Adaptive neuro-fuzzy inference system: Estimation of soil aggregates stability

In present study, the capabilities of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions (PTFs) for estimating geometric mean diameter (GMD) and mean weight diameter (MWD), from routine soil properties and combination of routine soil properties and fractal dimension of aggregates were evaluated. For this reason 101 samples were collected form the Northwest of Iran and some their properties such as soil texture, pH, cation exchange capacity (CEC), and organic matter (OM), fractal dimension of aggregates between number-diameter (Dn), mass-diameter (Dmt), and bulk density-diameter (Dmy) were determined and used as an input variables for determining of mean weight diameter (MWD) and geometric mean diameter (GMD) by MLR and ANFIS PTFs. Results showed that the application of fractal dimension of aggregates as a predictor in two methods improved the accuracy of PTFs. As well as, results showed that ANFIS have greater potential for determination of the relationships between soil aggregate stability indices and other soil properties in compared with MLR. Therefore using of adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions is recommended.     

Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge

Modelling of anaerobic digestion systems is difficult because their performance is complex and varies significantly with influent characteristics and operational conditions. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for modelling of anaerobic digestion system of primary sludge of Kayseri municipal WasteWater Treatment Plant (WWTP). Effluent Volatile Solid (VS) and methane yield were predicted by the ANFIS. Two stage models were performed. In the first stage, effluent VS concentration was predicted using pH, VS concentration, flowrate of pre-thickened sludge and temperature of the influent as input parameters. In the second stage, effluent VS concentration in addition to first stage input parameters were used as input parameters to predict methane yield. The low Root Mean Square Error (RMSE) and high Index of agreement (IA) values were obtained with subtractive clustering method of a first order Sugeno type inference. The model performance was evaluated with statistical parameters. According to statistical evaluations, the models satisfactorily predict effluent VS concentration and methane yield.     

Calculation of light penetration depth in photobioreactors

Light penetration depth in high-densityChlorella cultures can be successfully estimated by Beer-Lambert's law. The efficiency of light energy absorption by algal cultures was so high that algal cells near the illuminating surface shade the cells deep in the culture. To exploit the potential of high-density algal cultures, this mutual shading should be eliminated or minimized. However, providing more light energy will not ease the situation and it will simply drop the overall light utilization efficiency.     

Tripton,transparency and light penetration in seven New York reservoirs

Temporal patterns and inter-system differences in the attenuation coefficient for scalar irradiance (K _s), Secchi disc transparency (SD), several measures of tripton, and chlorophyll a (Chl) are documented for the lacustrine zones of seven reservoirs (nine distinct basins) in New York (U.S.A.), based on a single year of comprehensive measurements. Analyses of these data and historic (12 years) observations of SD and Chl, including application of empirical and deterministic modeling frameworks, demonstrate that inorganic tripton is the primary attenuating constituent responsible for the substantial differences in K _s and SD among these basins, and the major temporal variations observed in these optical characteristics in most of the study basins. These inorganic particles, of terrigenous origins, are supplied directly to the water column of these basins in inflows, particularly during runoff events, and through the sediment resuspension process. Comparison of the measures of tripton indicates electron-microscopy-based measurements performed somewhat better than gravimetric analyses in explaining the variations in K _s and SD in the lower concentration systems. Increases in average SD values by factors of 2–5, compared to prevailing values, are predicted for the study basins with the deterministic model for the case of no tripton.     

Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of <Emphasis Type="Italic">Bacillus megaterium</Emphasis> cellular concentrations

Penicillin G acylase (PGA) is one of the most important enzymes for the pharmaceutical industry. Bacillus megaterium has the advantage of producing extra-cellular PGA. This work compares two neural networks (NNs) architectures for on-line inference of B. megaterium cell mass in an aerated stirred tank bioreactor, during the production of PGA. Nowadays, intelligent computing tools such as artificial NNs and fuzzy logic are commonly applied for state inference and modeling of bioreactors. Combining these two approaches in hybrid, neuro-fuzzy systems, may be advantageous. Our results indicate that a neuro-fuzzy inference system showed a better performance to infer cell concentrations, when compared to multilayer perceptrons networks.     

