Stochastic Partial Budgeting: A New Look at an Old Tool

Farm business managers are constantly making adjustments in their businesses for smoother operations and profitability. Many times, these choices involve actions to enhance the financial return of the farm business; while other times these decisions are made out of necessity to minimize the effects of unfavorable conditions or events such as drought or changes in the market conditions. Some of these decisions are relatively simple, requiring making choices among alternatives within an enterprise; while others are complex involving a total overhaul of the business and its enterprises. Alternative choices within an individual enterprise can have a differential impact on farm profitability. Therefore, making the best decision may make the difference between profit or loss for that enterprise. Partial budgeting is very useful in making such changes within an enterprise of a farm.

Literacy Research: Issues of Authority, Ownership, and Representation

With the “social turn” of language in the past decade within English studies, ethnographic and teacher research methods increasingly have acquired legitimacy as a means of studying student literacy. And with this legitimacy, graduate students specializing in literacy and composition studies increasingly are being encouraged to use ethnographic and teacher research methods to study student literacy within classrooms. Yet few of the narratives produced from these studies discuss the problems that frequently arise when participant observers enter the classroom. Recently, some researchers have begun to interrogate the extent to which ethnographic and teacher research methods are able to construct and disseminate knowledge in empowering ways (Anderson & Irvine, 1993; Bishop, 1993; Fine, 1994; Fleischer. 1994; McLaren, 1992). While ethnographic and teacher research methods have oftentimes been touted as being more democratic and nonhierarchical than quantitative methods—-which oftentimes erase individuals lived experiences with numbers and statistical formulas—-researchers are just beginning to probe the ways that ethnographic and teacher research models can also be silencing, unreflective, and oppressive. Those who have begun to question the ethics of conducting, writing about, and disseminating knowledge in education have coined the term “critical” research, a rather vague and loose term that proposes a position of reflexivity and self-critique for all research methods, not just ethnography or teacher research. Drawing upon theories of feminist consciousness-raising, liberatory praxis, and community-action research, theories of critical research aim to involve researchers and participants in a highly participatory framework for constructing knowledge, an inquiry that seeks to question, disrupt, or intervene in the conditions under study for some socially transformative end. While critical research methods are always contingent upon the context being studied, in general they are undergirded by principles of non-hierarchical relations, participatory collaboration, problem-posing, dialogic inquiry, and multiple and multi-voiced interpretations. In distinguishing between critical and traditional ethnographic processes, for instance, Peter McLaren says that critical ethnography asks questions such as “[u]nder what conditions and to what ends do we. as educational researchers, enter into relations of cooperation. mutuality, and reciprocity with those who we research?” (p. 78) and “what social effects do you want your evaluations and understandings to have?” (p. 83). In»the same vein, Michelle Fine suggests that critical researchers must move beyond notions of the etic/emic dichotomy of researcher positionality in order to “probe how we are in relation with the contexts we study and with our informants, understanding that we are all multiple in those relations” (p. 72). Researchers in composition and literacy stud¬ies who endorse critical research methods, then, aim to enact some sort of positive transformative change in keeping with the needs and interests of the participants with whom they work.

Representation of the Weddell Sea deep water masses in the ocean component of the NCAR-CCSM model

We examine Weddell Sea deep water mass distributions with respect to the results from three different model runs using the oceanic component of the National Center for Atmospheric Research Community Climate System Model (NCAR-CCSM). One run is inter-annually forced by corrected NCAR/NCEP fluxes, while the other two are forced with the annual cycle obtained from the same climatology. One of the latter runs includes an interactive sea-ice model. Optimum Multiparameter analysis is applied to separate the deep water masses in the Greenwich Meridian section (into the Weddell Sea only) to measure the degree of realism obtained in the simulations. First, we describe the distribution of the simulated deep water masses using observed water type indices. Since the observed indices do not provide an acceptable representation of the Weddell Sea deep water masses as expected, they are specifically adjusted for each simulation. Differences among the water masses` representations in the three simulations are quantified through their root-mean-square differences. Results point out the need for better representation (and inclusion) of ice-related processes in order to improve the oceanic characteristics and variability of dense Southern Ocean water masses in the outputs of the NCAR-CCSM model, and probably in other ocean and climate models.

Exact field-driven interface dynamics in the two-dimensional stochastic Ising model with helicoidal boundary conditions

We investigate the interface dynamics of the two-dimensional stochastic Ising model in an external field under helicoidal boundary conditions. At sufficiently low temperatures and fields, the dynamics of the interface is described by an exactly solvable high-spin asymmetric quantum Hamiltonian that is the infinitesimal generator of the zero range process. Generally, the critical dynamics of the interface fluctuations is in the Kardar-Parisi-Zhang universality class of critical behavior. We remark that a whole family of RSOS interface models similar to the Ising interface model investigated here can be described by exactly solvable restricted high-spin quantum XXZ-type Hamiltonians. (C) 2012 Elsevier B.V. All rights reserved.

