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.     

Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference

Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.     

Doubly robust estimation in missing data and causal inference models

The goal of this article is to construct doubly robust (DR) estimators in ignorable missing data and causal inference models. In a missing data model, an estimator is DR if it remains consistent when either (but not necessarily both) a model for the missingness mechanism or a model for the distribution of the complete data is correctly specified. Because with observational data one can never be sure that either a missingness model or a complete data model is correct, perhaps the best that can be hoped for is to find a DR estimator. DR estimators, in contrast to standard likelihood-based or (nonaugmented) inverse probability-weighted estimators, give the analyst two chances, instead of only one, to make a valid inference. In a causal inference model, an estimator is DR if it remains consistent when either a model for the treatment assignment mechanism or a model for the distribution of the counterfactual data is correctly specified. Because with observational data one can never be sure that a model for the treatment assignment mechanism or a model for the counterfactual data is correct, inference based on DR estimators should improve upon previous approaches. Indeed, we present the results of simulation studies which demonstrate that the finite sample performance of DR estimators is as impressive as theory would predict. The proposed method is applied to a cardiovascular clinical trial.     

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.     

Exact unconditional inference for risk ratio in a correlated 2 x 2 table with structural zero

In this article, we consider small-sample statistical inference for rate ratio (RR) in a correlated 2 x 2 table with a structural zero in one of the off-diagonal cells. Existing Wald's test statistic and logarithmic transformation test statistic will be adopted for this purpose. Hypothesis testing and confidence interval construction based on large-sample theory will be reviewed first. We then propose reliable small-sample exact unconditional procedures for hypothesis testing and confidence interval construction. We present empirical results to evince the better confidence interval performance of our proposed exact unconditional procedures over the traditional large-sample procedures in small-sample designs. Unlike the findings given in Lui (1998, Biometrics 54, 706-711), our empirical studies show that the existing asymptotic procedures may not attain a prespecified confidence level even in moderate sample-size designs (e.g., n = 50). Our exact unconditional procedures on the other hand do not suffer from this problem. Hence, the asymptotic procedures should be applied with caution. We propose two approximate unconditional confidence interval construction methods that outperform the existing asymptotic ones in terms of coverage probability and expected interval width. Also, we empirically demonstrate that the approximate unconditional tests are more powerful than their associated exact unconditional tests. A real data set from a two-step tuberculosis testing study is used to illustrate the methodologies.     

Photographic estimation of photosynthetically active radiation: evaluation of a computerized technique

Summary We describe a computerized technique for the analysis of hemispherical photographs of sites within plant canopies. The analysis yields estimates of canopy openness, a summary of the spatial distribution of canopy openings, estimates of direct and diffuse site factors, predicted times and durations of sunflecks for any date, and predicted daily courses of photosynthetically active photon flux density (PFD) for any date and for horizontal as well as inclined surfaces. By comparing estimates from photographs with measured values in 32 tropical forest microsites, we evaluate this technique and assess its potential and limitations. In sites of widely varying light availability, and with predictions specific to leaves at various angles, measured daily PFD correlated strongly (r=0.908) with predictions from photographs. Measured daily PFD was also closely correlated with estimates, from the photographs, of diffuse and direct site factors (r>0.8). Differences between predictions and measurements were proportionately largest in heavily shaded sites. Under clear conditions, this technique reliably predicted both daily PFD and the temporal pattern of PFD including predicting suflecks lasting more than a few minutes. However, temporal patterns of PFD predicted from photographs differ in potentially important details from measured patterns.C.I.W.D.P.B. Publication no. 935     

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.     

Ecological research for the restoration and management of rivers and reservoirs in Japan

In Japan the River Law was amended in 1997 to expand the traditional roles of flood control and water supply in river management to include environmental conservation. Two major multidisciplinary research groups were also founded to address the environmental issues arising from the management of rivers and watershed areas in Japan. One called the River Ecology Research Group was formed in 1995 to search for an ideal dynamic state of rivers to be managed. Six case studies commenced involving measurements of natural and human impacts on representative rivers and their biota selected from different regions of the country. Restoration of natural rivers has also been attempted. The other, called the Watershed Ecology Research Group, was formed in 1998 to study the natural environment surrounding dams. It consists of four groups concerned with forest ecology in the headwaters, raptor management research, reservoir ecology, and flow regime research. The topics include modeling of regeneration dynamics of riparian forests, GIS mapping of endangered raptor habitats, developing measures to reduce eutrophication of reservoir water, and the use of biodiversity of benthic faunas as an indicator of environmental change in the downstream. In both groups, ecologists collaborate with engineers who are responsible for the river infrastructure, to predict future impacts and keep ecological perspectives for the maintenance of the healthy environment of rivers and reservoirs.     

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.