Parameter inference for an individual based model of chytridiomycosis in frogs

Individual based models (IBMs) and Agent based models (ABMs) have become widely used tools to understand complex biological systems. However, general methods of parameter inference for IBMs are not available. In this paper we show that it is possible to address this problem with a traditional likelihood-based approach, using an example of an IBM developed to describe the spread of chytridiomycosis in a population of frogs as a case study. We show that if the IBM satisfies certain criteria we can find the likelihood (or posterior) analytically, and use standard computational techniques, such as MCMC, for parameter inference.     

Adaptive design: estimation and inference with censored data in a semiparametric model

In this article, we provide a method of estimation for the treatment effect in the adaptive design for censored survival data with or without adjusting for risk factors other than the treatment indicator. Within the semiparametric Cox proportional hazards model, we propose a bias-adjusted parameter estimator for the treatment coefficient and its asymptotic confidence interval at the end of the trial. The method for obtaining an asymptotic confidence interval and point estimator is based on a general distribution property of the final test statistic from the weighted linear rank statistics at the interims with or without considering the nuisance covariates. The computation of the estimates is straightforward. Extensive simulation studies show that the asymptotic confidence intervals have reasonable nominal probability of coverage, and the proposed point estimators are nearly unbiased with practical sample sizes.     

Mineralization rates of peat from eroding peat islands in reservoirs

Reservoirs are sources of greenhouses gases to the atmosphere, primarily due to organic carbon mineralization in flooded plants and soils to carbon dioxide (CO₂) and methane (CH₄). Floating peat islands are common in reservoirs that inundated peatlands. These islands can decompose on mass, or small pieces of peat can erode from islands to decompose in the water column or on the bottom of reservoirs. Here we used large 450 liter sealed enclosures to measure mineralization rates of small peat pieces and larger peat blocks collected from floating peat islands. Mineralization rates were calculated by quantifying dissolved inorganic carbon (DIC), CO₂ and CH₄ accumulation within the water and headspace of the enclosures over time. We found that peat did decompose under water, but rates of mineralization of peat pieces were not different than rates of mineralization of larger peat blocks. Mineralization rates ranged between 59 and l40 g C g^–1 d^–1. Peat pieces acidified the water, shifting the bicarbonate equilibrium to almost exclusively dissolved CO₂, which was then readily able to flux to the atmosphere. We estimated that 2.4–5.6% of peat carbon was mineralized annually, suggesting that fluxes of CO₂ and CH₄ from reservoirs that flood peatlands could last at minimum 18–42 years from this carbon source alone.     

Angle distribution of foliage in individual Chamaecyparis obtusa canopies and effect of angle on diffuse light penetration

 The vertical distribution of foliage angle and area of three Chamaecyparis obtusa trees was determined by the triangle method, which calculates foliage geometry using measured coordinates of the leaf ”corners”, in a 43-year-old plantation in central Japan. Vertical distribution patterns of leaf area were different depending on tree size, but the boundary heights, which divide the canopy into sunlit and shaded parts, were similar in the three sample trees. The value of the average foliage angle [I(Z)] at a given depth (Z) from the tip of the stem decreased continually from the upper to lower layers within the canopy. The vertical patterns of changes in I(Z) were different among the three trees, but could be expressed by the following allometric equation as a function of depth.

