Control químico de cercospora coffeicola (Berk y cooke) agente causal de la mancha de hierro en viveros de café (Coffea arabica L.)

La presente investigación fue realizada en la estación experimental del café de Masatepe; Nicaragua, durante los meses de Julio a Enero de los años 1975 a 1976. Consistió en la evaluación de doce Fungicidas para controlar Cercospera Coffeicole, agente causal de la “Mancha de Hierro “ en viveros de café. Como fuente de inòculo se utilizó plántulas infestadas por C. coffeicola; alrededor del ensayo. Para evaluar los daños ocasionados por este patógeno así como la fitotoxicidad de los productos sobre las plántulas, se utilizó el índice de infección propuesto por Grangier (24) y se elaboró una escala de fitotoxicidad, efectuándose un análisis de varianza en forma de parcelas divididas para determinar los efectos de los fungicidas y la fecha de mayor incidencia con los índices de o infección obtenidos. Basados en el índice de infección, Vigor de las plántulas y grado de fitotoxicidad los mejores fungicidas fueron: Difectatàn, MK-23, Kocide “101, Dithare M45 y cupravit ob-21 en dosis de 4, 4. 3.5.5 y 5 gramos de material comercial por litro de agua respectivamente. La mayor incidencia de C. Coffeicola se produjo durante el mes de Septiembre, elaborando su cuadro epidemiológico con el conjunto de datos climáticos

Monte Carlo-Based Bayesian group object tracking and causal reasoning

We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordinated groups in potentially cluttered environments. Our proposed Bayesian inference schemes, which are primarily based on (Markov chain) Monte Carlo sequential methods, include: 1) an evolving network-based multiple object tracking algorithm that is capable of categorizing objects into groups, 2) a multiple cluster tracking algorithm for dealing with prohibitively large number of objects, and 3) a causality inference framework for identifying dominant agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts. © 2013 Springer-Verlag Berlin Heidelberg.

Spectral properties of a double-quantum-dot structure: A causal Green's function approach

Spectral properties of a double quantum dot (QD) structure are studied by a causal Green's function (GF) approach. The double QD system is modeled by an Anderson-type Hamiltonian in which both the intra- and interdot Coulomb interactions are taken into account. The GF's are derived by an equation-of-motion method and the real-space renormalization-group technique. The numerical results show that the average occupation number of electrons in the QD exhibits staircase features and the local density of states depends appreciably on the electron occupation of the dot.

Motivated Action Theory: A Formal Theory of Causal Reasoning

When we reason about change over time, causation provides an implicit preference: we prefer sequences of situations in which one situation leads causally to the next, rather than sequences in which one situation follows another at random and without causal connections. In this paper, we explore the problem of temporal reasoning --- reasoning about change over time --- and the crucial role that causation plays in our intuitions. We examine previous approaches to temporal reasoning, and their shortcomings, in light of this analysis. We propose a new system for causal reasoning, motivated action theory, which builds upon causation as a crucial preference creterion. Motivated action theory solves the traditional problems of both forward and backward reasoning, and additionally provides a basis for a new theory of explanation.

Learning World Models in Environments with Manifest Causal Structure

This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" (deterministic finite state machines) or too "hard" (containing much hidden state). We describe a new domain --- environments with manifest causal structure --- for learning. In such environments the agent has an abundance of perceptions of its environment. Specifically, it perceives almost all the relevant information it needs to understand the environment. Many environments of interest have manifest causal structure and we show that an agent can learn the manifest aspects of these environments quickly using straightforward learning techniques. We present a new algorithm to learn a rule-based causal world model from observations in the environment. The learning algorithm includes (1) a low level rule-learning algorithm that converges on a good set of specific rules, (2) a concept learning algorithm that learns concepts by finding completely correlated perceptions, and (3) an algorithm that learns general rules. In addition this thesis examines the problem of finding a good expert from a sequence of experts. Each expert has an "error rate"; we wish to find an expert with a low error rate. However, each expert's error rate and the distribution of error rates are unknown. A new expert-finding algorithm is presented and an upper bound on the expected error rate of the expert is derived.

Combining Associational and Causal Reasoning to Solve Interpretation and Planning Problems

This report describes a paradigm for combining associational and causal reasoning to achieve efficient and robust problem-solving behavior. The Generate, Test and Debug (GTD) paradigm generates initial hypotheses using associational (heuristic) rules. The tester verifies hypotheses, supplying the debugger with causal explanations for bugs found if the test fails. The debugger uses domain-independent causal reasoning techniques to repair hypotheses, analyzing domain models and the causal explanations produced by the tester to determine how to replace faulty assumptions made by the generator. We analyze the strengths and weaknesses of associational and causal reasoning techniques, and present a theory of debugging plans and interpretations. The GTD paradigm has been implemented and tested in the domains of geologic interpretation, the blocks world, and Tower of Hanoi problems.

