Simulation of growth and development of irrigated cowpea in Piauí State by CROPGRO model

The objective of this work was to adapt the CROPGRO model, which is part of the DSSAT system, for simulating the cowpea (Vigna unguiculata) growth and development under soil and climate conditions of the Baixo Parnaíba region, Piauí State, Brazil. In the CROPGRO, only input parameters that define crop species, cultivars, and ecotype were changed in order to characterize the cowpea crop. Soil and climate files were created for the considered site. Field experiments without water deficit were used to calibrate the model. In these experiments, dry matter (DM), leaf area index (LAI), yield components and grain yield of cowpea (cv. BR 14 Mulato) were evaluated. The results showed good fit for DM and LAI estimates. The medium values of R² and medium absolute error (MAE) were, respectively, 0.95 and 264.9 kg ha-1 for DM, and 0.97 and 0.22 for LAI. The difference between observed and simulated values of plant phenology varied from 0 to 3 days. The model also presented good performance for yield components simulation, excluding 100-grain weight, for which the error ranged from 20.9% to 34.3%. Considering the medium values of crop yield in two years, the model presented an error from 5.6%.

Brazilian maize genotypes sensitivity to water deficit estimated through a simple crop yield model

The objective of this work was to determine the sensitivity of maize (Zea mays) genotypes to water deficit, using a simple agrometeorological crop yield model. Crop actual yield and agronomic data of 26 genotypes were obtained from the Maize National Assays carried out in ten locations, in four Brazilian states, from 1998 to 2006. Weather information for each experimental location and period were obtained from the closest weather station. Water deficit sensitivity index (Ky) was determined using the crop yield depletion model. Genotypes can be divided into two groups according to their resistance to water deficit. Normal resistance genotypes had Ky ranging from 0.4 to 0.5 in vegetative period, 1.4 to 1.5 in flowering, 0.3 to 0.6 in fruiting, and 0.1 to 0.3 in maturing period, whereas the higher resistance genotypes had lower values, respectively 0.2-0.4, 0.7-1.2, 0.2-0.4, and 0.1-0.2. The general Ky for the total growing season was 2.15 for sensitive genotypes and 1.56 for the resistant ones. Model performance was acceptable to evaluate crop actual yield, whose average errors estimated for each genotype ranged from -5.7% to +5.8%, and whose general mean absolute error was 960 kg ha-1 (10%).

Rice yield estimation based on weather conditions and on technological level of production systems in Brazil

The objective of this work was to evaluate an estimation system for rice yield in Brazil, based on simple agrometeorological models and on the technological level of production systems. This estimation system incorporates the conceptual basis proposed by Doorenbos & Kassam for potential and attainable yields with empirical adjusts for maximum yield and crop sensitivity to water deficit, considering five categories of rice yield. Rice yield was estimated from 2000/2001 to 2007/2008, and compared to IBGE yield data. Regression analyses between model estimates and data from IBGE surveys resulted in significant coefficients of determination, with less dispersion in the South than in the North and Northeast regions of the country. Index of model efficiency (E1') ranged from 0.01 in the lower yield classes to 0.45 in higher ones, and mean absolute error ranged from 58 to 250 kg ha‑1, respectively.

Development of a PC Tool for Estimating Pressure Drops in Forced-convection Boiler Tubes

Kaksifaasivirtauksen kuvaamiseen käytettävät mallit, ja menetelmät kaksifaasivirtauksen painehäviön määrittämiseksi kehittyvät yhä monimutkaisimmiksi. Höyrystinputkissa tapahtuvien painehäviöiden arvioinnin vaatiman laskennan suorittamiseksi tietokoneohjelman kehittäminen on välttämätöntä. Tässä työssä on kehitetty itsenäinen PC-ohjelma painehäviöiden arvioimiseksi pakotetulle konvektiovirtaukselle pystysuorissa höyrykattilan höyrystinputkissa. Veden ja vesihöyryn aineominaisuuksien laskentaan käytetään IAPWS-IF97 –yhtälökokoelmaa sekä muita tarvittavia IAPWS:n suosittelemia yhtälöitä. Höyrystinputkessa kulloinkin vallitsevan virtausmuodon määrittämiseen käytetään sovelluskelpoisia virtausmuotojen välisiä rajoja kuvaavia yhtälöitä. Ohjelmassa käytetään painehäviön määritykseen kirjallisuudessa julkaistuja yhtälöitä, virtausmuodosta riippuen, alijäähtyneelle virtaukselle, kupla-, tulppa- ja rengasvirtaukselle sekä tulistetun höyryn virtaukselle. Ohjelman laskemia painehäviöarvioita verrattiin kirjallisuudesta valittuihin mittaustuloksiin. Laskettujen painehäviöiden virhe vaihteli välillä –19.5 ja +23.9 %. Virheiden itseisarvojen keskiarvo oli 12.8 %.