Causal inference for non-mortality outcomes in the presence of death

Evaluation of the causal effect of a baseline exposure on a morbidity outcome at a fixed time point is often complicated when study participants die before morbidity outcomes are measured. In this setting, the causal effect is only well defined for the principal stratum of subjects who would live regardless of the exposure. Motivated by gerontologic researchers interested in understanding the causal effect of vision loss on emotional distress in a population with a high mortality rate, we investigate the effect among those who would live both with and without vision loss. Since this subpopulation is not readily identifiable from the data and vision loss is not randomized, we introduce a set of scientifically driven assumptions to identify the causal effect. Since these assumptions are not empirically verifiable, we embed our methodology within a sensitivity analysis framework. We apply our method using the first three rounds of survey data from the Salisbury Eye Evaluation, a population-based cohort study of older adults. We also present a simulation study that validates our method.     

Statistical confidence for likelihood-based paternity inference in natural populations

Paternity inference using highly polymorphic codominant markers is becoming common in the study of natural populations. However, multiple males are often found to be genetically compatible with each offspring tested, even when the probability of excluding an unrelated male is high. While various methods exist for evaluating the likelihood of paternity of each nonexcluded male, interpreting these likelihoods has hitherto been difficult, and no method takes account of the incomplete sampling and error-prone genetic data typical of large-scale studies of natural systems. We derive likelihood ratios for paternity inference with codominant markers taking account of typing error, and define a statistic Δ for resolving paternity. Using allele frequencies from the study population in question, a simulation program generates criteria for Δ that permit assignment of paternity to the most likely male with a known level of statistical confidence. The simulation takes account of the number of candidate males, the proportion of males that are sampled and gaps and errors in genetic data. We explore the potentially confounding effect of relatives and show that the method is robust to their presence under commonly encountered conditions. The method is demonstrated using genetic data from the intensively studied red deer ( Cervus elaphus ) population on the island of Rum, Scotland. The Windows-based computer program, CERVUS , described in this study is available from the authors. CERVUS can be used to calculate allele frequencies, run simulations and perform parentage analysis using data from all types of codominant markers.     

Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state

A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5–10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.     

Empirical evaluation of a prior for Bayesian phylogenetic inference

The Bayesian method of phylogenetic inference often produces high posterior probabilities (PPs) for trees or clades, even when the trees are clearly incorrect. The problem appears to be mainly due to large sizes of molecular datasets and to the large-sample properties of Bayesian model selection and its sensitivity to the prior when several of the models under comparison are nearly equally correct (or nearly equally wrong) and are of the same dimension. A previous suggestion to alleviate the problem is to let the internal branch lengths in the tree become increasingly small in the prior with the increase in the data size so that the bifurcating trees are increasingly star-like. In particular, if the internal branch lengths are assigned the exponential prior, the prior mean mu0 should approach zero faster than 1/square root n but more slowly than 1/n, where n is the sequence length. This paper examines the usefulness of this data size-dependent prior using a dataset of the mitochondrial protein-coding genes from the baleen whales, with the prior mean fixed at mu0=0.1n(-2/3). In this dataset, phylogeny reconstruction is sensitive to the assumed evolutionary model, species sampling and the type of data (DNA or protein sequences), but Bayesian inference using the default prior attaches high PPs for conflicting phylogenetic relationships. The data size-dependent prior alleviates the problem to some extent, giving weaker support for unstable relationships. This prior may be useful in reducing apparent conflicts in the results of Bayesian analysis or in making the method less sensitive to model violations.     

Efficacy of light therapy for perinatal depression: a review

ABSTRACT: Perinatal depression is an important public health problem affecting 10-20% of childbearing women. Perinatal depression is associated with significant morbidity, and has enormous consequences for the well-being of the mother and child. Treatment of depression during the perinatal period poses a complex problem for both mother and clinician, as antidepressant treatment strategies must consider the welfare of both mother and child during pregnancy and lactation. Bright light therapy may be an attractive treatment for perinatal depression because it is low cost, home-based, and has a much lower side effect profile than pharmacotherapy. The antidepressant effects of bright light are well established, and there are several rationales for expecting that bright light might also be efficacious for perinatal depression. This review describes these rationales, summarizes the available evidence on the efficacy of bright light therapy for perinatal depression, and discusses future directions for investigation of bright light therapy as a treatment for perinatal depression.