar cases in the ED-WAVE tool, this work confirms the practical use of case-based design and case-based reasoning principles in the ED-WAVE tool in the design and evaluation of wastewater treatment 6 systems in sub-Sahara Africa, using Blantyre, Malawi, as the case study area. After encountering a new situation, already collected decision scenarios (cases) are invoked and modified in order to arrive at a particular design alternative. What is necessary, however, is to appropriately modify the case arrived at through the Case Study Manager in order to come up with a design appropriate to the local situation taking into account technical, socio-economic and environmental aspects. This work provides a training system for conceptual design and evaluation for wastewater treatment.

Optimizing Customer Energy Invoice Calculation with Parallel Computation

Diplomityön tarkoituksena on optimoida asiakkaiden sähkölaskun laskeminen hajautetun laskennan avulla. Älykkäiden etäluettavien energiamittareiden tullessa jokaiseen kotitalouteen, energiayhtiöt velvoitetaan laskemaan asiakkaiden sähkölaskut tuntiperusteiseen mittaustietoon perustuen. Kasvava tiedonmäärä lisää myös tarvittavien laskutehtävien määrää. Työssä arvioidaan vaihtoehtoja hajautetun laskennan toteuttamiseksi ja luodaan tarkempi katsaus pilvilaskennan mahdollisuuksiin. Lisäksi ajettiin simulaatioita, joiden avulla arvioitiin rinnakkaislaskennan ja peräkkäislaskennan eroja. Sähkölaskujen oikeinlaskemisen tueksi kehitettiin mittauspuu-algoritmi.

Analyzing the Effects of the New Renewable Energy Policy in Russia on Investments into Wind, Solar and Small Hydro Power

This thesis presents an analysis of recently enacted Russian renewable energy policy based on capacity mechanism. Considering its novelty and poor coverage by academic literature, the aim of the thesis is to analyze capacity mechanism influence on investors’ decision-making process. The current research introduces a number of approaches to investment analysis. Firstly, classical financial model was built with Microsoft Excel® and crisp efficiency indicators such as net present value were determined. Secondly, sensitivity analysis was performed to understand different factors influence on project profitability. Thirdly, Datar-Mathews method was applied that by means of Monte Carlo simulation realized with Matlab Simulink®, disclosed all possible outcomes of investment project and enabled real option thinking. Fourthly, previous analysis was duplicated by fuzzy pay-off method with Microsoft Excel®. Finally, decision-making process under capacity mechanism was illustrated with decision tree. Capacity remuneration paid within 15 years is calculated individually for each RE project as variable annuity that guarantees a particular return on investment adjusted on changes in national interest rates. Analysis results indicate that capacity mechanism creates a real option to invest in renewable energy project by ensuring project profitability regardless of market conditions if project-internal factors are managed properly. The latter includes keeping capital expenditures within set limits, production performance higher than 75% of target indicators, and fulfilling localization requirement, implying producing equipment and services within the country. Occurrence of real option shapes decision-making process in the following way. Initially, investor should define appropriate location for a planned power plant where high production performance can be achieved, and lock in this location in case of competition. After, investor should wait until capital cost limit and localization requirement can be met, after that decision to invest can be made without any risk to project profitability. With respect to technology kind, investment into solar PV power plant is more attractive than into wind or small hydro power, since it has higher weighted net present value and lower standard deviation. However, it does not change decision-making strategy that remains the same for each technology type. Fuzzy pay-method proved its ability to disclose the same patterns of information as Monte Carlo simulation. Being effective in investment analysis under uncertainty and easy in use, it can be recommended as sufficient analytical tool to investors and researchers. Apart from described results, this thesis contributes to the academic literature by detailed description of capacity price calculation for renewable energy that was not available in English before. With respect to methodology novelty, such advanced approaches as Datar-Mathews method and fuzzy pay-off method are applied on the top of investment profitability model that incorporates capacity remuneration calculation as well. Comparison of effects of two different RE supporting schemes, namely Russian capacity mechanism and feed-in premium, contributes to policy comparative studies and exhibits useful inferences for researchers and policymakers. Limitations of this research are simplification of assumptions to country-average level that restricts our ability to analyze renewable energy investment region wise and existing limitation of the studying policy to the wholesale power market that leaves retail markets and remote areas without our attention, taking away medium and small investment into renewable energy from the research focus. Elimination of these limitations would allow creating the full picture of Russian renewable energy investment profile.

Finding Methods for Recognizing and Classifying Spare Part Criticality – Case Full Deposit Stripping Machines

The aim of this Master’s thesis is to find a method for classifying spare part criticality in the case company. Several approaches exist for criticality classification of spare parts. The practical problem in this thesis is the lack of a generic analysis method for classifying spare parts of proprietary equipment of the case company. In order to find a classification method, a literature review of various analysis methods is required. The requirements of the case company also have to be recognized. This is achieved by consulting professionals in the company. The literature review states that the analytic hierarchy process (AHP) combined with decision tree models is a common method for classifying spare parts in academic literature. Most of the literature discusses spare part criticality in stock holding perspective. This is relevant perspective also for a customer orientated original equipment manufacturer (OEM), as the case company. A decision tree model is developed for classifying spare parts. The decision tree classifies spare parts into five criticality classes according to five criteria. The criteria are: safety risk, availability risk, functional criticality, predictability of failure and probability of failure. The criticality classes describe the level of criticality from non-critical to highly critical. The method is verified for classifying spare parts of a full deposit stripping machine. The classification can be utilized as a generic model for recognizing critical spare parts of other similar equipment, according to which spare part recommendations can be created. Purchase price of an item and equipment criticality were found to have no effect on spare part criticality in this context. Decision tree is recognized as the most suitable method for classifying spare part criticality in the company.