Paperikoneen laatusäätöjärjestelmän uusinnan vaikutukset laatusäätöjen suorituskykyyn

Tutkimuksen kohteena olleen UPM-Kymmene Oyj Kajaanin tehtaan PK3:n laatusäätöjärjestelmä ja mittapalkki uusittiin, jolloin haluttiin selvittää uusinnan vaikutuksia laatusäätöjen suorituskykyyn ja paperin laatuun. Työn kirjallisessa osassa perehdyttiin paperinvalmistusprosessin osiin kyseisen sanomalehtipaperikoneen tapauksessa sekä keskeisimpiin paperin laatuominaisuuksiin liittyviin mittaus- ja säätölaitteisiin sekä niiden toimintaan. Seurattaviksi paperin laatusuureiksi valittiin neliömassa, kuivamassa, kosteus ja paksuus, jotka ovat sanomalehtipaperin tärkeimpiä online-mitattavia ominaisuuksia. Paperin laatusuureiden seurantaan käytetään erilaisia tunnuslukuja ja työkaluja, joita on esitelty tässä työssä. Laatusuureiden konesuuntaisen ja poikkisuuntaisen seurannan tunnusluvuksi valittiin yleisesti käytössä oleva 2σ-keskiarvohajonta. Säätöjen suorituskykyä seurattiin suorituskykykolmion ohjausmatkaindeksien (CTI) ja erosuureen integraalien (IAE) avulla. Kokeellisessa osassa kerättiin mittaustietoja sekä vanhan että uuden laatusäätöjärjestelmän aikana. Seurattavat ajotilanteet paperikoneella jaettiin stabiiliin ajoon ja muutostilanteisiin, jotka käsittävät katkot ja lajinvaihtotilanteet. Stabiilin ajon aikana selvitettiin laatusuureiden hajontojen ja säätöjen suorituskykyindeksien normaaleissa tasoissa tapahtuneet muutokset. Muutostilanteiden osalta haluttiin selvittää, nopeuttaako järjestelmäuusinta katkoista toipumista ja lajinvaihtoaikaa. Stabiilin ajon seurannasta saatujen tulosten perusteella neliömassan ja kuivamassan konesuuntaiset hajonnat kasvoivat järjestelmäuusinnan myötä, mutta kosteuden konesuuntaiset hajonnat pienenivät. Laatusuureiden poikkisuuntaisista hajonnoista neliömassan sekä kuivamassan hajonnat kasvoivat ja kosteuden sekä paksuuden hajonnat pienenivät joidenkin lajien osalta. Poikkisuuntaisten laatusuureiden, etenkin paksuuden, toipuminen katkon jälkeen nopeutui. Myös lajinvaihtoon kuluva aika lyheni poikkisuuntaisilla laatusuureilla. Muutostilanteiden konesuuntaisten hajontojen asettumisajat eivät juuri parantuneet.

Kohinan poisto spektrikuvista pehmeillä morfologisilla suodattimilla

Tämän diplomityön tavoitteena oli tutkia kohinan poistoa spektrikuvista käyttäen pehmeitä morfologisia suodattimia. Työssä painotettiin impulssimaisen kohinan suodattamista. Suodattimien toimintaa arvioitiin numeerisesti keskimääräisen itseisarvovirheen, neliövirheen sekä signaali-kohinasuhteen avulla ja visuaalisesti tarkastelemalla suodatettuja kuvia sekä niiden yksittäisiä spektritasoja. Käytettyjä suodatusmenetelmiä olivat suodatus kuvapisteittäin spektrin suunnassa, suodatus koko spektrissä sekä kuutiomenetelmä ja komponenteittainen suodatus. Suodatettavat kuvat sisälsivät joko suola ja pippuri- tai bittivirhekohinaa. Parhaimmat suodatustulokset sekä numeeristen virhekriteerien että visuaalisen tarkastelun perusteella saatiin komponenteittaisella sekä kuvapisteittäisellä menetelmällä. Työssä käytetyt menetelmät on esitetty algoritmimuodossa. Suodatinalgoritmien toteutukset ja suodatuskokeet tehtiin Matlab-ohjelmistolla.

