Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 Elsevier Inc. All rights reserved.

PK/DB: database for pharmacokinetic properties and predictive in silico ADME models

The study of pharmacokinetic properties (PK) is of great importance in drug discovery and development. In the present work, PK/DB (a new freely available database for PK) was designed with the aim of creating robust databases for pharmacokinetic studies and in silico absorption, distribution, metabolism and excretion (ADME) prediction. Comprehensive, web-based and easy to access, PK/DB manages 1203 compounds which represent 2973 pharmacokinetic measurements, including five models for in silico ADME prediction (human intestinal absorption, human oral bioavailability, plasma protein binding, bloodbrain barrier and water solubility).

Intrinsically Multivariate Predictive Genes

Canalizing genes possess such broad regulatory power, and their action sweeps across a such a wide swath of processes that the full set of affected genes are not highly correlated under normal conditions. When not active, the controlling gene will not be predictable to any significant degree by its subject genes, either alone or in groups, since their behavior will be highly varied relative to the inactive controlling gene. When the controlling gene is active, its behavior is not well predicted by any one of its targets, but can be very well predicted by groups of genes under its control. To investigate this question, we introduce in this paper the concept of intrinsically multivariate predictive (IMP) genes, and present a mathematical study of IMP in the context of binary genes with respect to the coefficient of determination (CoD), which measures the predictive power of a set of genes with respect to a target gene. A set of predictor genes is said to be IMP for a target gene if all properly contained subsets of the predictor set are bad predictors of the target but the full predictor set predicts the target with great accuracy. We show that logic of prediction, predictive power, covariance between predictors, and the entropy of the joint probability distribution of the predictors jointly affect the appearance of IMP genes. In particular, we show that high-predictive power, small covariance among predictors, a large entropy of the joint probability distribution of predictors, and certain logics, such as XOR in the 2-predictor case, are factors that favor the appearance of IMP. The IMP concept is applied to characterize the behavior of the gene DUSP1, which exhibits control over a central, process-integrating signaling pathway, thereby providing preliminary evidence that IMP can be used as a criterion for discovery of canalizing genes.

The illusion of creation of the text

Is it really possible to create a (literary) text? It is actually impossible as to create an authentic text we need an authentic context which is impossible to create. So all the literary txets we have are not authentic and are not created authentically.

"Att gå med det i huvudet"-vägen från text till föreställning. : En scenografs arbetsprocess

Jag har försökt att sammanställa dels en enskild scenografs; Calle von Gegerfelts arbetsprocess i successiva steg, för att se hur denne arbetar fram en scenografi, dels har jag i en tabell försökt sammanställa flera scenografers arbetsprocesser för att se om det finns steg som är lika, och som gäller för flera scenografer. I resultatet av dessa sammanställningar har jag kunnat se hur von Gegerfelt steg för steg arbetar fram sin scenografi ända fram till teaterhändelsen. Det har framkommit att han har en lång process med rent tankearbete där omedvetna processer är tydliga och hur dessa successivt övergår till mer konkret arbete med händerna, för att till sist lämna över själva byggandet av scenografin och sömnaden av kläderna till andra. Vissa av dessa steg har han gemensamt med andra scenografer, men dessa har beskrivit de stegen med andra ord och på annat sätt, men man kan se att de hör ihop.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Hur fyra lärare arbetar med textsamtal i årskurs 4-6 : En studie om hur fyra lärare arbetar med textsamtal i syfte att bearbeta en text, samt vilka förmågor som kan utvecklas hos eleverna.

Syfte: Syftet med denna studie var att undersöka hur fyra lärare i grundskolan årskurs 4-6 arbetar med textsamtal i svenskundervisningen. Syftet var också att lyfta fram de förmågor som elever kan utveckla vid textsamtal samt pedagogens betydelse för textsamtalets genomförande. Utifrån syftet har följande frågeställningar formulerats. Dessa är: 1. Hur arbetar fyra lärare med textsamtal i svenskundervisningen i årskurs 4-6? 2. Vilken kunskap bör pedagogen ha för att kunna genomföra samtal om texter enligt de deltagande lärarna? 3. Hur vanligt förekommande är samtal om texter i grundskolan årskurs 4-6 rent generellt enligt de deltagande lärarna? 4. Vilka förmågor anser de intervjuade lärarna att elever kan utveckla vid samtal om texter? Metod: Den metod som har använts för att genomföra denna empiriska studie är observation och intervju. Urvalet som gjordes var att begränsa observationerna till fyra lektionstillfällen med fyra olika lärare. Den intervjuform som kändes mest lämplig att använda för detta ändamål var en halvstrukturerad ansats med ett kvalitativt utgångsläge. Resultat och analys: Resultatet visar att de fyra deltagande lärarna har en medvetenhet kring vad ett textsamtal innebär. Denna kunskap är en följd av att lärarna helt eller delvis använder sig av ett färdigt arbetsmaterial i undervisningen. I materialet ingår strukturerade textsamtal. Den slutsats som kan göras efter att ha genomfört både observationer och intervjuer, är att endast en lärare säger sig arbeta medvetet, regelbundet och strukturerat med textsamtal i undervisningen. Det var dock inget som observerades under lektionen jag deltog i. En av lärarna säger också att hon arbetar medvetet och strukturerat med aktiviteten men menar att det är svårt att få tiden att räcka till. De övriga två arbetar inte med textsamtal i undervisningen men den ena läraren uppger att hon använder sig av öppna frågor rent generellt i undervisningen. Ett tryggt klassrumsklimat, goda ämneskunskaper, kunskap i att ställa rätt frågor samt att vara förberedd är förmågor som benämns som betydelsefulla för att textsamtal ska kunna äga rum. Ingen av lärarna uppger att de tror att textsamtal är vanligt förekommande i skolan. Samtliga intervjuade lärare uppger ett antal förmågor som de menar att eleverna kan utveckla vid samtal om texter. Dessa är förmågan att kommunicera, förmågan att lyssna på andra, förmågan att uttrycka egna åsikter samt att kunna reflektera, analysera och bygga vidare på resonemang.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.