Causal and Teleological Reasoning in Circuit Recognition

This thesis presents a theory of human-like reasoning in the general domain of designed physical systems, and in particular, electronic circuits. One aspect of the theory, causal analysis, describes how the behavior of individual components can be combined to explain the behavior of composite systems. Another aspect of the theory, teleological analysis, describes how the notion that the system has a purpose can be used to aid this causal analysis. The theory is implemented as a computer program, which, given a circuit topology, can construct by qualitative causal analysis a mechanism graph describing the functional topology of the system. This functional topology is then parsed by a grammar for common circuit functions. Ambiguities are introduced into the analysis by the approximate qualitative nature of the analysis. For example, there are often several possible mechanisms which might describe the circuit's function. These are disambiguated by teleological analysis. The requirement that each component be assigned an appropriate purpose in the functional topology imposes a severe constraint which eliminates all the ambiguities. Since both analyses are based on heuristics, the chosen mechanism is a rationalization of how the circuit functions, and does not guarantee that the circuit actually does function. This type of coarse understanding of circuits is useful for analysis, design and troubleshooting.

Critical realism and causal analysis in international relations

Kurki, M. (2007). Critical realism and causal analysis in international relations. Millennium: Journal of International Studies, 35 (2), 361-378. RAE2008

Do debit cards increase household spending? Evidence from a semiparametric causal analysis of a survey

© Institute of Mathematical Statistics, 2014.Motivated by recent findings in the field of consumer science, this paper evaluates the causal effect of debit cards on household consumption using population-based data from the Italy Survey on Household Income and Wealth (SHIW). Within the Rubin Causal Model, we focus on the estimand of population average treatment effect for the treated (PATT). We consider three existing estimators, based on regression, mixed matching and regression, propensity score weighting, and propose a new doubly-robust estimator. Semiparametric specification based on power series for the potential outcomes and the propensity score is adopted. Cross-validation is used to select the order of the power series. We conduct a simulation study to compare the performance of the estimators. The key assumptions, overlap and unconfoundedness, are systematically assessed and validated in the application. Our empirical results suggest statistically significant positive effects of debit cards on the monthly household spending in Italy.

Variabilidad genética de poblaciones de Melampsora spp. agente causal de la roya del álamo, en la zona del Delta Inferior del río Paraná

En la zona del Delta del Paraná, Argentina, el cultivo de álamo constituye el principal recurso productivo y económico. La roya del álamo, producida por especies de Melampsora spp., es la enfermedad fúngica de mayor importancia económica por su carácter epidemiológico. El cultivo de un limitado número de clones, altamente especializados y ecológicamente inestables, provocan la evolución de distintos patosistemas generando el reemplazo de ciertos clones por otros más resistentes. El objetivo de este trabajo fue estudiar la variabilidad genética molecular de la especie Melampsora, sobre 32 aislamientos de ejemplares pertenecientes a clones de Populus spp. durante los años 2007, 2008 y 2009, utilizando las técnicas de AFLPs y SSRs. La utilización de AFLPs permitió diferenciar 32 fenotipos moleculares, mostrando variabilidad genética en la población, mientras que a partir de la utilización de SSRs se confirmó la presencia de M. medusae y M. larici-populina en la zona de estudio. El análisis de agrupamientos (UPGMA) a partir de AFLPs permitió distinguir la cepa de Populus nigra del resto quedando como unidad aislada, a su vez se pudieron identificaron tres clusters de aislamientos provenientes de Populus deltoides, con tendencia a agruparse por origen genético y origen geográfico. El análisis de AMOVA confirmó las diferencias significativas para dichos agrupamientos. Los resultados del presente estudio confirman la existencia de variabilidad genética para los marcadores

Presencia de Xanthomonas campestris pv. undulosa agente causal del rayado bacteriano en cultivos de trigo bajo riego

p.211-212

A versatile characterization of causal relationships

This paper introduces a framework for representing versatile temporal relationships between events and their effects. The framework is based on a simple time model which characterizes each time element as a subset of the set of real numbers and allows expression of both absolute time values and relative temporal relations. The formalism presented here formally specifies the so-called most general temporal constraint (GTC), which guarantees the common-sense assertion that “the beginning of the effect cannot precede the beginning of the cause”. It is shown that there are in fact 8 possible causal relationships which satisfy GTC, including cases where, on the one hand, effects start simultaneously with, during, immediately after, or some time after their causes, and on the other hand, events end before, simultaneously with, or after their causes. The causal relationships characterized in this paper are versatile enough to subsume those representatives in the literature.