Application of Neural Network Methods in the Prediction of Stock Markets

The purpose of the research is to define practical profit which can be achieved using neural network methods as a prediction instrument. The thesis investigates the ability of neural networks to forecast future events. This capability is checked on the example of price prediction during intraday trading on stock market. The executed experiments show predictions of average 1, 2, 5 and 10 minutes’ prices based on data of one day and made by two different types of forecasting systems. These systems are based on the recurrent neural networks and back propagation neural nets. The precision of the predictions is controlled by the absolute error and the error of market direction. The economical effectiveness is estimated by a special trading system. In conclusion, the best structures of neural nets are tested with data of 31 days’ interval. The best results of the average percent of profit from one transaction (buying + selling) are 0.06668654, 0.188299453, 0.349854787 and 0.453178626, they were achieved for prediction periods 1, 2, 5 and 10 minutes. The investigation can be interesting for the investors who have access to a fast information channel with a possibility of every-minute data refreshment.

Uma abordagem robusta de cálculo aplicada a um sistema potenciométrico de medida de concentrações hidrogeniônicas em soluções aquosas

The data analyzed in this work were generated following the methodology developed by Molina et al.(J. Electroanal. Chem., 1979) for the calibration of a potentiometric system of measurement of hydrogen-ion concentrations resulting from neutralizations, at 25 ºC, of acidic or alkaline solutions at constant ionic strength (0.1 mol.l-1) held with NaClO4. The observed data present a serious deviation in relation to the mathematical model derived from the Nernst equation, for pH values ranging from 3 to 11, where pH=-log[H+]. We show that the minimization of the sum of the absolute values of the residuals gives estimates that are not influenced by outlying values.

New approximation to the third-order density : application to the calculation of correlated multicenter indices

In this work we present the formulas for the calculation of exact three-center electron sharing indices (3c-ESI) and introduce two new approximate expressions for correlated wave functions. The 3c-ESI uses the third-order density, the diagonal of the third-order reduced density matrix, but the approximations suggested in this work only involve natural orbitals and occupancies. In addition, the first calculations of 3c-ESI using Valdemoro's, Nakatsuji's and Mazziotti's approximation for the third-order reduced density matrix are also presented for comparison. Our results on a test set of molecules, including 32 3c-ESI values, prove that the new approximation based on the cubic root of natural occupancies performs the best, yielding absolute errors below 0.07 and an average absolute error of 0.015. Furthemore, this approximation seems to be rather insensitive to the amount of electron correlation present in the system. This newly developed methodology provides a computational inexpensive method to calculate 3c-ESI from correlated wave functions and opens new avenues to approximate high-order reduced density matrices in other contexts, such as the contracted Schrödinger equation and the anti-Hermitian contracted Schrödinger equation

Hydrological simulation using SWAT model in headwater basin in Southeast Brazil

Hydrological models are important tools that have been used in water resource planning and management. Thus, the aim of this work was to calibrate and validate in a daily time scale, the SWAT model (Soil and Water Assessment Tool) to the watershed of the Galo creek , located in Espírito Santo State. To conduct the study we used georeferenced maps of relief, soil type and use, in addition to historical daily time series of basin climate and flow. In modeling were used time series corresponding to the periods Jan 1, 1995 to Dec 31, 2000 and Jan 1, 2001 to Dec 20, 2003 for calibration and validation, respectively. Model performance evaluation was done using the Nash-Sutcliffe coefficient (E NS) and the percentage of bias (P BIAS). SWAT evaluation was also done in the simulation of the following hydrological variables: maximum and minimum annual daily flowsand minimum reference flows, Q90 and Q95, based on mean absolute error. E NS and P BIAS were, respectively, 0.65 and 7.2% and 0.70 and 14.1%, for calibration and validation, indicating a satisfactory performance for the model. SWAT adequately simulated minimum annual daily flow and the reference flows, Q90 and Q95; it was not suitable in the simulation of maximum annual daily flows.

Étude pharmacogénomique sur l’utilisation de la warfarine en pratique clinique réelle