ACID-BASE REACTIONS: CONCEPT, REPRESENTATION AND GENERALIZATION FROM THE ENERGY INVOLVED IN TRANSFORMATIONS

ACID-BASE REACTIONS: CONCEPT, REPRESENTATION AND GENERALIZATION FROM THE ENERGY INVOLVED IN TRANSFORMATIONS. Undergraduate students on the first year of Chemistry Courses are unfamiliar with the representation of acid-base reactions using the ionic equation H+ + OH- -> H2O. A chemistry class was proposed about acid-base reactions using theory and experimental evaluation of neutralization heat to discuss the energy involved when water is formed from H+ and OH- ions. The experiment is suggested using different strong acids and strong base pairs. The presentation of the theme within a chemistry class for high school teachers increased the number of individuals that saw the acid-base reaction from this perspective.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Representation and analysis of control surface freeplay nonlinearity

Different representations for a control surface freeplay nonlinearity in a three degree of freedom aeroelastic system are assessed. These are the discontinuous, polynomial and hyperbolic tangent representations. The Duhamel formulation is used to model the aerodynamic loads. Assessment of the validity of these representations is performed through comparison with previous experimental observations. The results show that the instability and nonlinear response characteristics are accurately predicted when using the discontinuous and hyperbolic tangent representations. On the other hand, the polynomial representation fails to predict chaotic motions observed in the experiments. (c) 2012 Elsevier Ltd. All rights reserved.

A spatial stochastic model for rumor transmission

We consider an interacting particle system representing the spread of a rumor by agents on the d-dimensional integer lattice. Each agent may be in any of the three states belonging to the set {0,1,2}. Here 0 stands for ignorants, 1 for spreaders and 2 for stiflers. A spreader tells the rumor to any of its (nearest) ignorant neighbors at rate lambda. At rate alpha a spreader becomes a stifler due to the action of other (nearest neighbor) spreaders. Finally, spreaders and stiflers forget the rumor at rate one. We study sufficient conditions under which the rumor either becomes extinct or survives with positive probability.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

STOCHASTIC MODEL FOR SIMULATING MAIZE YIELD

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Central pain due to stroke: cognitive representation and coping according to gender

Objective: To identify and compare perceptions of pain and how it is faced between men and women with central post-stroke pain. Methods: The participants were 25 men and 25 women of minimum age 30 years-old and minimum schooling level of four years, presenting central post-stroke pain for at least three months. The instruments used were: Mini-Mental State Examination; structured interview for the Brief Psychiatric Scale; Survey of Sociodemographic and Clinical Data; Visual Analogue Scale (VAS); Ways of Coping with Problems Scale (WCPS) in Scale; Revised Illness Perception Questionnaire (IPQ-R); and Beck Depression Inventory (BD). Results: A significantly greater number of women used the coping strategy "Turn to spiritual and religious activities" in WCPS. They associated their emotional state with the cause of pain in IPQ-R. "Distraction of attention" was the strategy most used by the subjects. Conclusion: Women used spiritual and religious activities more as a coping strategy and perceived their emotional state as the cause of pain.

Stochastic gravito-inertial modes discovered by CoRoT in the hot Be star HD51452

Context. Be stars are rapidly rotating stars with a circumstellar decretion disk. They usually undergo pressure and/or gravity pulsation modes excited by the kappa-mechanism, i.e. an effect of the opacity of iron-peak elements in the envelope of the star. In the Milky Way, p-modes are observed in stars that are hotter than or equal to the B3 spectral type, while g-modes are observed at the B2 spectral type and cooler. Aims. We observed a B0IVe star, HD51452, with the high-precision, high-cadence photometric CoRoT satellite and high-resolution, ground-based HARPS and SOPHIE spectrographs to study its pulsations in great detail. We also used the lower resolution spectra available in the BeSS database. Methods. We analyzed the CoRoT and spectroscopic data with several methods: CLEAN-NG, FREQFIND, and a sliding window method. We also analyzed spectral quantities, such as the violet over red (V/R) emission variations, to obtain information about the variation in the circumstellar environment. We calculated a stellar structure model with the ESTER code to test the various interpretation of the results. Results. We detect 189 frequencies of variations in the CoRoT light curve in the range between 0 and 4.5 c d(-1). The main frequencies are also recovered in the spectroscopic data. In particular we find that HD51452 undergoes gravito-inertial modes that are not in the domain of those excited by the kappa-mechanism. We propose that these are stochastic modes excited in the convective zones and that at least some of them are a multiplet of r-modes (i.e. subinertial modes mainly driven by the Coriolis acceleration). Stochastically excited gravito-inertial modes had never been observed in any star, and theory predicted that their very low amplitudes would be undetectable even with CoRoT. We suggest that the amplitudes are enhanced in HD51452 because of the very rapid stellar rotation. In addition, we find that the amplitude variations of these modes are related to the occurrence of minor outbursts. Conclusions. Thanks to CoRoT data, we have detected a new kind of pulsations in HD51452, which are stochastically excited gravito-inertial modes, probably due to its very rapid rotation. These modes are probably also present in other rapidly rotating hot Be stars.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Robustness of the non-Markovian Alzheimer walk under stochastic perturbation

The elephant walk model originally proposed by Schutz and Trimper to investigate non-Markovian processes led to the investigation of a series of other random-walk models. Of these, the best known is the Alzheimer walk model, because it was the first model shown to have amnestically induced persistence-i.e. superdiffusion caused by loss of memory. Here we study the robustness of the Alzheimer walk by adding a memoryless stochastic perturbation. Surprisingly, the solution of the perturbed model can be formally reduced to the solutions of the unperturbed model. Specifically, we give an exact solution of the perturbed model by finding a surjective mapping to the unperturbed model. Copyright (C) EPLA, 2012