Contexte: Bien que plusieurs algorithmes pharmacogénétiques de prédiction de doses de warfarine aient été publiés, peu d’études ont comparé la validité de ces algorithmes en pratique clinique réelle. Objectif: Évaluer trois algorithmes pharmacogénomiques dans une population de patients qui initient un traitement à la warfarine et qui souffrent de fibrillation auriculaire ou de problèmes de valves cardiaques. Analyser la performance des algorithmes de Gage et al., de Michaud et al. ainsi que de l’IWPC quant à la prédiction de la dose de warfarine permettant d’atteindre l’INR thérapeutique. Méthodes: Un devis de cohorte rétrospectif fut utilisé afin d’évaluer la validité des algorithmes chez 605 patients ayant débuté une thérapie de warfarine à l’Institut de Cardiologie de Montréal. Le coefficient de corrélation de Pearson ainsi que l’erreur absolue moyenne ont été utilisés pour évaluer la précision des algorithmes. L’exactitude clinique des prédictions de doses fut évaluée en calculant le nombre de patients pour qui la dose prédite était sous-estimée, idéalement estimée ou surestimée. Enfin, la régression linéaire multiple a été utilisée pour évaluer la validité d’un modèle de prédiction de doses de warfarine obtenu en ajoutant de nouvelles covariables. Résultats : L’algorithme de Gage a obtenu la proportion de variation expliquée la plus élevée (R2 ajusté = 44 %) ainsi que la plus faible erreur absolue moyenne (MAE = 1.41 ± 0.06). De plus, la comparaison des proportions de patients ayant une dose prédite à moins de 20 % de la dose observée a confirmé que l’algorithme de Gage était également le plus performant. Conclusion : Le modèle publié par Gage en 2008 est l’algorithme pharmacogénétique le plus exact dans notre population pour prédire des doses thérapeutiques de warfarine.

A Comparative Study of Convective Parameterization Schemes in WRF-NMM Model

Severe local storms, including tornadoes, damaging hail and wind gusts, frequently occur over the eastern and northeastern states of India during the pre-monsoon season (March-May). Forecasting thunderstorms is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent non-linearity of their dynamics and physics. In this paper, sensitivity experiments are conducted with the WRF-NMM model to test the impact of convective parameterization schemes on simulating severe thunderstorms that occurred over Kolkata on 20 May 2006 and 21 May 2007 and validated the model results with observation. In addition, a simulation without convective parameterization scheme was performed for each case to determine if the model could simulate the convection explicitly. A statistical analysis based on mean absolute error, root mean square error and correlation coefficient is performed for comparisons between the simulated and observed data with different convective schemes. This study shows that the prediction of thunderstorm affected parameters is sensitive to convective schemes. The Grell-Devenyi cloud ensemble convective scheme is well simulated the thunderstorm activities in terms of time, intensity and the region of occurrence of the events as compared to other convective schemes and also explicit scheme

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Regional Differences in the Prediction of Extratropical Cyclones by the ECMWF Ensemble Prediction System

A regional study of the prediction of extratropical cyclones by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) has been performed. An objective feature-tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast error statistics have then been produced for the position, intensity, and propagation speed of the storms. In previous work, data limitations meant it was only possible to present the diagnostics for the entire Northern Hemisphere (NH) or Southern Hemisphere. A larger data sample has allowed the diagnostics to be computed separately for smaller regions around the globe and has made it possible to explore the regional differences in the prediction of storms by the EPS. Results show that in the NH there is a larger ensemble mean error in the position of storms over the Atlantic Ocean. Further analysis revealed that this is mainly due to errors in the prediction of storm propagation speed rather than in direction. Forecast storms propagate too slowly in all regions, but the bias is about 2 times as large in the NH Atlantic region. The results show that storm intensity is generally overpredicted over the ocean and underpredicted over the land and that the absolute error in intensity is larger over the ocean than over the land. In the NH, large errors occur in the prediction of the intensity of storms that originate as tropical cyclones but then move into the extratropics. The ensemble is underdispersive for the intensity of cyclones (i.e., the spread is smaller than the mean error) in all regions. The spatial patterns of the ensemble mean error and ensemble spread are very different for the intensity of cyclones. Spatial distributions of the ensemble mean error suggest that large errors occur during the growth phase of storm development, but this is not indicated by the spatial distributions of the ensemble spread. In the NH there are further differences. First, the large errors in the prediction of the intensity of cyclones that originate in the tropics are not indicated by the spread. Second, the ensemble mean error is larger over the Pacific Ocean than over the Atlantic, whereas the opposite is true for the spread. The use of a storm-tracking approach, to both weather forecasters and developers of forecast systems, is also discussed.

Forecast calibration and combination: a simple Bayesian approach for ENSO

This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST Niño-3.4 index forecasts started at the end of the preceding July (5-month lead time). The empirical forecasts were obtained by linear regression between December and the preceding July Niño-3.4 index values over the period 1950–2001. Coupled model ensemble forecasts for the period 1987–99 were provided by ECMWF, as part of the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) project. Empirical and raw coupled model ensemble forecasts alone have similar mean absolute error forecast skill score, compared to climatological forecasts, of around 50% over the period 1987–99. The combined forecast gives an increased skill score of 74% and provides a well-calibrated and reliable estimate of forecast uncertainty.